Global forests are the main component of the land carbon
sink, which acts as a partial buffer to CO
Forests globally provide ecosystem services including provision of timber,
fuel, and water; regulation of local climate and hydrology; carbon
sequestration; support of biodiversity; and recreation (Bonan, 2008; Mori et
al., 2017). The effects of climate change on forest productivity and
biodiversity may be predicted to be negative due to increased
evapotranspiration and reduced rainfall in many forested areas; an increase
in extreme events like droughts, wildfires, storms, and insect attacks; and
local or regional extinctions of plant and animal species (Easterling et
al., 2000; Seidl et al., 2011; Anderegg et al., 2013; Urban, 2015). On the
other hand, productivity may increase due to the fertilising effect of
increased nitrogen deposition and higher atmospheric CO
Forests make up the largest portion of the current land carbon sink and are
estimated to have absorbed 20 %–50 % of CO
Forests cover 33 % of Europe's land area (Forest Europe, 2015) and store
approximately 13 Pg C in vegetation and 28 Pg C in soils (Pan et al., 2011).
The carbon sink of European forests in 2000–2007 has been estimated at 0.27 Pg C yr
Dynamic vegetation models (DVMs) provide a potential framework for predicting the combined effects of climate and forest management scenarios on forest ecosystem structure and carbon balance. Based on such information, the potential of forest landscapes to contribute to climate change mitigation by maintaining or enhancing carbon sinks and to climate adaptation through sustained production of forest products and other ecosystem services in the face of climate change can be assessed. Applications of DVMs to represent climate responses of potential natural vegetation (PNV) have been shown in the past, for example as a basis for nature conservation planning (Hickler et al., 2012). Human management of land, including cropland, pasture, and managed forest, has been introduced in a number of global DVMs (Bondeau et al., 2007; Bellassen et al., 2010; Lindeskog et al., 2013; Arneth et al., 2017). Key elements required to represent managed forests in a DVM framework include the ability to initialise a simulation with historical land use; to represent age and size structure of forests stands and their change over time; to account for tree species composition; and to apply silvicultural treatments that modify stand composition and structure like planting, thinning, and harvesting.
LPJ-GUESS (Smith et al., 2001, 2014) is a second-generation DVM tailored for regional- and global-scale applications. It is one of few globally applicable DVMs that incorporate a detailed representation of forest ecosystem composition and stand dynamics, suitable for the implementation of a forest management scheme. It captures the distribution of European PNV at species level and can make projections of vegetation shifts under future climate scenarios (Hickler et al., 2012). The model has been shown to represent stand-level vegetation growth and succession successfully (Smith et al., 2014). It has been used to estimate forest vulnerability to climate change (Seiler et al., 2015) and carbon mitigation potential of regrowth forests and forests under alternative management scenarios (Pugh et al., 2019; Krause et al., 2020). Earlier versions of LPJ-GUESS have been modified to enable analysis of clear-cut forest management and the effects of wind damage and insect outbreaks (Lagergren et al., 2012; Jönsson et al., 2012). In this study, we describe the implementation of expanded forest management capabilities including even-age and clear-cut as well as uneven-age and continuous-cover management in LPJ-GUESS v4.0. In addition to detailed carbon and water cycle processes, this version of the model incorporates a dynamic nitrogen cycle and nitrogen limitation on plant productivity (Smith et al., 2014). In this way, forest management in LPJ-GUESS is for the first time fully integrated in a model version capable of simulating a landscape containing a mosaic of land-cover types like PNV, cropland, pasture, and peatland and with a sophisticated land-use and land-cover change functionality. Model alternatives for forest stand initialisation (land-use history and species and age distribution) and silvicultural management (detailed and automated harvest strategies) are presented in detail. Simulations using different forest management alternatives are evaluated against observations of standing volume and harvest for even-aged monospecific European beech and Norway spruce stands in central Europe. Using an automated thinning and clear-cutting approach for European forests, we compare modelled carbon stocks and fluxes with observational data and explore the dynamic behaviour of the model under changing climate forcing.
LPJ-GUESS (Smith et al., 2001, 2014) simulates the dynamics of terrestrial
vegetation and soils across a regional or global grid, forced by
meteorological and land-use inputs and soil physical properties. In the
absence of land use, each grid cell encompasses
a landscape of natural, climatically determined vegetation (PNV). Replicate
patches, nominally 0.1 ha (1000 m
Data structures in LPJ-GUESS relevant for this study. Patch number is defined separately for PNV and secondary stands. If a secondary stand is created from PNV or managed forest with intact vegetation, the patch number of the parent stand is used. During land-cover change events, stands belonging to forest stand types can only be reduced in size. Expansion of such stand types results in new stands.
