CARBON CYCLE IN MANAGED FORESTS – APPLYING A PROCESS MODEL TO
STANDS OF MAJOR TREE SPECIES IN CENTRAL-EUROPEAN FORESTRY
EMIL CIENCIALA, FYODOR TATARINOV, MARTIN ČERNÝ
Institute of Forest Ecosystem Research, 1544, CZ–254 01 Jílové u Prahy, Czech Republic,
[email protected]
CIENCIALA, E., TATARINOV, F., ČERNÝ, M.: Carbon cycle in managed forests – applying a process model
to stands of major tree species in central-european forestry. Lesn. Čas. – Forestry Journal, 52(1–2): 29–39,
2006, 4 fig., tab. 2, ref. 23. Original paper. ISSN 0323–1046
This study describes the adaptation and application of a process model BIOME-BGC to simulate carbon
cycle of forest stands under typical conditions of Central-European forestry. BIOME-BGC is a point
model developed to study energy, water, carbon and nitrogen cycling in major natural biomes. BIOMEBGC attracts researchers by its reasonable model simplification of ecosystems, while retaining the
spectrum of key physiological processes and feedbacks important for advanced and effective analysis of
ecosystem behaviour and responses to imposed factors. To be applicable also for managed forest
ecosystems, several new routines were added to the model code. They include thinning and felling routine
that can be specific as for time and intensity, and species change after clear-cut. This contribution
describes 1) sensitivity analysis of the key model parameters and 2) model application to typical managed
stands of spruce, pine, beech and oak, utilizing the long-term measurements of aboveground biomass on
permanent research plots. These steps are vital to address the next/final goal of the model adaptation,
namely large-scale grid application permitting regional analysis of managed forest ecosystems under
defined scenarios of management and changing environmental conditions.
Keywords: carbon budget, prediction, forest ecosystem, biomass, forest management, BIOME-BGC
1. Introduction
Carbon budget and sink capacity of forest ecosystems are important for mitigating
environmental change. Policies on sustainable forest management and climate change
stimulate research agendas on forest carbon budget and its interpretation. The recent adoption
of Kyoto Protocol further spurred the need of a sound understanding carbon-related processes.
For example, countries may voluntarily use forest management Kyoto Protocol Art. 3.4 (more
here) to offset part of its emission reduction target. Such a decision requires thorough analysis
of the likely development of forest carbon stock. This in turn requires application of several
types of prediction tools, which aid analysis and interpretation needed for making the optimal
choice. Evidently, this is very challenging, as the analysis should include management
scenarios, species-specific differences and interactions of biomass and soil compartments.
Several types of models are available to address the issues of carbon budget. A traditional
type of models used in forestry are regression models, which are tree and/or stand level
growth models based on empirically derived statistical relationships between biometric
parameters of trees or stands, and production, which is most often expressed as height and
volume growth. Such models are simple and hence easy to apply. However, they include no
causality and hence do not provide much explanatory power for ecosystem analysis under
changing growth conditions. In contract to regression models, the process models include ecophysiological processes describing ecosystem functioning in terms of key processes and their
interactions. They simulate ecosystem development as a result of eco-physiological processes
described mechanistically. Such models are able to quantify effects of e.g., change in climate,
elevated CO2, nitrogen deposition and land use scenarios. Moreover, ecosystem process
models include both soil and biomass components and their interactions. A major drawback
of process-based models is their complexity. They usually require a considerable set of ecophysiological and site parameters. Therefore, a critical task for the application of a process-
based model is its parameterisation, including sensitivity analysis of model output to the input
data and parameters. Secondly, for both types of models, verification of model simulations on
real observations is needed to gain further confidence in predictions.
This contribution describes application of a process model BIOME-BGC (RUNNING and
HUNT, THORNTON 1998) that was specifically adapted for application to managed forest
ecosystems. It addresses the topics of sensitivity analysis as performed in TATARINOV and
CIENCIALA (2005) and model application to long-term observations of above-ground
production from permanent research plots based on the study of CIENCIALA and TATARINOV
(2006). A sensitivity analysis of BIOME-BGC was previously conducted by WHITE et al.
