Impact model: 4C

The model 4C (‘FORESEE’-Forest Ecosystems in a Changing Environment) has been developed to describe long-term forest behaviour under changing environmental conditions. It describes processes at the tree and stand level based on findings from eco-physiological experiments, long term observations and physiological modelling. The model includes descriptions of tree species composition, forest structure, total ecosystem carbon content as well as leaf area index. The model shares a number of features with gap models, which have often been used for the simulation of long-term forest development. Establishment, growth and mortality of tree cohorts are explicitly modelled on a patch on which horizontal homogeneity is assumed. Currently the model is parameterised for the five most abundant tree species of Central Europe (beech (Fagus sylvatica L.), Norway spruce (Picea abies L. Karst.), Scots pine (Pinus sylvestris L.), oaks ( Quercus robur L., and Quercus petraea Liebl.), and birch (Betula pendula Roth)) as well as other tree species, namely aspen (Populus tremula (L.), P. tremuloides (Michx.)), Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco), black locust (Robinia pseudoacacia L.), Aleppo pine (Pinus halepensis Mill.), Ponderosa pine (Pinus ponderosa Dougl.), and Lodgepole pine (Pinus contorta Dougl.).

Sector
Forests
Region
regional

Information for the model 4C 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
Martin Gutsch: gutsch@pik-potsdam.de, 0000-0001-7109-273X, Potsdam Institute for Climate Impact Research (Germany)
Petra Lasch: lasch@pik-potsdam.de, Potsdam Institute for Climate Impact Research (PIK) (Germany)
Mats Mahnken: mahnken@pik-potsdam.de, 0000-0002-9755-8814, Potsdam Institute for Climate Impact Research (Germany)
Christopher Reyer: reyer@pik-potsdam.de, 0000-0003-1067-1492, Potsdam Institute for Climate Impact Research (PIK) (Germany)
Additional persons involved: Petra Lasch, lasch@pik-potsdam.de
Output Data
Experiments: I, Ia, Ib, II, IIa, IIb, IIc, IId, IIe, III, IIIa, IIIb, IIIc, IIId, IIIe, V, VI, VII, VIIa (all rcp26 climate files lack extended future period) (Hyytiälä, Peitz, Solling beech, Solling spruce, Sorø, Kroof, Collelongo, Bily Kriz)
Climate Drivers: None
Date: 2021-08-26
Basic information
Model Version: 4C 2.1
Model Output License: CC0
Model Homepage: https://gitlab.pik-potsdam.de/foresee/4C
Model License: BSD 2-Clause License
Reference Paper: Main Reference: Lasch-Born P, Suckow F, Reyer C, Gutsch M, Kollas C, Badeck F, Bugmann H, Grote R, Fürstenau C, Lindner M, Schaber J et al. Description and evaluation of the process-based forest model 4C v2.2 at four European forest sites. Geoscientific Model Development,13,5311-5343,2020
Reference Paper: Other References:
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: IPSL-CM5A-LR, GFDL-ESM2M, MIROC5
Emissions Data Sets Used: CO2 concentration
Other Human Influences Data Sets Used: Nitrogen deposition
Climate Variables: hurs, sfcWind, tasmax, tas, tasmin, rsds, ps, pr
Spin-up
Was A Spin-Up Performed?: No
Management & Adaptation Measures
Management: thinning from below or from above according to targets of relative basal area targets, final harvest and planting
Extreme Events & Disturbances
Key Challenges: drought stress
Model set-up specifications
How did you initialize your model, e.g. using Individual tree dbh and height or stand basal area? How do you initialize soil conditions?: using individual tree dbh and height
Which data from PROFOUND DB did you use for initialisation (name of variable, which year)? From stand data or from individual tree data?: vegetation was initialized at the first year data was available with the variables species_id, dbh1_cm and height_m from the TREE table in the PROFOUND DB
How is management implemented? E.g. do you harvest biomass/basal area proportions or by tree numbers or dimensions (target dbh)?: harvest and final cutting is based on relative basal area targets
When is harvesting simulated by your model (start/middle/end of the year, i.e., before or after the growing season)?: at the end of the year
How do you regenerate? Do you plant seedlings one year after harvest or several years of gap and then plant larger saplings?: seedlings are planted the year after the final harvest
How are the unmanaged simulations designed? Is there some kind of regrowth/regeneration or are the existing trees just growing older and older?: existing trees are growing older and older