Different land-use and land-cover types in addition to PNV are represented in the model by stand types with different management, e.g. cropland, pasture, and managed forest (Lindeskog et al., 2013, Fig. 1). Transitions between different stand types may occur at any point in time, according to land-use data inputs, to take into account land-use history or future land-use scenarios. When a potentially forested stand type area expands, new stands are created, keeping the soil history from the previous stand type intact and allowing vegetation succession to proceed from bare ground (in most cases; see Sect. 2.2.1). In modelled wood harvest events, 66 % of wood biomass and 30 % of leaf biomass are typically removed from the stand and the rest remains as litter. Removed leaf biomass and part of wood biomass (by default 67 %) is oxidised the same year. The remaining wood biomass is put into a product pool with a turnover rate of 4 % per year.
The typical forest management types covered in the model and presented in this paper are no management (pristine forests, simulated as PNV), even-aged forestry, typically modelled by stands with prescribed ages starting from bare ground after a specified land-use history, and uneven-aged/continuous cover forestry, typically modelled by a cohort structure within a patch derived from prescribed cuttings after starting as bare ground and a regeneration phase. Alternatives to these typical setups can be used to achieve age structures at other spatial scales, e.g. landscape level, and will be described below.
Forest stand age and species distributions can be achieved in the model by utilising the structure of a previous PNV stand or by defining a new age and species structure at various levels of detail.
A managed forest stand may be created in the model by two different options (Figs. 2, B1). By cloning the parent stand, the complete state with all patches intact is inherited by the secondary stand. If the origin is previous woodland (PNV or secondary forest), a cutting scheme may start with the existing tree structure, optionally cutting unwanted species. In the other alternative, tree growth starts from bare ground after an initial clear-cut or when expanding on former cropland or pasture. In this case (with an even-age stand and if disturbance and fire are turned off), the secondary stand can in many cases be modelled by a smaller number of replicate patches since there is usually no random variation in the timing of management events.
Examples of different histories and initialisations of modelled
forest stands at a southern Swedish site (55.75
Managed forest stands with an uneven age structure can be represented in the model by selecting different options, depending on the spatial context of the age classes, i.e. whether they correspond to tree age cohorts co-occurring within local stands thereby competing with each other or represent different fractions of a wider landscape with no local interactions between age cohorts. An age structure may be created in individual patches by thinning (enabling regeneration by increased light at the forest floor) at defined intervals during an initialisation period, allowing for both intraspecific and interspecific competition (Fig. 3a). When competition between different age classes does not apply, i.e. when the spatial context is that of a landscape, different age-classes can be modelled in separate patches. To achieve an age structure among patches within a stand, the semi-randomised age structure of PNV (see Sect. 2.1) may be conserved after the conversion to managed forest if the cloning functionality is used (Fig. B1). Alternatively, multiple patches in a secondary stand may be clear-cut successively, one by one, at regular intervals during an initialisation period (Fig. 3b). In the final approach, a prescribed age structure, either representing a specific moment in time, or a historical development, may be created among stands representing a stand type using land-cover change input data (Fig. 3c).
Examples of age structure setup at three different structural
levels, patch, stand, and stand type. Monocultures of European beech are
created from clear-cutting of PNV. The target in the year 2000 was three cohorts
of 100, 67, and 33 years.
Species mixtures may be defined either at the management type level (Fig. 1), using predefined planting densities for individual species and/or later cuttings to achieve prescribed relative biomass abundances of the different species within a patch (Fig. 4a, see below), or at the landscape level, using land-cover input data to achieve predefined groundcover area-based mixtures of monocultures (Fig. 4b), or a combination of both of these options.
Examples of species structure setup at the patch and forest level.
Beech–spruce 60 %–40 % mixes are created after clear-cutting of PNV.
Two types of harvest systems are available in the model: clear-cutting and continuous cutting, which are used in conjunction with the even-aged and uneven-aged/continuous-cover age structure systems, respectively (Table 1). Depending on the level of detail in historic forest management input data or, in simulations of future scenarios, whether the management should be able to adapt to a changing climate or other factors, various model alternatives are available.
Detailed forest management options. All management options except re-establishment can be defined in separate management types (see Fig. 1), which may be selected in a stand type rotation scheme at pre-defined calendar years.
A simplified method to represent forestry using global wood harvest input data (e.g. harvested area) is achieved by creating secondary forest stands after clear-cutting either a PNV stand or other secondary forest stands, representing cutting of primary or secondary forest, respectively. In cutting events, looping through the stands, these are cut according to stand age rules (cut oldest or youngest stands first, avoid cutting stands younger than 15 years old), allowing the allocation of wood harvest to primary forest and mature or young secondary forest. This method was used by Pugh et al. (2019) with reconstructed time series of land use from the Land Use Harmonization Project (LUH2, Hurtt et al., 2017)
A number of forest management options can be selected at the stand type or management type level in the LPJ-GUESS instruction text file required to run a simulation and used with both even-aged and uneven-aged forestry (Table 1).
