Eur J Forest Res (2005) 124: 349–362 DOI 10.1007/s10342-005-0086-2
O R I GI N A L P A P E R
Thomas Ro¨tzer Æ Ru¨diger Grote Æ Hans Pretzsch
Effects of environmental changes on the vitality of forest stands
Received: 23 February 2005 / Accepted: 12 July 2005 / Published online: 8 November 2005 Ó Springer-Verlag 2005
Abstract Using the physiological single tree growth model BALANCE, vitality of forest stands was simulated in dependence of the site-related factors, climate and stand structure. At six level II plots in southern Germany with the main tree species beech (Fagus sylvatica L.), oak (Quercus robur L.), spruce (Picea abies [L.] Karst.), and pine (Pinus sylvestris L.), simulated results were compared to measured values (soil water content, bud burst and leaf colouring, diameter at breast height, tree height and crown density) in order to validate the model. Sensitivity tests were done to examine the influence and the interactions of the environmental parameters. The validation results show that BALANCE is capable of realistically simulating the growth and vitality of forest stands for central European regions for medium term time spans (several years). The validation of the water balance module produces mean absolute errors based on field capacity between 2.7 and 6.9% in dependence of sites and forest stands. Senescence of foliage as well as crown density is reproduced with a correlation coefficient of 0.70 compared to measurements. Differences between measured and simulated diameter values were smaller than 1% for spruce and smaller than 6.5% for beech after 7 years of simulation, and smaller than 1% for oak after 8 years of simulation. On the other hand, the simulations for pine trees conform less with the measurements (difference: 22.6% after 8 years). The sensitivity of the model on environmental changes and on combinations of these parameters could be demonstrated. The responses of the forest stands were quite different.
Communicated by Peter Biber T. Ro¨tzer (&) Æ R. Grote Æ H. Pretzsch Department of Ecosystem and Landscape Management, Chair of Forest Yield Science, Technische Universita¨t Mu¨nchen, Am Hochanger 13, D-85354 Freising, Germany E-mail:
[email protected] Tel.: +49-08161-714665 Fax: +49-08161-714721
Keywords Tree growth modelling Æ Vitality Æ Crown condition Æ Forest stand Æ Climate change Æ Level II
Introduction Currently, human activities increase the atmospheric carbon dioxide concentration by 1% each year. With CO2 concentrations reaching values of 650–700 ppm, which means a doubling within the next 100 years, climate will also change (IPCC 2000). The global average surface air temperature, for example, is expected to rise by 1.4–5.8°C (IPCC 2000). The responses of forests on the changes of the environmental parameters, CO2 concentration and climate are documented in numerous articles. Most of the results show a rise of photosynthesis and net primary production under higher CO2 concentrations and elevated temperatures (e.g. Lewis et al. 2001; Hamilton et al. 2002; Zheng et al. 2002). A further response of plants to the global warming is the extension of the growing season (e.g. Menzel and Fabian 1999; White et al. 1999). Chmielewski and Ro¨tzer (2001), for example, found a lengthening of the vegetation period of 5 days per temperature increase of 1°C. But there are still other environmental parameters that affect the vitality and growth of plants: nutrition, level of stress (i.e. immissions, diseases or parasite infections), stand structure or management impact. Competition between plants can also play a major role for growth and vitality (e.g. McDonald et al. 2002; Spinnler et al. 2002; Derner et al. 2003). Many direct effects of individual environmental influences on the growth of forests, such as defoliation by insects (Armour et al. 2003), plant nutrition and soil acidity (Demchik and Sharpe 2000) or ozone (Barbo et al. 2002) have already been described. Most of these environmental parameters are in turn influenced by a changing climate, by other environmental parameters or even by the plant or the forest stand itself. To examine these interactions and analyse
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the changes of the vitality of a plant, physiological growth models are suitable tools (e.g. Sinoquet and Le Roux 2000). The particular advantage of this type of model is that a number of different physiological processes are described separately in response to environmental parameters and that the interaction of these parameters leads to integrated results (e.g. tree growth or foliage biomass) that are not previously parameterised. Thus, also new combinations of environmental conditions can be investigated. Individual tree models, such as BALANCE can furthermore simulate growth responses on the single tree level. This also enables to assess the influence of competition, stand structure, species mixture, and management impacts because tree development is described as a response to individual environmental conditions which in turn change with individual tree development. However, the requirement of data of physiologically based growth models is generally high. Therefore, such data have been collected at more than 840 permanent level II monitoring sites all over Europe since the middle of the 1990 s (UNECE 1998). In the Bavarian programme for intensive monitoring of forest ecosystems, they have already been collected till the end of the 1980 s (Preuhsler 1993–2000). The frequencies of data measurements range from continuous (e.g. meteorology) to decadal (e.g. soil condition) recordings. At all 22 Bavarian level II plots, meteorological records and data of the stand structure and soil conditions are available. Further information that are required to parameterise and validate growth models are soil water and plant nutrition data, air quality and plant vitality data as well as data of soil condition, deposition and phenology. The main vitality parameters of trees, which are measured at the level II plots are diameter and height increment as well as crown condition. Using these data pool, the aim of this study is to show that the physiological growth model BALANCE is able to sufficiently reproduce growth and vitality parameters of level II plots and that the model reacts sensitive on environmental influences making causal analytical explanations possible. Therefore, measured values [soil water content, leaf fall, diameter at breast height (dbh), tree height and crown density (cd)] of six level II plots with the main tree species beech, oak, spruce and pine were compared to simulations in order to validate the physiological growth model BALANCE. Sensitivity tests were done to analyse the influences and interactions of environmental changes. Vitality parameters of forest stands were analysed in dependence of the site-related factors climate and stand structure.