How are models implementing the noco2 scenario? Please confirm that co2 is follwing the historical trend (based on PROFUND DB) until 2000 (for ISIMIPFT) or 2005 (for ISIMIP2b) and then fixed at 2000 or 2005 value respectively?: noco2 scenarios follow the observed trend until 2000/2005 and stay constant after that
Does your model consider leap-years or a 365 calendar only? Or any other calendar?: the model does consider leap years
In hyytiälä and kroof, how did you simulate the "minor tree species"? e.g. in hyytiälä did you simulate only pine trees and removed the spruce trees or did you interpret spruce basal area as being pine basal area?: in Hyytiälä both species were simulated and in Kroof all four species were simulated
How did you simulate nitrogen deposition from 2005 onwards in the 2b picontrol run? Please confirm you kept them constant at 2005-levels?: nitrogen deposition was kept constant after 2005 in the 2b picontrol runs
What is the soil depth you assumed for each site and how many soil layers (including their depths) do you assume in each site? Please upload a list of the soil depth and soil layers your model assumes for each site as an attachment (Section 7).: soil depth is site specific from 118 cm to 200 cm with multiple horizons
Is there any stochastic element in your model (e.g. in the management or mortality submodel) that will lead to slightly different results if the model is re-run, even though all drivers etc. remain the same?: yes, the management module introduces a slight stochasticity in the model outputs
What is the minimum diameter at which a „tree is considered a tree“? and is there a similar threshold for the minimum harvestable diameter?: all trees are represented from the seedling stage. the minimum DBH is 0 cm. there is no minimum for harvestable diameter
Has your model been "historically calibrated" to any of the sites you simulated? e.g. has the site been used for model testing during model development?: yes, some of the sites were used for model testing but not for proper model/parameter calibration. see Lasch-Born et al. 2020, GMD
Key model processes
dynamic vegetation: yes: Forest dynamics are described by forest growth, regeneration/planting, management.
nitrogen limitation: yes: Different approaches are used to estimate the nitrogen depending reduction of NPP. Nitrogen supply is calculated by a soil model (water, temperature, C/N module) from the soil conditions, the litter input and nitrogen uptake and leaching.
CO2 effects: yes: The annual course of net photosynthesis is simulated with a mechanistic formulation of net photosynthesis as a function of environmental influences (temperature, water and nitrogen availability, radiation, and CO2) where the physiological capacity (maximal carboxylation rate) is calculated based on optimisation theory (modified after Haxeltine and Prentice, 1996) plus calculation of total tree respiration following the concept of constant annual respiration fraction as proposed by Landsberg and Waring (1997).
light interception: yes: The share of any tree cohort in the total stand’s net photosynthetic assimilation of carbon is proportional to its share of the absorbed photosynthetically active radiation. The total fraction of photosynthetically active radiation absorbed by each tree cohort is calculated each time stand phenology changes, based on the Lambert-Beer law. Four models exist to calculate light transmission and absorption through the canopy.
light utilization: Light use efficiency is calculated according to Haxeltine and Prentice (1996).
phenology: yes: The phenological approach in 4C is based on the interaction of inhibitory and promotory agents that are assumed to control the developmental status of a plant. The agents are driven by temperature and photoperiod, which play the most prominent role in phenology. Using these simple but basic principles a model for the abundance or concentration of an inhibitory and a promotory compound made of a system of two difference equations is used (Schaber and Badeck, 2003).
water stress: yes: After calculating water demand by forest stand and water supply from the soil for each tree cohort photosynthesis is being reduced if demand is greater than supply. Allocation is also affected.
Evapo-transpiration approach: yes: Different approaches are used, these are Turc/Ivanov, Priestley/Taylor, Penman/ Monteith.
Differences in rooting depth: yes: The model uses a fixed site-specific rooting depth as input parameter depending on soil characteristics.