A forest stand may contain a full selection of tree species (as in PNV) or a selection of species defined in the management type. After a clear-cut event, or after creating a new forest stand from bare ground or grassland, selected species may be planted at defined sapling densities with or without the additional need to fall within the envelope of the bioclimatic limits that govern PFT establishment in PNV mode (Table A1). Re-establishment can be optionally enabled or disabled for selected and unselected species. If several tree species are selected, it is possible to prescribe a target relative abundance for each species and apply cutting to regulate the mixing proportion. Relative biomass values of selected species are then monitored at 5-year intervals, and if the values deviate more than 10 %, dominant species are cut to reach the target (Fig. 4a).
A fixed rotation period is defined at the end of which a clear-cut takes place (Fig. 5a). Alternatively, a clear-cut may be triggered by attainment of a prescribed stand density limit (Fig. 5b). The timing of a number of thinning events (default 5) may be defined as fractions of the rotation period in the case of a fixed rotation period. The harvest amount (intensity) for such thinning events is defined as a fraction of current biomass, with the option of different settings for selected and unselected species. At each thinning event, trees may be cut using alternative strategies. Available size and age criteria are (1) old or big trees first (“from above”), (2) young or small trees first (“from below”), (3) a specified harvest amount pertaining to trees above a specified diameter limit only (“threshold diameter thinning”), and (4) all sizes and ages cut equally. These may be combined with the following species criteria: (1) selected species first, (2) unselected species first, (3) separately defined harvest amounts for selected and unselected species, (4) shrubs and shade-intolerant species first, and (5) all species cut equally (Fig. 5a). In (1) and (2) size overrides age settings.
Examples of forest management settings. Forestry stands were
created from clear-cutting of PNV in 1901.
When modelling continuous cutting, it is possible to define the same harvest parameters and cutting priority settings as described above for the clear-cutting case for two different periods: the first for a specified “regeneration” time following a clear-cut and the second for a “continuous” phase in which the cutting cycle is repeated indefinitely (Fig. 5c).
As an alternative to specifying thinning in clear-cut forestry in detail, a thinning scheme based on Reineke's self-thinning rule may be chosen (Fig. 5b). The implementation follows Bellassen et al. (2010):
The parameters
As an alternative to imposing a specified rotation length in clear-cut
forestry, a clear-cut may be triggered by stand density when it is below
A specified amount of plant-available nitrogen may be applied to the soil evenly distributed over the whole year (Fig. B2). With irrigation enabled, the amount of water required to avoid water stress is calculated and applied to the soil surface every year.
To capture management changes, a new silvicultural treatment of a stand type can be prescribed any specified calendar year, changing from one specified management type to another with the next harvest event as an optional trigger (Fig. 6).
Example of management change during an ongoing simulation. Spruce monoculture changed to mixed broadleaved (both with automated thinning and clear-cutting). Management change is activated after first management has completed by a clear-cut event. Location, climate input, and species in PNV are as in Fig. 2.
To demonstrate the implemented forest management functionality and its
effects on simulated stand structure, composition, and productivity, we
performed demonstration simulations for representative locations
(grid cells) in Europe and across Europe as a whole. PNV stands were
modelled using 25 replicate patches and a disturbance return time of 400 years. Managed forest stands contained only one patch except where
explicitly stated (Sect. 2.5), disturbance and fire were turned off, and
mortality was deterministic. In managed forest stands created after clearing
the previous vegetation, this setup saves computational time and produces
almost identical results compared to using multiple patches and adding the
stochastic component to establishment and mortality. Parameters for European
species were adopted from Hickler et al. (2012) with updated parameters
(Tables A1–A2) and with the addition of
Historic (1901–2015) monthly temperature, radiation, and precipitation data
at 0.5
In future climate scenario simulations, monthly temperature, radiation, and
precipitation data for 1850–2100 were adopted from the IPSLCM5A-MR (Dufresne
et al., 2013) GCM (global climate model) projections from the CMIP5 ensemble (Taylor et al., 2011).