Data Six German level II plots, which are all Bavarian forest climate stations (Preuhsler 1993–2000) are used to initialise the model and evaluate the simulations. A brief description of these sites is given in Table 1.
While the geographical extension is between 48 and 50°N and 11 and 13°E, the elevations of the plots range between 406 and 1,025 m. For the period 1992– 2003, records of 10–12 years were kept depending on site. At all plots, daily averages of measured temperature, precipitation, global radiation, humidity and wind speed are available. The lowest annual mean temperature of 5.5°C was recorded at Mitterfels that also shows the highest annual precipitation of 1,551 mm. The highest annual mean temperature of 8.5°C was measured at Altdorf, which is a comparatively dry site with a precipitation sum of 819 mm. The minimum precipitation amount of 688 mm was measured at Riedenburg. The mean daily global radiation sums, the mean daily humidity values and the mean daily wind speed data as well as the soil characteristics used for simulation are also presented in Table 1. As structural forest stand data for BALANCE tree position, initial tree and crown height as well as initial dbh are required. These data as well as crown radii at eight directions and tree age are measured at every level II plot. Table 1 shows the mean initial values of the stand structure parameters at six plots. The main tree species at the plot Altdorf is pine (Pinus sylvestris L.), at Ebersberg and Flossenbu¨rg it is spruce (Picea abies [L.] Karst.) and at Mitterfels it is beech (Fagus sylvatica L.). All of these stands are pure stands. Freising with beech (F. sylvatica L.) and Riedenburg with oak (Quercus robur L.) as main tree species are mixed stands that are both composed of beech and oak. The youngest stand with a mean tree age of 74 years is the spruce stand in Flossenbu¨rg, the oldest stand is the mixed stand in Freising with 140 years. All values listed previously are the initial structure and age parameters and the driving forces needed for modelling tree growth with BALANCE. To validate BALANCE, diverse data measured at each of the six plots were used. Daily measured soil water content data in different layers of the years 2000–2002 resp. 2003 were used to validate the water balance model. For the validation of the growth development measured dbh and tree height data of the years 1995 and 1999 as well as annually observed cd data for the period 1995–2000 were used. As because cd was observed in January in the year 1995, while in other years it was observed in the months of July to August, only the data of the years 1996–2000 are suitable for comparison. The average cd percentage of the years 1995–2000 for six plots is shown in Fig. 1. Spruce trees in Ebersberg and Flossenbu¨rg as well as beech trees in Mitterfels and oak trees in Riedenburg show an increase of the cd over the observed 6 years. Compared to the starting value, cd had not changed in Altdorf at the end of the observation period. Only in Freising cd percentage declined from 86.92% in 1996 to 82.65% in 2000. Furthermore, values of the autumn colouring of beech leaves were made available from the German Weather Service for the phenological stations Freising
351 Table 1 Description of six level II plots Altdorf 1992–2002 11
Period Years
Geography / (°N) 49.41 c (°E) 11.32 Z (m) 406 Climate tm (°C) 8.5 rr (mm) 819 2 rg (J/cm ;) 1,046 rh (%) 72 ws (m/s) 1.6 Soil parameters (used for simulation) fc (mm) 126 wp (mm) 61 it (cm) 22 d (cm) 110 Stand structure (initial values) Tree, species Pine, P. sylvestris n nmts age (years) dbh (cm) s.d.dbh h (m) s.d.h
256 256 90 20 4.6 17.9 1.7
Ebersberg 1993–2002 10
Flossenbg. 1994–2003 10
Freising 1994–2003 10
Mitterfels 1993–2002 10
Riedenburg 1992–2003 12
48.12 11.92 540
49.76 12.4 840
48.41 11.66 508
48.98 12.88 1,025
48.93 11.76 475
7.7 1,004 1,007 81 1.3
6 887 1,015 79 2.3
8.1 846 1,117 77 1.2
5.5 1,551 997 76 2.3
7.9 688 1,074 76 1.3
286 125 22 110
314 102 10 100
413 174 30 120
366 130 22 110
426 261 15 105
Spruce, P. Abies
Spruce, P. abies
Beech, F. sylvatica
Beech, F. sylvatica
Oak, Q. Robur
144 144 76 40.4 9.5 28.9 2.5
160 158 74 29.8 7 22.9 2.7
75 48 140 42.7 14.2 30.4 5.3
109 105 100 29.1 6.7 17.9 1.3
177 75 101 35.5 5.7 31.3 1.9
Abbreviations: / latitude, c longitude, Z altitude, tm temperature, rr precipitation, rg global radiation, rh humidity, ws windspeed, fc field capacity, wp wilting point, lt layer thickness, d simualtion depth, n number of trees, nmts number of trees of the main tree species, dbh diameter at breast height, h tree height, s.d. standard deviation
and Viechtach situated in the vicinity of the plots Freising and Riedenburg. This way, they can be used to validate the modelling of leaf senescence.
