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Impact model: 3PGN-BW

Sector
Forests
Region
local

3PGN-BW is an enhanced version of the stand-scale process-based model 3PGN (Xenakis et al. 2008). 3PGN couples the forest growth model 3PG (Landsberg and Waring 1997) with the soil model ICBM/2N (Kätterer and Andrén 2001). The model computes the carbon assimilation based on the absorbed radiation and canopy quantum efficiency. The NPP is subsequently allocated to different tree compartments, defining the growth patterns of the stand.

Information for the model 3PGN-BW 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
Andrey Lessa Derci Augustynczik: augustynczik@iiasa.ac.at, 0000-0001-5513-5496, IIASA (Austria)
Rasoul Yousefpour: rasoul.yousefpour@ife.uni-freiburg.de, 0000-0003-3604-8279, University of Freiburg (Germany)
Output Data
Experiments: I, Ia, Ib, II, IIa, IIb, IIc, IId, IIe, III, IIIa, IIIb, IIIc, IIId, IIIe, IV, V, VI, VIa, VII, VIIa, VIII, VIIIa, VIIIb, VIIIc, VIIId, VIIIe, VIIIf, VIIIg (Peitz, Solling beech, Sorø, Collelongo)
Climate Drivers: None
Date: 2021-08-02
Basic information
Model Output License: CC BY 4.0
Simulation Round Specific Description: * Data in embargo period, not yet publicly available
Reference Paper: Main Reference: Landsberg, J.J.; Waring, R.H. et al. A generalised model of forest productivity using simplified concepts of radiation-use efficiency, carbon balance and partitioning. Forest Ecology and Management,95,209-228,1997
Reference Paper: Other References:
Resolution
Spatial aggregation: forest stand
Additional spatial aggregation & resolution information: stand scale
Temporal resolution of input data: climate variables: monthly
Temporal resolution of input data: co2: annual
Input data
Simulated atmospheric climate data sets used: IPSL-CM5A-LR, HadGEM2-ES, GFDL-ESM2M, MIROC5
Observed atmospheric climate data sets used: EWEMBI
Emissions data sets used: Atmospheric CO2 concentration
Other human influences data sets used: Nitrogen deposition (ISIMIP2)
Climate variables: tasmax, tasmin, rsds, pr
Spin-up
Was a spin-up performed?: No
Management & Adaptation Measures
Management: Thinning intensity prescribed by the number of trees harvested or basal area removal
Extreme Events & Disturbances
Key challenges: Not included
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?: Individual tree data and generalized biomass equations were used to compute the initial biomass of foliage, stem and roots
Which data from profound db did you use for initialisation (name of variable, which year)? from stand data or from individual tree data?: The dbh tree data for each stand was used to calculate the initial biomass of the stands (dbh1_cm and height1_m). Furthermore, the soil data (sand_percent, silt_percent, clay_percent, wiltpv, fcapv_percent, c_percent, n_percent) and climate data from the PROFOUND database were also used.
How is management implemented? e.g. do you harvest biomass/basal area proportions or by tree numbers or dimensions (target dbh)?: Management prescriptions are based on basal area or tree number removal
When is harvesting simulated by your model (start/middle/end of the year, i.e., before or after the growing season)?: 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 one year after harvesting
How are the unmanaged simulations designed? is there some kind of regrowth/regeneration or are the existing trees just growing older and older?: There is no regrowth/regeneration
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?: CO2 follows the historical trend until 2005
Does your model consider leap-years or a 365 calendar only? or any other calendar?: 365 calendar
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?: Not simulated
How did you simulate nitrogen deposition from 2005 onwards in the 2b picontrol run? please confirm you kept them constant at 2005-levels?: Historical N deposition simulated until 2005 and constant afterwards
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).: 1 soil layer. Soil depth is only used to calculated min and max available soil water, C and N content
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?: There is no stochasticity
What is the minimum diameter at which a „tree is considered a tree“? and is there a similar threshold for the minimum harvestable diameter?: There are no thresholds
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?: Not for the parameter set used in the simulations
Key model processes
Dynamic vegetation: yes.
Nitrogen limitation: yes. A fertility rating parameter reduces the potential GPP, according to the available soil nitrogen and the demand of this nutrient for foliage production.
Co2 effects: yes. A modifier alters the GPP accroding to the atmospheric CO2 concentration.
Light interception: yes. Light interception is computed based on the Beer-Lambert Law.
Light utilization: yes. RUE-approach.
Phenology: yes. For deciduous species a month of leaf fall and leaf production are specified.
Water stress: yes. A soil water modifier reduces the GPP, according to the ASW.