(2000). These authors were mostly interested in parameters important for daily net primary
production (NPP), while this study is focused on individual carbon pools and key parameters
that affect changes of these pools. Other studies also exist that previously applied BIOMEBGC to managed forest ecosystems (e.g. PIETSCH et al. 2003, PIETSCH and HASENAUER 2002,
CHURKINA et al. 2003, VETTER et al. 2005), but they did not include a wider set of
management options apart from final cut. The study presents the predicted and observed
aboveground production for individual tree species and discusses the topics important for
model application to managed stands.
2. Material and Methods
2.1. Model description, adaptation and application
We used the Biome-BGC (RUNNING and HUNT 1993, THORNTON 1998) model version 4.1.1 adapted so as to
include key management routines (thinning, felling, tree species selection) with some additional changes to
interception, evaporation and throughfall (TATARINOV and CIENCIALA 2006). BIOME-BGC is a process-based
model operating with a daily time step. It describes distribution of energy and cycles of water, carbon and
nitrogen for a specific type of terrestrial ecosystem. The calculation of gross primary production follows
FARQUHAR et al. (1980), distinguishing illuminated and shaded foliage. Autotrophic respiration is separated into
maintenance respiration calculated proportionally to nitrogen content of living tissues (RYAN 1991) and growth
respiration that is handled as a function of carbon allocated to the different plant compartments. Other details on
the applied model can be found in TATARINOV and CIENCIALA (2006). The model requires site parameters, ecophysiological parameters and series of daily meteorological data as input information. The meteorological data
series including minimum and maximum daily temperatures and daily precipitation were extrapolated from a
closest reference weather station to a given locality via MTClim simulation model (RUNNING et al. 1987,
THORNTON and RUNNING 1999). Altogether 40 weather stations situated all over the Czech Republic with data
series from 1961 to 2000 were available for this purpose. The mean annual precipitation totals for each plot
required by MTClim for the extrapolation of the actual precipitation from the base station to the individual plots
were obtained from the spatial data set of the annual mean temperatures and precipitation totals all over Czech
Republic with the regular grid of 1 to 1 km (KVĚTOŇ 2001). The ambient CO2 concentration for each simulation
year was taken from the Mauna Loa record (since 1959, KEELING and WHORF 2004) and from Law Dome ice
cores (before 1959, ETHERIDGE et al. 1998). The industrial nitrogen deposition was set in the range from 6 to
14 g⋅m-2year-1 at reference year 2000 for different plots based on the maps of the Czech Hydrometeorological
Institute (2001).
BIOME-BGC simulation procedure always included spin-up simulation and pre-defined historical land-use
scenario. The simulation of the current stands started with the stand felling and planting that matched the year of
stand establishment. The model was run with species-specific parameter sets (CIENCIALA and TATARINOV 2006),
while site parameters varied for individual sites. The applied current and historical scenarios are described below.
2.2. Management scenarios
The current management included intensity and timing of thinning events corresponding to
the recommendation of the Czech Forestry Act. The actual timing (stand age) and thinning
intensity for the analysed plots was derived from the recorded data from the individual
research plots and complemented from the Czech growth and yield tables (ČERNÝ et al. 1996)
for the earlier period of stand development that was not covered by the long-term plot
inventory. Thinning volumes were converted to corresponding share of biomass removed
from the sample plot at given age. Besides thinning regime, the mortality rates were changed
for the current stand generation: fire mortality was set to zero and the whole plant mortality
was reduced to 0.002 yr-1. This expressed the partial compensation of natural mortality by the
imposed thinning regime.
To reflect land-use management history of the studied sites, we applied two historical
management scenarios based on site elevation. This was based on the available historical
land-use records (NOŽIČKA 1957). For elevations below 800 m, we applied clear-cut in the
Middle Age (XIV century) with temporal transformation of forest into the grassland, followed
by the forest restoration in XVII century with four 100-year long forest rotations. For
elevation above 800 m, we applied two 100-year long forest rotations starting in XVIII–XIX
century followed by the simulation for the actual stand rotation.