Model description The primary task of the physiological growth model BALANCE, which was developed within the scope of the research project SFB 607 ‘Growth and parasite deFig. 1 Development of the crown density (cd) percentage for the main tree species at six level II plots for the years 1995– 2000
fense’ (Grote and Pretzsch 2002), is to calculate the three-dimensional development of individual trees or forest stands and to estimate the consequences of environmental influences. Essential processes for the simulation of vitality and growth of beech, oak, spruce and pine are their individual carbon, water and nitrogen balances. Thereby, the biomass of an individual tree is calculated from the measured or estimated dimensional variables tree position, tree and crown base height, diameter, and crown radii.
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Dimensional tree growth is calculated once a year based on the biomass increase of the woody tissue that has been accumulated during the last year by each single tree. Since this increase in biomass is the result of the interaction of several physiological processes, which depend on the physical and chemical microenvironment that itself is simulated from stand structure, the detailed description of all relevant processes would require more space than available here. A general description, however, can be obtained in Grote and Pretzsch (2002), whereas in the following, only model aspects relevant for this study are described, and most of them with reference given to more detailed information.
lates crown radii length for each canopy layer with a species-specific crown shape function that depends on crown length (Grote 2003; Grote and Reiter 2004). All canopy layers are of equal height, selected by the model user. The volume between two radii in each layer is called a canopy segment. The same procedure with separately selected layer heights is used to determine the shape of the fine-root distribution profile (Fig. 2). Foliage biomass is calculated for each canopy segment based on specific leaf area, leaf area density and the fraction of foliated segment volume, which are all derived from a segment-specific competition factor (Grote and Reiter 2004). Microclimate and water balance
Structure and initial biomass In BALANCE, each tree is structured in crown and root layers, which are in turn divided in up to eight crownand root sectors (see Fig. 2). For each layer, each sector, microclimate and water balance are calculated from temperature, radiation, precipitation, humidity and wind speed measurements, respectively, from climate stations on the one hand and leaf area distribution on the other hand. While these calculations are computed daily, the physiological processes assimilation, respiration, nutrient uptake, growth, senescence, and allocation are calculated in monthly or decadal (=10-day) periods from the aggregated driving variables. Thus, weather conditions as well as CO2 concentration, soil condition, competition between individuals, and stress factors, as for example, air pollution and nutrition deficiency, can be integrated in modelling growth and vitality of trees. Based on the individual carbon balance, dimensional changes and mortality of a tree are computed at the last day of each year (Fig. 2). From the initialised tree data (coordinates, dbh, tree height, crown height, crown radii) BALANCE calcu-
BALANCE includes sophisticated approaches for the estimation of the weather conditions for each tree because physiological processes are driven by the individual environmental conditions of each tree, including radiation, temperature and water availability (Grote and Pretzsch 2002). Light conditions are calculated for each canopy segment from field climate conditions using a competition factor that is basically the sum of all crown volumes that fall into a competition cone, positioned above this segment. In addition to the description in Grote and Reiter (2004), all crown volumes are weighted by the foliage area they contain for the simulation of relative light availability. Soil temperature is determined applying the respective module of the SWAT model (Soil and Water Assessment Tool, Arnold et al. 1998) that uses a stand biomass-related damping function for the estimation of soil surface temperature. The temperature in a specific canopy layer of a particular tree crown is between field and soil temperature (tf and ts, respectively) and is weighted by the foliage area above this layer (Lcum) relative to the total foliage area (Ltot): tc ¼ tf Lcum =Ltot ðtf ts Þ
Fig. 2 Process scheme of the physiological tree growth model BALANCE