Heat stress: no
Evapo-transpiration approach: yes. Penman-Monteith equation.
Differences in rooting depth: no
Root distribution over depth: no
Closed energy balance: no
Coupling/feedback between soil moisture and surface temperature: no
Latent heat: no
Sensible heat: no
Assimilation: yes. Carbon assimilation is based on the . It depends on the PAR and various environmental multiplicative modifiers (ASW, temperature, nutrient supply, CO2 concentration). GPP is based on a LUE approach.
Respiration: yes. Heterotrophic and autotrophic respiration (growth and maintenance) are simulated. Maintenance respiration depends on temperature. Soil respiration depends on the tenmperature and water availability.
Carbon allocation: yes. Carbon is initially allocated to roots, depending on the soil fertility and a physiological modifier (depends on ASW, VPD and age). Subsequently, carbon is allocated to stem and roots, depending on foliage:stem partitioning ratios.
Regeneration/planting: yes. Planting is performed after final harvesting and the stand is initialized with the biomass stocks of saplings. No regeneration is implemented.
Soil water balance: yes. ASW is updated according to rainfall, irrigation, ET, runoff and canopy interception. Temperature and precipitation affect the soil water balance.
Carbon/nitrogen balance: yes. Carbon in the vegetation is simulated according to the LUE approach, and further allocated to different tree compartments. Carbon losses are given by respiration and mortality. For foliage and root litter, turnover rates are applied. Litterfall and dead trees act as C and N input to the soil. Furthermore, N deposition is added to the available N to plants. The C and N output in the soil are given by decomposition rates of different compartments (labile, refractory and old carbon pools).
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. Changes in leaf biomass alter the stand's LAI and light interception, as well as nutrient availability. Biomass losses via mortality affect soil nutrient availability.
Causes of mortality in vegetation models
Age/senescence: Senescence mortality is based on the mortality rate for young and for old stands. The current mortality rate is a function of the stand age, contained in the range of the mortality rates for young and old stands.
Fire: no
Drought: no
Insects: no
Storm: no
Stochastic random disturbance: no
Other: Self-thinning mortality is simulated based on Reineke's rule. It depends on the average stem mass and the number of stems in the stand.
NBP components
Fire: no
Land-use change: no
Harvest: yes. Harvest from forest management
Species / Plant Functional Types (PFTs)
List of species / pfts: [Fagus sylvatica] ([fasy]); [Pinus sylvestris] ([pisy])
Model output specifications
Do you provide the initial state in your simulation outputs (i.e., at year 0; before the simulation starts)?: No
Output format: Per hectare
Output per pft?: no
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?: Only pure stands are simulated
Did you report any output per dbh-class? if yes, which variables?: No
Additional Forest Information
Forest sites simulated: Collelongo, Solling, Soro, Peitz
Attachments
556-3PGN_parameters.pdf (307.9 KB)
Person responsible for model simulations in this simulation round
Andrey Lessa Derci Augustynczik: augustynczik@iiasa.ac.at, 0000-0001-5513-5496, IIASA (Austria)
Rasoul Yousefpour: rasoul.yousefpour@ife.uni-freiburg.de, 0000-0003-3604-8279, University of Freiburg (Germany)
Basic information
Model Output License: CC0
Reference Paper: Main Reference: Landsberg, J.J.; Waring, R.H. et al. A generalised model of forest productivity using simplified concepts of radiation-use efficiency, carbon balance and partitioning. Forest Ecology and Management,95,209-228,1997
Reference Paper: Other References:
Resolution
Spatial aggregation: forest stand
Additional spatial aggregation & resolution information: stand scale
Temporal resolution of input data: climate variables: monthly
Temporal resolution of input data: co2: annual
Input data
Observed atmospheric climate data sets used: GSWP3, PGMFD v2.1 (Princeton), WATCH (WFD), WATCH-WFDEI
Emissions data sets used: Atmospheric CO2 concentration
Other human influences data sets used: Nitrogen deposition (ISIMIP2)
Climate variables: tasmax, tasmin, rsds, pr
Spin-up
Was a spin-up performed?: No
Management & Adaptation Measures
Management: Thinning intensity prescribed by the number of trees harvested or basal area removal
Extreme Events & Disturbances
Key challenges: Not included
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?: Individual tree data and generalized biomass equations were used to compute the initial biomass of foliage, stem and roots
Which data from profound db did you use for initialisation (name of variable, which year)? from stand data or from individual tree data?: The dbh tree data for each stand was used to calculate the initial biomass of the stands (dbh1_cm and height1_m). Furthermore, the soil data (sand_percent, silt_percent, clay_percent, wiltpv, fcapv_percent, c_percent, n_percent) and climate data from the PROFOUND database were also used.