2.3. Sensitivity analysis
The sensitivity analysis was focused on the effect of individual site and eco-physiological
parameters on the set of the key output variables under steady state. The output variables
included carbon content in plants, litter and soil and total carbon content (abbreviated as Cp,
Cl, Cs, Ct, respectively) and mean daily net primary production (NPP). The following text
provides only basic description of the performed procedures. The sensitivity of output
variables (y) to input parameters (x) (or the effect of parameter x on the variable y), ∆y/∆x was
calculated as ratio of output variable change to parameter change (both in %). The sensitivity
to qualitative variables, as forest type and aspect, was calculated as a ratio of two output
variable values corresponding to two parameter values y(x1)/y(x2). The ranking of parameter
sensitivity was based on the effect magnitude (|∆y/∆x|, in absolute values), distinguishing a)
very sensitive parameters (|∆y/∆x| > 0.2) ii) sensitive parameters (effect between 0.1 < |∆y/∆x|
< 0.2) and iii) parameters with low sensitivity (|∆y/∆x| < 0.1). Sensitivity analysis focused on
Norway spruce (Picea abies [L.] Karst.) and European beech (Fagus sylvatica L.). Other
details and methodological approaches of sensitivity analysis are described in TATARINOV and
CIENCIALA (2006).
2.4. Observed stand biomass
The observed stand production was obtained from the database of the Permanent Research
Plots (PRP). The plots selected for the model analysis represented mono-specific stands of
spruce, pine, beech and oak with the share of basal area for the individual species above 90%.
Other criterion for plot selection was a long-time series of recurring growing stock inventory.
The final set of stands included 22 plots of four major species with different elevation and site
classes. Each included information on plot altitude, stocking density, stand volume, age, mean
stem diameter and height. The inventory measurements were performed repeatedly four to
eight times with intervals of about five years. Available inventory data were presented before
and after thinning (being equal if no thinning occurred), which also gave information on the
actual thinning volumes. Stand volume was converted to total above-ground biomass using
age-dependent species-specific biomass expansion factors, which were derived from a large
dataset of tree-level information of the PRP database and species-specific biomass equations
of WIRTH et al. (2004), CIENCIALA et al. (2005 and 2006) and PAŘEZ et al. (1990).
3. Results
3.1. Sensitivity analysis – effect of site and ecosystem parameters
The assessed effects ∆y/∆x (for continuous x) and y(x1)/y(x2) (for discrete x) from the
sensitivity analysis are presented in Table 1 and 2. The negative sign of ∆y/∆x means that
parameter increase results in a decrease of the variable.
Table 1. Sensitivity (ratio of % of variable change to % of parameter change) of the selected model output
variables at steady-state to the most important site parameters, namely elevation (H, m), soil texture – fraction of
sand particles (βs, %), effective soil depth (d, m), CO2 concentration and nitrogen input (sum of nitrogen
background and industrial deposition and nitrogen fixation, Nd). The following default values of parameters were
applied: βs = 30 %, slope = 0, albedo = 0.2, H = 450 m and no slope (ϕ = 0). Bottom index indicates the ranking
of sensitivity (0.1 to 0.2 – index 1, medium sensitivity, above 0.2 – index 2, high sensitivity). No index means
low sensitivity.
Parameter
Constant
H (450–550 m)
H (450–550 m)
βs (10–30%)
βs (30–50%)
βs (50–80%)
βs (30–50%)
βs (50–80%)
d (1.0–1.5 m)
d (0.5–1.0 m)
d (0.3–0.5 m)
d (1.0–1.5 m)
d (0.5–1.0 m)
d (0.3–0.5 m)
CO2 (298–350 ppm)
CO2 (298–350 ppm)
Nd (0.5–0.7)
Nd (0.3–0.5)
Nd (0.2–0.3)
Nd (0.5–0.7)
Nd (0.3–0.5)
Nd (0.2–0.3)
Spruce
Beech
Spruce
Spruce
Spruce
Beech
Beech
Spruce
Spruce
Spruce
Beech
Beech
Beech
Beech, 450 m
Spruce, 450 m