Projections forced by the RCP 4.5 and 8.5 future radiative forcing scenarios
were used. The raw GCM climate output fields were interpolated to
0.5
In future forest projections, either the historic environmental drivers were
recycled after 2015 or future climate, CO
A grid cell in southern Sweden (55.75
Four datasets of European beech and Norway spruce monoculture stand time series (1–21 points in time) of standing volume and harvested volume were used in simulations to initialise species and age structure, assuming a landscape distribution of even-aged stands. The stands were located in central and southern Germany (GER-Bav, GER-C, GER-CS) and northern Slovenia (SLO, beech only) (Appendix D, Table D1). The model setup and input climate data were as described in Sect. 2.4. Three different harvest strategies were used: no harvest, detailed harvest from observations, and automated thinning and clear-cutting (Sect. 2.3.2). The setup of the detailed harvest for stands from the different datasets differed slightly depending on the number of harvest data points. For the stands from the GER-Bav, GER-C, and SLO data sources (3–21 data points per stand), the harvest data (fraction of biomass) were used in the simulations at the reported timings. During the time period prior to the first harvest data point, mean harvest intensities from the harvest data were used, in the case of GER-Bav and GER-C converted to fit a 5-year harvest interval, while in the case of SLO keeping the 10-year interval used in the sampling. The GER-CS data contain only one harvest data point for the whole stand lifetime (100 years). In this case, harvests were performed at 5-year intervals during the whole simulation using the calibrated harvest intensity values required to obtain a cumulative harvest fraction equal to the reported harvest fraction for the whole 100-year period. Thinnings in the detailed harvest simulations were performed equally for the different cohorts to obtain some regeneration of saplings in old stands. The automated thinning and clear-cutting method used the parameter settings in Table A3 and thinnings from below started at a stand age of 10 years.
To constrain European secondary forest age and species structure in the
model to the actual state of the forests, we used the global forest age
dataset GFAD (Poulter et al., 2019; Pugh et al., 2019), describing the
0.5
The EFI Tree species map describes the spatial distribution (fraction of
land area) of 20 tree species groups at 1
The structure of European forests in 2010 was reconstructed by using a
combination of the GFAD age database and the EFI Tree species map. For each
grid cell, the most common species or species group within the GFAD NE and
BD forest types was obtained from the EFI Tree species map and these were
then mapped to LPJ-GUESS tree species or species groups (Table C1, Fig. C2). In
the multi-species LPJ-GUESS groups, species compete with each other for
resources (see Sect. 2.1). BE was mapped to
Secondary forest stands were created in the model from 1871 to 2010 to
obtain the GFAD age (1–140 years) distribution in 2010, and species
selections were planted (without climate restrictions for NE and ND stands
to bypass establishment temperature limits used in PNV). The oldest forest
class in GFAD (
To perform a limited sensitivity test of some of the uncertainties in land-use and residue removal assumptions, additional alternative simulations were performed: a simulation where a fraction (as in standard harvest) of the biomass of trees killed in natural disturbance events in old-growth forests was removed from year 1871, simulating an extensive wood harvest scheme and two simulations where the leaf removal fraction in harvest events was set to 10 % and 0 %, respectively, instead of the standard 30 % value.
Growing stock, net annual increment (NAI), and harvested volume were
calculated from vegetation carbon, net ecosystem exchange (NEE), and total
carbon of harvested trees, respectively, by multiplying with expansion
factors for each country, ranging from 1.1 to 3.5 (mean 2.7) m
Modelled and observed standing volume
Secondary forest stand initialisation and land-use history have long-term
effects on the development of tree species distribution, productivity, and
carbon fluxes in the model (Fig. 2). When the age distribution and species
composition from spin-up is retained in each patch (i.e. cloning PNV), both
the warming climate in the 20th century and the prevention of fires and
other disturbances result in an increase in tree biomass and a tree species
shift from a
Modelled carbon sink
The choice between the different age and species structure setup options depends on whether competition between species and cohorts within patches is required or not (Figs. 3–4). Also, the desired level of detail of the age structure might decide whether to use a simplified setup or a detailed structure with many separate stands, increasing computation time. Setups using separate stands for each species–age combination offer the possibility of reflecting regional distributions based on inventory data but will not represent competition correctly e.g. in mixed forests.
Although management changes during the course of a simulation may be
prescribed, using detailed but static harvest methods would not reflect
foresters' choice of gradual adaptation of harvest parameters under changing
CO
Modelled and observed forest vegetation carbon stock in Europe.
Central European beech and Norway spruce stands were modelled with three
harvest alternatives: no harvest, detailed harvest based on reported
harvested volumes, and automated thinning and clear-cutting. The model was
not able to reach the high productivity of beech and spruce stands in
Germany. The modelled standing volumes of these stands were relatively
accurate at low standing volumes but about 2–3 times underestimated at high
observed standing volumes (Fig. 7a). The correlation between modelled and
observed German standing volume was generally good:
Dominant tree species in managed forests based on the EFI species map differ
from PNV simulations in large parts of Europe. In central and eastern
Europe, broadleaved species are to a large degree replaced by needleleaved
species in managed forests, especially by
For the European continent, the modelled mean vegetation carbon density (5.7 kg C m
Modelled and observed total carbon stock, soil plus litter carbon, and net ecosystem exchange (NEE) in European forests.