How is management implemented? e.g. do you harvest biomass/basal area proportions or by tree numbers or dimensions (target dbh)?: Management prescriptions are based on basal area or tree number removal
When is harvesting simulated by your model (start/middle/end of the year, i.e., before or after the growing season)?: 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 one year after harvesting
How are the unmanaged simulations designed? is there some kind of regrowth/regeneration or are the existing trees just growing older and older?: There is no regrowth/regeneration
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?: CO2 follows the historical trend until 2000
Does your model consider leap-years or a 365 calendar only? or any other calendar?: 365 calendar
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?: Not simulated
How did you simulate nitrogen deposition from 2005 onwards in the 2b picontrol run? please confirm you kept them constant at 2005-levels?: Not simulated
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).: 1 soil layer. Soil depth is only used to calculated min and max available soil water, C and N content
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?: There is no stochasticity
What is the minimum diameter at which a „tree is considered a tree“? and is there a similar threshold for the minimum harvestable diameter?: There are no thresholds
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?: Not for the parameter set used in the simulations
Key model processes
Dynamic vegetation: yes.
Nitrogen limitation: yes. A fertility rating parameter reduces the potential GPP, according to the available soil nitrogen and the demand of this nutrient for foliage production.
Co2 effects: yes. A modifier alters the GPP accroding to the atmospheric CO2 concentration.
Light interception: yes. Light interception is computed based on the Beer-Lambert Law.
Light utilization: yes. RUE-approach.
Phenology: yes. For deciduous species a month of leaf fall and leaf production are specified.
Water stress: yes. A soil water modifier reduces the GPP, according to the ASW.
Heat stress: no
Evapo-transpiration approach: yes. Penman-Monteith equation.
Differences in rooting depth: no
Root distribution over depth: no
Closed energy balance: no
Coupling/feedback between soil moisture and surface temperature: no
Latent heat: no
Sensible heat: no
Assimilation: yes. Carbon assimilation is based on the . It depends on the PAR and various environmental multiplicative modifiers (ASW, temperature, nutrient supply, CO2 concentration). GPP is based on a LUE approach.
Respiration: yes. Heterotrophic and autotrophic respiration (growth and maintenance) are simulated. Maintenance respiration depends on temperature. Soil respiration depends on the tenmperature and water availability.
Carbon allocation: yes. Carbon is initially allocated to roots, depending on the soil fertility and a physiological modifier (depends on ASW, VPD and age). Subsequently, carbon is allocated to stem and roots, depending on foliage:stem partitioning ratios.
Regeneration/planting: yes. Planting is performed after final harvesting and the stand is initialized with the biomass stocks of saplings. No regeneration is implemented.
Soil water balance: yes. ASW is updated according to rainfall, irrigation, ET, runoff and canopy interception. Temperature and precipitation affect the soil water balance.
Carbon/nitrogen balance: yes. Carbon in the vegetation is simulated according to the LUE approach, and further allocated to different tree compartments. Carbon losses are given by respiration and mortality. For foliage and root litter, turnover rates are applied. Litterfall and dead trees act as C and N input to the soil. Furthermore, N deposition is added to the available N to plants. The C and N output in the soil are given by decomposition rates of different compartments (labile, refractory and old carbon pools).
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. Changes in leaf biomass alter the stand's LAI and light interception, as well as nutrient availability. Biomass losses via mortality affect soil nutrient availability.
Causes of mortality in vegetation models
Age/senescence: Senescence mortality is based on the mortality rate for young and for old stands. The current mortality rate is a function of the stand age, contained in the range of the mortality rates for young and old stands.
Fire: no
Drought: no
Insects: no
Storm: no
Stochastic random disturbance: no
Other: Self-thinning mortality is simulated based on Reineke's rule. It depends on the average stem mass and the number of stems in the stand.
NBP components
Fire: no
Land-use change: no
Harvest: yes.
Other processes: yes. Harvest from forest management
Species / Plant Functional Types (PFTs)
List of species / pfts: [Fagus sylvatica] ([fasy]); [Pinus sylvestris] ([pisy])
Model output specifications
Do you provide the initial state in your simulation outputs (i.e., at year 0; before the simulation starts)?: No
Output format: Per hectare
Output per pft?: no
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?: Only pure stands are simulated
Did you report any output per dbh-class? if yes, which variables?: No
Additional Forest Information
Forest sites simulated: Collelongo, Solling, Soro, Peitz
Attachments
555-3PGN_parameters.pdf (307.9 KB)




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