Beech, 450 m
Beech, 450 m
Beech, 450 m
Spruce, 450 m
Spruce, 450 m
Spruce, 450 m
Sensitivity
(% variable change) / (% parameter change)
Carbon pool
Plant
Litter
Soil
Total
0.352
0.352
0.402
0.442
0.312
0.272
0.262
0.302
-0.03
0.06
0.06
0.01
-0.01
-0.01
-0.09
-0.151
-0.412
-0.402
-0.442
-0.472
-0.02
-0.01
-0.10
-0.141
-0.422
-0.402
-0.562
-0.622
0.482
0.482
0.322
0.181
0.552
0.552
0.412
0.302
0.432
0.592
0.592
0.502
0.352
0.342
0.272
0.242
0.452
0.482
0.492
0.462
0.922
0.892
0.802
0.762
0.562
0.782
0.762
0.622
0.482
0.662
0.642
0.542
0.04
0.05
0.06
0.05
0.262
0.372
0.402
0.292
0.612
0.862
0.892
0.682
0.00
-0.01
0.00
0.00
0.622
0.672
0.782
0.682
0.572
0.702
0.782
0.662
Spruce
1.5
1.5
NPP
0.432
0.302
-0.02
-0.161
-0.482
-0.141
-0.642
0.171
0.282
0.452
0.191
0.452
0.782
0.572
0.462
0.05
0.252
0.632
-0.01
0.602
0.582
Beech
Plant
Soil
Sensitivity (-)
Sensitivity (-)
Plant
Soil
0.5
-0.5
-1.5
0
20
40
60
Sand fraction (%)
80
100
0.5
-0.5
-1.5
25
30
35
40
Specific leaf area (m 2/kg C)
45
Figure 1. Sensitivity of simulated steady-state carbon pools to the model parameters: example of site parameter
(sand fraction, Spruce, left) and ecophysiological parameter (specific leaf area, Beech, right).
Among the site parameters, the most significant effect on steady-state carbon pools and NPP
were site elevation, soil texture (sand fraction), soil depth, ambient CO2 concentration and the
total nitrogen deposition (Table 1). The effect of elevation was positive for the tested tree
species. Specific dynamic can be observed for the effect of soil texture that increased with
increasing share of sand for both species (Figure 1). The effect of soil depth was strong and
increasing with more shallow depths (Table 1). The effect of industrial nitrogen deposition
was rather strong, but quickly saturating above certain threshold.
Table 2 Sensitivity of simulated steady state carbon pools to single eco-physiological parameters. All
simulations were performed for elevation 450 m, soil with 30% of sand and ϕ = 0. Bottom index indicates the
ranking of sensitivity (0.1 to 0.2 – index 1, medium sensitivity, above 0.2 – index 2, high sensitivity). No index
means low sensitivity. Parameter abbreviations: gs, max – maximum stomatal conductance, SLA – specific leaf
area, VPD – vapour pressure deficit, WP – leaf water potential. * = Dimensionless.
Parameter
Tree species
Beech
Fine roots C: N
Leaf C: N
gs,max
gs,max
N in Rubisco
New fine rootC: leafC
New stemC: leafC
SLA
VPD full reduction
Spruce
Fine roots C: N
Leaf C: N
Total mortality
Fire mortality
gs, max
Leaf turnover
N in Rubisco
New fine rootC: leafC
New stemC: leafC
SLA
WP full reduction
Unit
Range
Sensitivity
(% variable change)/(% parameter change)
Carbon pool
NPP
Plant
Litter
Soil
Total
Dim. *
Dim. *
m⋅s-1
m⋅s-1
Dim. *
Dim. *
Dim. *
M2kgC
Pa
50–72
19–25
0.004–0.005
0.005–0.006
0.07–0.088
1.43–2.0
2.0–2.71
35.0–37.9
2500–3000
0.832
-0.522
0.08
-0.201
1.552
-0.942
0.482
-0.09
-0.07
1.202
-0.882
0.652
0.201
2.192
-1.282
0.121
0.412
0.252
1.202
-0.822
0.642
0.212
2.172
-0.772
-0.372
0.442
0.262
0.932
-0.612
0.232
-0.09
1.722
-0.932
0.282
0.06
0.02
0.632
-0.622
-0.252
-0.312
1.512
-0.372
-0.372
-0.141
-0.161
Dim. *
Dim. *
Year-1
Year-1
m⋅s-1
Year-1
Dim. *
Dim. *
Dim. *
M2kgC
MPa
27.6–37.1
27.7–43.0
0.005–0.006
0.002–0.005
0.004–0.006
0.19–0.24
0.04–0.05
0.66–1.4
1.45–2.02
7.8–9.4
-2.5–2.3
0.692
-0.181
-0.832
-0.872
-0.532
0.402
0.682
-1.552
0.822
-0.252
0.06
0.802
-0.262
0.04
-0.252
-0.212
0.322
0.812
-2.062
0.742
-0.121
0.272
0.812
-0.222
0.03
-0.242
-0.181
0.312
0.812
-1.522
0.342
-0.111
0.272
0.742
-0.201
-0.452
-0.572
-0.392
0.362
0.732
-1.582
0.662
-0.191
0.141
0.722
-0.151
-0.08
-0.151
-0.512
0.482
0.742
-1.062
0.452
-0.362
0.232
The most significant ecophysiological parameters are listed in Table 2. From the tested
parameters, the nitrogen content in Rubisco and the allocation ratio of new fine roots carbon
to new leaves carbon had the highest effect on NPP and output carbon pools, reaching up to 2.