Modelled and observed growing stock (GS) in European forests in 2010 and net annual increment (NAI) and fellings in forests available for wood supply (FAWS) in Europe for 2001–2010.
Modelled vegetation carbon, total carbon pool, growing stock, NAI, and
fellings for individual European countries show varying levels of agreement
with reported values, with the best fit for vegetation carbon and growing
stock (
Modelled and observed (Forest Europe, 2015) values for individual
European countries, excluding Georgia, Iceland, Cyprus, Malta, and Russia, in
2010. Vegetation carbon
Modelled and observed (Forest Europe, 2015) values for individual
European countries. Growing stock (GS) in 2010
Modelled and observed (Forest Europe, 2015) yearly fellings for
individual European countries in 2001–2010 showing a
Carbon pools and fluxes were partitioned into old-growth and regrowth forest
components (modelled as PNV and secondary forest stands, respectively) (Fig. 12, Tables 5–6). Modelled European old-growth and regrowth forests have
about equally sized vegetation carbon pools in 2000 (about 7 Pg C each) but
with a downward trend for old-growth forests in 2001–2010 driven by a
reduction in area. The vegetation carbon density in old-growth forests,
increasing from 8.5 to 9.2 kg C m
Modelled European forest vegetation carbon for 2000, 2010, and
2015 and carbon sink (
Vegetation carbon and total carbon stock in European forests separated into regrowth and old-growth forest.
Net ecosystem exchange (NEE), harvested carbon, and natural
mortality in European forests
For the European continent, including thinning in the simulation reduced
total forest vegetation carbon, soil plus litter carbon, total carbon pool, and
growing stock in 2010 by 3 %–5 %; increased the magnitude of NEE in
2000–2007 by 39 %; and increased NAI in 2001–2010 by 100 % compared to
a simulation without thinning (Figs. 13–14, Tables 2–4). In regrowth forests,
including thinning reduced vegetation carbon by 6 %–7 %, soil plus litter carbon,
and the total carbon pool by 5 %–6 % in 2000–2010 and increased the
magnitude of NEE in 1991–2010 by 41 % (Tables 5–6). The average thinning
rate on regrowth forest land was 1.9 % of wood biomass per year in 2001–2010.
Including thinning generally improved the match of simulations with observed
data. The increased regrowth forest carbon sink seen in a simulation with
thinning (0.12 kg C m
Simulated forest
Simulated forest
In a simulation with removal of biomass during disturbance events in the
old-growth stands (not shown), the carbon sink in this forest class
increased to 0.04 Pg C yr
Simulations with alternative settings of leaf removal fractions during harvests of 10 % or 0 %, instead of 30 % in the standard simulation (not shown), decreased the total carbon sink in 2001–2010 by 0.9 % and 1.3 %, respectively, resulting from an increased soil respiration of 0.3 % and 0.4 %, respectively, partially offset by an increase in NPP by 0.06 % and 0.09 %, respectively. Vegetation carbon increased by 0.08 % and 0.13 % and soil plus litter carbon increased by 0.07 % and 0.10 % in these simulations.
To demonstrate the automated harvest methods in which thinning intensity
and rotation times are adjusted to maintain standing stock when stand
productivity changes in response to forcing conditions, we used
CO
LPJ-GUESS representations of unmanaged forest have previously been shown to compare favourably with observed forest vegetation succession, growth, stand structure, biomass, and regrowth timescales (Smith et al., 2001, 2014; Pugh et al., 2019), and land-use and land-cover change (LULCC) functionality has been included in the model since version 4.0 (Lindeskog et al., 2013). In a recent global study that used the model to analyse the carbon stocks of old-growth and regrowth forests (modelled as primary and secondary forest stands, respectively), without applying wood harvest (Pugh et al., 2019), the total forest carbon sink was found to be about 50 % of values reported by Pan et al. (2011) based on upscaled inventory data. Disregarding wood harvest has been identified as causing underestimation of carbon sinks in vegetation models (Zaehle et al., 2006; Ciais et al., 2008). In an effort to improve the ability to simulate carbon pools and fluxes on managed land, we introduced new forest management options into LPJ-GUESS v4.0 and provide a comprehensive description of forest initialisation and wood harvest alternatives. The initialisation and harvest alternatives in the model are tailored to enable available forest inventory data and harvest information to be used to initialise and guide simulations. Ideally, both age and species structure, as well as land-use history and current wood harvest strategy, should be taken into account, but this is not always possible for simulations with a large spatial extent because of limited data availability. To demonstrate a possible workaround, we used an automated thinning and clear-cutting alternative to represent European regrowth forests, initialised on the basis of inventory-based age and species data but without wood harvest or LULCC data input. In simulations of central European beech and spruce stands, the automated thinning method was shown to result in similar modelled standing volume but often in a higher carbon sink compared to a more detailed harvest scheme based on reported harvest intensities (Fig. 7). The harvested volume was generally substantially higher in the automated thinning simulations, as the optimum harvested volume required to completely avoid self-thinning may not be realised in real managed forest stands. Ideally, automated thinning should be just enough to avoid self-thinning mortality in the model, so the biomass should not be severely reduced, but in old beech stands self-thinning is very low in the model (Fig. C1), and thus in these stands both detailed and automated harvests result in a relatively large reduction in biomass compared to unharvested stands (Fig. 7).