Root distribution over depth: yes: 4C uses an approach according to Jackson (1996), which assumes an exponential decrease of fine root biomass with soil depth. Additionally, a site and species specific root distribution can be used as input.
closed energy balance: It is not considered.
Coupling/feedback between soil moisture and surface temperature: yes: 4C includes a coupled soil moisture and temperature model.
latent heat: Latent heat is not calculated.
sensible heat: Sensible heat is not calculated.
Assimilation: follows from light use efficiency
Respiration: constant fraction of GPP
Carbon allocation: species specific dynamic allocation based on pipe model theory and functional balance
Regeneration/planting: new individuals are prescribed by age, height and species
Soil water balance: dynamic multi-layer bucket model
Carbon/Nitrogen balance: carbon and nitrogen balance is considered
Are feedbacks considered that reflect the influence of changing carbon state variables on the other system components and driving data (i.e. Growth (leaf area), light, temperature, water availability, nutrient availability)?: yes, e.g. C/N ratio in the soil
Causes of mortality in vegetation models
Age/Senescence: yes: The so called ‘age related’ mortality basing on life span corresponds to the intrinsic mortality developed by (Botkin, 1993).
Fire: not simulated
Drought: yes: The response of trees to growth suppression by drought is described by a carbon-based stress mortality .
Insects: yes: Modelling of impacts of mistletoe, phloem feeder, defoliator, stem disturber, xylem disturber, root disturber is under construction.
Storm: not simulated
Stochastic random disturbance: not simulated
Other: Self-thinning mortality due to light availability is implemented (stress mortality).
NBP components
Harvest: The model includes harvests and all fluxes caused by thinning and harvesting (litter fall, harvest pool). Harvested biomass is pooled into different carbon pools and later on released to the atmosphere e.g. by decomposition.
Other processes: Dead biomass which is not harvested is an input to the litter pool, where it is decomposed.
Species / Plant Functional Types (PFTs)
List of species / PFTs: Fagus sylvatica (fasy) Picea abies (piab) Pinus sylvestris (pisy) Quercus robur (quro) Betula pendula (bepe) Pinus contorta (pico) Pinus ponderosa (pipo) Populus tremula (potr) Pinus halepensis (piha) Pseudotsuga menziesii (psme) Robinia pseudoacacia (rops) Eucalyptus globulus (eugl) Eucalyptus grandis (eugr)
Comments: 4C uses tree species and parameters for 13 tree species.
Model output specifications
Do you provide the initial state in your simulation outputs (i.e., at year 0; before the simulation starts)?: yes
Output format: per forest stand/per simulated forest unit
When you report a variable as "xxx-total" does it equal the (sum of) "xxx-species" value(s)? or are there confounding factors such as ground/herbaceous vegetation contributing to the "total" in your model?: no ground vegetation was simulated
Did you report any output per dbh-class? if yes, which variables?: yes
Additional Forest Information
Forest sites simulated: Soroe, Hyytiälä, KROOF, Solling beech, Solling spruce, Peitz, Colllelongo, Bily Kriz
Attachments
Person responsible for model simulations in this simulation round
Martin Gutsch: gutsch@pik-potsdam.de, 0000-0001-7109-273X, Potsdam Institute for Climate Impact Research (Germany)
Petra Lasch: lasch@pik-potsdam.de, Potsdam Institute for Climate Impact Research (PIK) (Germany)
Mats Mahnken: mahnken@pik-potsdam.de, 0000-0002-9755-8814, Potsdam Institute for Climate Impact Research (Germany)
Christopher Reyer: reyer@pik-potsdam.de, 0000-0003-1067-1492, Potsdam Institute for Climate Impact Research (PIK) (Germany)
Additional persons involved: Petra Lasch, lasch@pik-potsdam.de
Basic information
Model Version: 4C 2.1
Model Output License: CC0
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
Input data sets used
Observed Atmospheric Climate Data Sets Used: GSWP3, Historical observed climate data, PGMFD v2.1 (Princeton), WATCH (WFD), WATCH-WFDEI
Climate Variables: tasmax, tas, tasmin, rhs, rsds, ps, pr
Additional Input Data Sets: N deposition (EMEP)
Spin-up
Was A Spin-Up Performed?: No
Management & Adaptation Measures