C : N ratios of leaves and fine roots, new stem carbon to new leaves carbon allocation ratio,
specific leaf area (SLA), leaf turnover and maximum stomatal conductance (gs,max) mostly
high effect on the tested carbon pools and NPP. The effect of SLA for plant and soil carbon
pools of beech is shown in Figure 1.
3.2. Predicted and observed aboveground biomass
The predicted and observed carbon held in aboveground biomass (CAB) for the set of analysed
permanent research plots (n = 22) is shown in Figure 2. The model was generally able to
match the observed data well, with slightly better results for conifers as compared to
broadleaved species. For the stands of broadleaved species (Figure 2 left), the coefficient of
determination (r2) and standard error of estimate reached 0.87 and ±1.2 kg C/m2, respectively
(n = 49 observations). This analysis excluded oak plot No. 6061602 site-located in a
floodplain region. As for coniferous stands (Figure 2 right), the corresponding results were
r2 = 0.95 and SE ±0.87 kg C/m2 (n = 68 observations). The example plots of the individual
species with the simulated and observed CAB on time axis are shown in Figure 3.
25
Beech
Oak
-2
20
CAB modelled (kg C m )
-2
CAB modelled (kg C m )
25
15
10
5
0
0
5
10
15
20
CAB measured (kg C m -2)
Pine
Spruce
20
15
10
5
0
0
25
5
10
15
20
CAB measured (kg C m -2)
25
Figure 2. The scatter of the measured and simulated carbon pool of aboveground tree biomass (CAB) for the
analysed permanent research plots of broadleaved species beech and oak (left) and coniferous species pine and
spruce (right) with inserted regression line. The points of the oak plot No. 6061602 (left, open symbols) situated
in a floodplain area are excluded from the regression.
25
Pine 500622
Spruce 501625
2
CAB (kg/m )
20
2
CAB (kg/m )
20
25
Beech 501118
Oak 501069
15
10
5
0
0
15
10
5
20
40
60
80
Age (years)
100
120
0
0
20
40
60
Age (years)
80
100
Figure 3. Simulated (lines) and observed (symbols) aboveground biomass carbon (CAB) pool during current
rotation – the examples of permanent research plots of broadleaved (left) and coniferous (right) tree species.
4. Discussion
The sensitivity analysis of the model identified the key site and eco-physiological parameters
important for tree species-specific model application to managed forest stands in CentralEuropean conditions. It must be noted that the interpretation of parameter sensitivity analysis
is not easy, because of many interactions involved. With several key site parameters and
additional 34 eco-physiological parameters, BIOME-BGC is still considered as being still
relatively robust process model, hence also suitable for large-scale application. The sensitivity
analysis is the required step for efficient model application. The results demonstrated here and
those included in TATARINOV and CIENCIALA (2006) basically correspond to the fundamental
BIOME-BGC parameter sensitivity analysis performed for NPP by WHITE et al. (2000). The
extension of analysis to effect on carbon pools helped to identify some specific differences.