The modelled mean vegetation carbon density in European forests in 2000–2010
is close to observations from several published sources (Pan et al., 2011;
Liu et al., 2015; Forest Europe, 2015). Including thinning in the simulation
has a rather small impact on vegetation carbon (
Details in the simplified European setup might explain the remainder of the
“missing” carbon sink relative to reported values. One potential cause is
that old-growth (
The automated thinning and clear-cutting modelling strategy applied in the model in the present study is intended as an example for demonstrating the new forest management capabilities and an improvement on the age structure setup of Pugh et al. (2019) and does not include all available possibilities in the model. In addition to the shortcomings in the setup already noted concerning land-use history, many central European forests are managed by continuous wood harvest and not by clear-cutting and also consist of species mixes (Pretzsch et al., 2021). Estimating the effect of such different wood harvest strategies and monoculture or mixed-species alternatives on carbon stocks and fluxes is now possible and will be done in further studies. The self-thinning and tree-density-based harvest method is less successful in the northernmost and southernmost parts of Europe, where productivity is strongly limited by temperature and precipitation, respectively, and the self-thinning relationship between biomass and tree density in the model is weaker. The low simulated productivity of forests in the Mediterranean points to the need for a review of the parameterisation of tree species to reflect Mediterranean managed forests or the introduction of tree species that are not currently represented in the model (Fig. E8). While the model shows good skills when reproducing reported mean values for Europe's vegetation carbon and productivity, the correlation between modelled results and observations for the individual countries shows a large spread with no simple pattern for the deviations (Figs. E1–E5). However, it is obvious that modelled thinning intensities for countries in the Balkans, except Albania and Greece, are higher than the corresponding reported total harvest intensities. These countries also show a poorer fit to observed NAI values in a simulation with thinning compared to a simulation without thinning. In any case, including thinning in simulations improves the fit to observed national NAI values in most other countries.
Our simulation results using LPJ-GUESS exhibit similarity with results from
the ORCHIDEE DVM, which was applied with the same automated thinning method
at a central European site (Bellassen et al., 2010). The ORCHIDEE simulation
with automated thinning, compared to a simulation without thinning, gave a
similar vegetation reduction (7 %) and thinning fraction (0.55), reduced
heterotrophic respiration (ca. 20 %), and a carbon sink increase (67 %).
The forest NPP reduction over time in ORCHIDEE simulations (ca. 10 %) is
also seen in the average value for unharvested regrowth forests in European
simulations with LPJ-GUESS (Fig. E7b). The decline of NPP directly after
thinnings in ORCHIDEE is not simulated by LPJ-GUESS, but both models display
a short-lived increase in heterotrophic respiration after thinnings (not
shown). The recovery time after a clear-cut (when the stand turns into a
carbon sink) is 6 years in the example southern Swedish site with a standard
harvest removal, but it is 18 years if the harvested biomass is left on site (Fig. 2). This is similar to the ORCHIDEE results with a stand recovery time of
10–20 years after a clear-cut. A similar recovery time after
clear-cutting, 7–11 years, has been diagnosed based on CO
Responses of soil carbon and nitrogen cycling to harvest and fertilisation can be complex and qualitatively different in clear-cut and continuous-harvest systems (Parolari and Porporato, 2016). The coupled carbon–nitrogen cycling in LPJ-GUESS (Smith et al., 2014) should enable the investigation of the effect of different management practices on forest productivity and sustainability at both stand and regional scale in future studies. Nitrogen depletion of the soil in previous land-use history reduces forest productivity and causes a shift in species succession in the model (Fig. 2c). At the European scale, removing a smaller fraction of residues (0 % of leaves rather than 30 %) makes a small positive impact on productivity (0.1 %; see Sect. 3.4). However, since many European forests receive large amounts of atmospheric nitrogen deposition, other nutrients such as Ca, Mg, K, and P may be more important for limiting productivity, and acidification of the soil by N and S deposition may further decrease the availability of these nutrients (Sverdrup et al., 2006). Ca is especially close to or below the limit of sustainability in current forest management systems in southern Sweden (Sverdrup et al., 2006). Thus, ongoing development of limitation and cycling of additional nutrient species into LPJ-GUESS may be beneficial for capturing the full effects of different harvest regimes. Also relevant to achieving a better model of nutrient uptake is an improved representation of the soil profile.