Management: thinning from below or from above according to targets of stem biomass or stem number or relative targets of stem biomass planting, natural regeneration short-rotation coppice
Extreme Events & Disturbances
Key Challenges: drought stress
Model set-up specifications
How did you initialize your model, e.g. using Individual tree dbh and height or stand basal area? How do you initialize soil conditions?: using individual tree dbh and height
Which data from PROFOUND DB did you use for initialisation (name of variable, which year)? From stand data or from individual tree data?: vegetation was initialized at the first year data was available with the variables species_id, dbh1_cm and height_m from the TREE table in the PROFOUND DB
How is management implemented? E.g. do you harvest biomass/basal area proportions or by tree numbers or dimensions (target dbh)?: the tree number was reduced to the observed tree number in the PROFOUND DB if simulated tree number was higher by a thinning from above or from below as indicated in the simulation protocol
When is harvesting simulated by your model (start/middle/end of the year, i.e., before or after the growing season)?: at the end of the year
How do you regenerate? Do you plant seedlings one year after harvest or several years of gap and then plant larger saplings?: seedlings are planted the year after the final harvest
How are the unmanaged simulations designed? Is there some kind of regrowth/regeneration or are the existing trees just growing older and older?: existing trees are growing older and older except for the case when there is a final harvest, in which case seedlings are planted
How are models implementing the noco2 scenario? Please confirm that co2 is follwing the historical trend (based on PROFUND DB) until 2000 (for ISIMIPFT) or 2005 (for ISIMIP2b) and then fixed at 2000 or 2005 value respectively?: noco2 scenarios follow the observed trend until 2000/2005 and stay constant after that
Does your model consider leap-years or a 365 calendar only? Or any other calendar?: the model does consider leap years
In hyytiälä and kroof, how did you simulate the "minor tree species"? e.g. in hyytiälä did you simulate only pine trees and removed the spruce trees or did you interpret spruce basal area as being pine basal area?: on hyytiälä both species were simulated in in kroof 4 species were simulated
How did you simulate nitrogen deposition from 2005 onwards in the 2b picontrol run? Please confirm you kept them constant at 2005-levels?: nitrogen deposition was kept constant after 2005 in the 2b picontrol runs
What is the soil depth you assumed for each site and how many soil layers (including their depths) do you assume in each site? Please upload a list of the soil depth and soil layers your model assumes for each site as an attachment (Section 7).: soil depth is site specific from 118 cm to 200 cm with multiple horizons
Is there any stochastic element in your model (e.g. in the management or mortality submodel) that will lead to slightly different results if the model is re-run, even though all drivers etc. remain the same?: yes, the management module introduces a slight stochasticity in the model outputs
What is the minimum diameter at which a „tree is considered a tree“? and is there a similar threshold for the minimum harvestable diameter?: all trees are represented from the seedling stage. the minimum dbh is 0 cm. harvest diameter follows the specified simulation protocol
Has your model been "historically calibrated" to any of the sites you simulated? e.g. has the site been used for model testing during model development?: -
Please upload a list of your parameters as an attachment (Section 7). The list should include species-specific parameters and other parameters not depending on initialization data including the following information: short name, long name, short explanation, unit, value, see here for an example (http://www.pik-potsdam.de/4c/web_4c/theory/parameter_table_0514.pdf): -
Key model processes
dynamic vegetation: yes: Forest dynamics are described by forest growth, regeneration/planting, management.
nitrogen limitation: yes Different approaches are used to estimate the nitrogen depending reduction of NPP. Nitrogen supply is calculated by a soil model (water, temperature, C/N module) from the soil conditions, the litter input and nitrogen uptake and leaching.