For example, the effect of specific leaf area (SLA), which is a critical parameter for LAI-
related variables, was decreasing with larger values for beech, being either positive or
negative with respect to plant carbon, while it remains positive for soil carbon (Figure 1,
right). This stands out against the observation of WHITE et al. (2000), who reported strong
negative effect of SLA on NPP for broadleaved and coniferous woody species. With respect
to site parameters, one of the key problems represents the effective soil depth, which is
seldom available, although it strongly affects both water and carbon budget fluxes. The model
has a simplified treatment of soil hydrology and does not consider drainage or ground water
supply. The latter was actually found to be the reason for model failure for the oak plot from a
flood-plain area (Figure 2). In such cases, model might be enhanced with additional routines,
which in the case of groundwater supply was demonstrated by PIETSCH et al. 2003. Other
details on sensitivity analysis can be found in TATARINOV and CIENCIALA (2006).
The comparison of model prediction with the observed stand biomass indicates the model
potential for analysis of carbon cycling in managed forests. The overall match of the modelled
and observed aboveground biomass is considered promising. It should be stressed that one
identical set of eco-physiological parameters was applied for all stands of given tree species,
while adapting site parameters only. Secondly, the observed data usually covered only a
fraction of the simulated rotation, while the other vital information for predicting the growing
stock, such as thinning volumes, was derived from the growth and yield tables (ČERNÝ et al.
1996), the application of which remained uncertain for the actually analysed plots. Other
source of uncertainty represents the applied land-use history for given sites. It can be
demonstrated that the residual effect can be long-term, especially on soil carbon pools (Figure
4).
100
80
60
Spruce site
2
80
Carbon stock (kg/m )
2
Carbon stock (kg/m )
Beech site
Plant
Litter
Soil
40
20
0
1300 1400 1500 1600 1700 1800 1900 2000
Year
60
Plant
Litter
Soil
40
20
0
1300 1400 1500 1600 1700 1800 1900 2000
Year
Figure 4. Effect of land-use management scenario on carbon pools on beech and spruce stands (note: different site class).
Virgin forest with carbon pools in equilibrium felled in XIV century replaced by grassland and afforested in XVII century
with planted forest of 100 year long rotation circle; thinning applied only during the last rotation.
Hence, it is apparent that model predictions must be interpreted cautiously. CIENCIALA and
TATARINOV (2006) provided additional check of model performance using an independent set
of the observed soil carbon data. These were derived from the permanent research plot
database including those plots, where such information was available. The comparison of
these independently observed data and model prediction of soil compartment for the selected
stands used in analysis of above-ground biomass showed general agreement on the level of
individual species. It was concluded, however, that soil compartments represent the most
uncertain components of carbon budget. Specifically challenging in this respect is the
reporting under Climate Convention (UNFCCC) and its Kyoto Protocol required by
individual pools. The methodological guidance of IPCC (2003) for emission inventory from
the sector including forestry justifies excluding those pools from reporting that do not loose
carbon. However, it is good practice to provide evidence for this, which is problematic
considering the uncertainty of evidence on historical land use, uncertainty in actual effect of
land-use change on carbon stocks and overall high variability of soil carbon stock and high
sampling requirements for any detection of significant carbon stock changes. BIOME-BGC
provides only indications of the likely trends in carbon compartments and more analytical
effort and/or alternative models are needed to gain confidence in soil carbon stock change
detection.
It is apparent that the current model can be further improved. At the same time, the model
must remain relatively pragmatic in order to be practically applicable for regional analysis,
which is our major aim of the effort spent on testing and adaptation of the model. The
distinguished feature to be improved is handling of multilayer vegetation, which would be
needed for a more realistic modelling of transition between old and new stand. Recently,
BOND-LAMBERTY et al. (2005) demonstrated an elegant adaptation of BIOME-BGC for such
situations. Other improvement should concern model parameterisation, which should be
further simplified for regional application. Specifically important are the site parameters,
some of which could be generalized by additional functional relationships.
5. Conclusions
This contribution highlighted some items of the sensitivity analysis identifying key ecosystem
and site parameters for species-specific model parameterisation. It demonstrated model
prediction of aboveground carbon stock for managed forest ecosystems of four major tree
species. The sensitivity analysis, model adaptation to managed forest stands and model
verification on the observed production data are the necessary steps towards a landscape grid
model analysis of regional responses of forest ecosystems to climate change under likely
management scenarios. This is the aim of the next model application.
6. Acknowledgement
The authors gratefully acknowledge the support of the Czech Science Foundation (GAČR),
Grant number 526/03/1021 (CzechRECAF) and of the Czech Ministry of Environment
(VaV/640/18/03 – CzechCARBO).
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