While the mean productivity of European forests is captured well by the model
(Table 4), and mean productivity of forests in individual European countries
is captured reasonably well (Figs. 10, E4), the inability to reproduce observed
productivity levels in high-productivity beech and spruce stands in Germany
(Fig. 7a) highlights the need for allowing a wider range of productivities.
The lack of certain physiological processes in the model, e.g.
hardening and dehardening (Bergh et al., 1998), could explain why productivities
along the whole temperature gradient in European forests cannot be fully
represented in the model. Model tuning that aims for correct mean values of, for example, biomass and carbon fluxes over large geographic areas compensates for
an overestimation of productivity in northern Europe by lowering average
productivity along the whole temperature gradient. This could partially
explain, for example, why the productivity of some southern German sites is
underestimated, while average productivity for Germany as a whole is in line
with inventory data. Additionally, the selected individual German Norway
spruce and European beech sites in this study were generally of above-average site quality and are not fully representative of German forests,
which includes forests of other tree species, especially Scots pine (
The emergent competition between PFTs with similar shade-tolerance values in the model, e.g. beech and spruce, can deviate from actual dynamics, as seen in the poor performance of spruce compared to beech in a succession at the example site in southern Sweden (Fig. 5).
The management systems covered by the new forest management functionality in LPJ-GUESS include the most important features required for the improvement of modelling carbon pools and fluxes and the development of forest stands under future climates, but a few important additions will be desirable to include in the future. These include automated continuous wood harvesting and coppice management. For a good representation of coppicing, the model should also be improved to include plant carbohydrate storage. For better representations of European forests, land-use history, including litter raking, should be included to generate more realistic soil carbon pools by adapting functionality already available in the model.
PFT parameters used in this study. Values in bold text are updated compared to Hickler et al. (2012).
Common PFT parameters for shade tolerance, geographic range, growth form, and chilling requirement categories in Table A1. Values in bold text are updated compared to Hickler et al. (2012).
Parameters for automated thinning and clear-cutting.
Options when creating managed forest stands from PNV.
Effect of nitrogen fertilisation (50 kg ha
Mapping of EFI tree groups to LPJ-GUESS species selections.
Self-thinning log–log plots of quadratic mean diameter (Dg) and
tree density (dens) for simulations of
Mapping of dominant EFI tree species groups in the needleleaf
evergreen (NE) and broadleaf deciduous (BD) GFAD forest classes to LPJ-GUESS
species selections and the resulting dominant species (LAI) in 1986–2015 in
an LPJ-GUESS simulation with automated thinning. Abbreviations of EFI
species and species groups are as follows: Abies (
Comparison of dominant EFI tree species groups (area) and modelled LPJ-GUESS managed forest dominant tree species (LAI) in 1986–2015 in an LPJ-GUESS simulation with automated thinning. Abbreviations of LPJ-GUESS species are as in Fig. C2.
Modelled LPJ-GUESS dominant species (LAI) (including grass) in
The GER-Bav dataset contains pure European beech (three sites) and pure Norway
spruce (three sites) and comes from the database of the Chair of Forest Growth and
Yield Science TUM School of Life Sciences Technical University of Munich.
Mean annual temperature is 6–7.7
The GER-C dataset contains pure European beech stands (three sites) and pure
Norway spruce stands (five sites) and comes from the database of long-term
research plots from Nordwestdeutsche Forstliche Versuchsanstalt, Abteilung
Waldwachstum. Site quality is from average to above average, mean annual
temperature is 6.5–8.5
The GER-CS dataset (Pretzsch, 2005; Pretzsch and Biber, 2005) is derived from
long-term thinning experiments in pure stands of Norway spruce (eight sites) and
European beech (nine sites), mostly in the lowlands or subalpine parts of
southern and central Germany. Plot sizes were 0.25–0.5 ha. The spruce plots
were concentrated on the southern German Pleistocene in the natural habitat of
Norway spruce and were artificially established in re-afforestation after
clear-cutting or afforestation of cropland and pastures. The site fertility
was excellent (class I and II). The plots were subjected to light, moderate,
and heavy thinning as was also the case for the GER-Bav dataset. The beech plots represented
sites with average to very good fertility on red marl and red sandstone
soils in central Germany and were the result of natural regeneration
following cutting according to a compartment shelterwood system, resulting
in consistently even-aged stands despite natural regeneration. For the beech
plots, mean annual temperature is 6.5–8.8
The SLO dataset consisted of 27 forest sub-compartments of an average size
of 25.6 ha from the high karst plateau Pokljuka in the Alps (46.35
Central European beech and Norway spruce site data used in the study.