CO2 effects: The annual course of net photosynthesis is simulated with a mechanistic formulation of net photosynthesis as a function of environmental influences (temperature, water and nitrogen availability, radiation, and CO2) where the physiological capacity (maximal carboxylation rate) is calculated based on optimisation theory (modified after Haxeltine and Prentice, 1996) plus calculation of total tree respiration following the concept of constant annual respiration fraction as proposed by Landsberg and Waring (1997).
light interception: yes: The share of any tree cohort in the total stand’s net photosynthetic assimilation of carbon is proportional to its share of the absorbed photosynthetically active radiation. The total fraction of photosynthetically active radiation absorbed by each tree cohort is calculated each time stand phenology changes, based on the Lambert-Beer law. Four models exist to calculate light transmission and absorption through the canopy.
light utilization: Light use efficiency is calculated according to Haxeltine and Prentice (1996).
phenology: yes: The phenological approach in 4C is based on the interaction of inhibitory and promotory agents that are assumed to control the developmental status of a plant. The agents are driven by temperature and photoperiod, which play the most prominent role in phenology. Using these simple but basic principles a model for the abundance or concentration of an inhibitory and a promotory compound made of a system of two difference equations is used (Schaber and Badeck, 2003).
water stress: yes: After calculating water demand by forest stand and water supply from the soil for each tree cohort photosynthesis is being reduced if demand is greater than supply. Allocation is also affected.
Evapo-transpiration approach: yes: Different approaches are used: Turc/Ivanov, Priestley/Taylor, Penman/ Monteith.
Differences in rooting depth: yes: The model uses a fixed site-specific rooting depth as input parameter depending on soil characteristics.
Root distribution over depth: yes 4C uses an approach according to Jackson (1996), which assumes an exponential decrease of fine root biomass with soil depth. Additionally, a site and species specific root distribution can be used as input.
closed energy balance: It is not considered.
Coupling/feedback between soil moisture and surface temperature: yes: 4C includes a coupled soil moisture and temperature model.
latent heat: Latent heat is not calculated.
sensible heat: Sensible heat is not calculated.
Assimilation: follows from light use efficiency
Respiration: constant fraction of GPP
Carbon allocation: species specific dynamic allocation based on pipe model theory and functional balance
Regeneration/planting: new individuals are prescribed by age, height and species
Soil water balance: dynamic multi-layer bucket model
Carbon/Nitrogen balance: carbon and nitrogen balance is considered
Are feedbacks considered that reflect the influence of changing carbon state variables on the other system components and driving data (i.e. Growth (leaf area), light, temperature, water availability, nutrient availability)?: yes, e.g. C/N ratio in the soil
Causes of mortality in vegetation models
Age/Senescence: yes: The so called ‘age related’ mortality basing on life span corresponds to the intrinsic mortality developed by (Botkin, 1993).
Fire: not simulated
Drought: yes: The response of trees to growth suppression by drought is described by a carbon-based stress mortality .
Insects: yes: Modelling of impacts of mistletoe, phloem feeder, defoliator, stem disturber, xylem disturber, root disturber is under construction. not simulated here
Storm: not simulated
Stochastic random disturbance: not simulated
Other: Self-thinning mortality due to light availability is implemented (stress mortality).
NBP components
Harvest: The model includes harvests and all fluxes caused by thinning and harvesting (litter fall, harvest pool). Harvested biomass is pooled into different carbon pools and later on released to the atmosphere e.g. by decomposition.
Other processes: Dead biomass which is not harvested is an input to the litter pool, where it is decomposed.
Species / Plant Functional Types (PFTs)
List of species / PFTs: Fagus sylvatica (fasy) Picea abies (piab) Pinus sylvestris (pisy) Quercus robur (quro) Betula pendula (bepe) Pinus contorta (pico) Pinus ponderosa (pipo) Populus tremula (potr) Pinus halepensis (piha) Pseudotsuga menziesii (psme) Robinia pseudoacacia (rops) Eucalyptus globulus (eugl) Eucalyptus grandis (eugr)
Comments: 4C uses tree species and parameters for 13 tree species.
Model output specifications
Do you provide the initial state in your simulation outputs (i.e., at year 0; before the simulation starts)?: yes
Output format: per forest stand/ per simulated forest unit
When you report a variable as "xxx-total" does it equal the (sum of) "xxx-species" value(s)? or are there confounding factors such as ground/herbaceous vegetation contributing to the "total" in your model?: no ground vegetation was simulated
Did you report any output per dbh-class? if yes, which variables?: yes
Additional Forest Information
Forest sites simulated: Soroe, Hyytiälä, KROOF, Solling beech, Solling spruce, Peitz, Colllelongo, Bily Kriz