Location of the beech and spruce sites for the four stand datasets.
Modelled and observed (Forest Europe, 2015) vegetation carbon for individual countries in 2010. LPJ-GUESS is the simulation without thinning. LPJ-GUESS thin is the simulation with automated thinning.
Modelled and observed (Forest Europe, 2015) total carbon pool for
individual countries in 2010. LPJ-GUESS is the simulation without thinning.
LPJ-GUESS thin is the simulation with automated thinning.
Modelled and observed (Forest Europe, 2015) growing stock (GS) for individual countries in 2010. LPJ-GUESS is the simulation without thinning. LPJ-GUESS thin is the simulation with automated thinning.
Modelled and observed (Forest Europe, 2015) net annual increment (NAI) for individual countries in 2001–2010. LPJ-GUESS is the simulation without thinning. LPJ-GUESS thin is the simulation with automated thinning.
Modelled and reported (Forest Europe, 2015) yearly fellings for individual countries in 2001–2010. LPJ-GUESS is the simulation without thinning (clear-cutting at creation of secondary forest). LPJ-GUESS thin is the simulation with automated thinning. Reported values are missing for Belarus and Luxembourg.
Simulation of European old-growth and regrowth forests with
(Regrowth harv) and without (Regrowth) wood harvest in regrowth forests
using historic CRU-NCEP climate, recycling the last 30 data years after
2015.
Simulation of European old-growth and regrowth forests with and
without wood harvest in regrowth forests using historic CRU-NCEP climate,
recycling the last 30 data years after 2015:
Simulations of broadleaf forests using automated thinning and
clear-cutting under RCP 4.5 and RCP 8.5 CO
Simulations of European forests using automated thinning and
clear-cutting in regrowth forests under RCP4.5 and RCP8.5 CO
LPJ-GUESS development is managed and the code maintained in a permanent repository at Lund University, Sweden. Source code is normally made available on request to research users. Conditions apply in the case of model versions still under active development. The model version presented in this paper is identified by the permanent revision number r9710 in the code repository. There is no DOI associated with the code.
Observational and modelled data used to create figures and tables are available at
ML and FL developed the forestry model code. ML and AR designed the simulations. ML performed the simulations and designed and performed the analyses. HP provided the GER-Bav site data. AF provided the SLO site data. ES contributed to the forest site data simulation setup and analysis. All authors contributed to the manuscript.
The authors declare that they have no conflict of interest.
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
This study was funded by the Swedish Research Council Formas through the ERA-Net SUMFOREST project Forests and extreme weather events: Solutions for risk resilient management in a changing climate (FOREXCLIM), the project Land Use, Carbon Sinks and Negative Emissions for Climate Targets of the German Federal Office for Agriculture and Food (BLE) through the FOREXCLIM project, and by the Slovenian Ministry of Agriculture, Forestry and Food (MKGP) through the FOREXCLIM project. The study also contributes to the Strategic Research Areas BECC and MERGE. Anja Rammig acknowledges funding from the Bavarian Ministry of Science and the Arts (BayKliF). The computations were enabled by resources provided by the Swedish National Infrastructure for Computing (SNIC) at LUNARC, Lund University, partially funded by the Swedish Research Council. We thank Gerhard Schütze, Martin Nickel, and Leonhard Steinacker for providing measurement data from Bavaria. Further we thank the Bayerische Staatsforsten (BaySF) for providing the observational plots and the Bavarian State Ministry of Food, Agriculture, and Forestry for permanent support of the projectW07 “Long-term experimental plots for forest growth and yield research”. We thank Ralf Nagel and the Nordwestdeutsche Forstliche Versuchsanstalt, Göttingen, for providing the measurement data from central Germany. We thank Thomas Pugh for helpful discussions.
This research has been supported by the Swedish Research Council Formas (grant nos. 2016-02110 and 2016-01201), the Swedish Research Council (grant no. 2019/3-592), the German Federal Office for Agriculture and Food (BLE) (grant no. 2816ERA01S), the Bavarian Ministry of Science and the Arts (BayKliF), the Bavarian State Ministry of Food, Agriculture, and Forestry (grant no. 7831-26625-2017), the Slovenian Ministry of Agriculture, Forestry and Food (MKGP) (grant no. 2330-17-000077) and the Slovenian Research Agency (research core funding grant no. P4-0059).
This paper was edited by Tomomichi Kato and reviewed by two anonymous referees.