To estimate individual water balance, water demand and water availability are also determined tree specifically. Therefore, potential evaporation is calculated from the Penman–Monteith equation (Monteith 1965) with individual micrometeorological information. Transpiration demand is obtained by subtracting interception evaporation from potential evaporation. Water within the soil volume occupied by the tree fine roots is available for transpiration, considering a decreasing relative availability with decreasing soil water amount. This water content is increased by through-fall (= precipitation - interception) and horizontal water exchange (from soil fractions that are not occupied by fine roots) and is decreased by soil evaporation (Allen et al. 1989), percolation (water above field capacity of a soil layer flows into the next layer), and plant water uptake (see Grote et al. 2003).
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Carbon gain, allocation and loss The variant of the Farquhar-model suggested by Haxeltine and Prentice (1996) is applied to estimate photosynthesis in each canopy segment over months or 10-day periods throughout the year. Respiration is calculated after Thornley and Cannell (2000) and is divided into costs for uptake and transport processes, compartment-specific growth respiration, and residual respiration that is mainly required for maintenance purposes. Residual respiration depends on nitrogen content and temperature of each compartment (foliage, fine roots, buds and sapwood of branches, coarse roots and stem wood) and is taken directly from the biomass of the specific compartment. Fixed carbon that is not used for growth and uptake processes is allocated into the plant compartments according to their relative sink strength (Grote 1998). This sink strength is defined as the difference between the biomass of a particular compartment and the optimum biomass given by its relation to foliage biomass (in case of fine roots), or according to the trees dimension (in case of buds and woody tissue). The demand for the different woody compartments and for the buds is determined for each simulation period in a step by step procedure: firstly, the current ratio between stem-sapwood and all other living tissues is calculated. This ratio is used to allocate a fraction of the available carbon into stem wood. From the biomass increase a hypothetic tree height and dbh is calculated assuming there is no change in crown base height and height to diameter ratio (see below for more detailed descriptions) and wood density is constant. Secondly, based on the new stem dimension, an increase of every crown-segment (and root volume-) is calculated on the assumption of the same profile of relative growth rates that has been determined in the last annual period. From this increase in volume, the biomass requirements for branches (and coarse roots) and new foliage tissue are determined according to the principles outlined below. We emphasize that the dimensional changes are not realised at each time step but only used to determine allocation coefficients and biomass increase. The allocation according to the new foliage demand is used to build up the new bud compartment. Dimensional changes Similar to the calculations per time step, the new stem height and diameter are calculated from the new stem wood biomass, assuming the same wood density, crown height, height to diameter ratio, and ratio between tree diameter at the ground and at breast height. The total branch biomass increase is allocated to each crown segment using the relative growth rate profile of the last year. Finally, the new segment volume is calculated from the new branch biomass that supports this volume, considering the basic geometrical figures of each branch
by assuming no change in the length of the unfoliated branch fraction. After this dimensional growth calculations, which are consistent with the allocation procedure, new crown segment properties and changes in crown height and height to diameter ratio relationships are determined. The competition factor of each segment has to be recalculated after the changes in tree dimension to define the new foliage properties, such as specific leaf area in each segment (see Grote and Reiter 2004). Finally, the new height to diameter ratio that determines dimensional tree growth as well as the relative growth rate of each crown segment that determines crown shape by the allocation procedure during the following year have to be defined. In principle, both depend on the social state of a tree within the stand and can change considerably during stand development (e.g. Zeide and VanderSchaaf 2001). However, since the presented simulations cover only relatively short growth periods relative to the development of forests, it is not changed here. Foliage growth and mortality To depict the relationships between the environmental influences and different vitality parameters, as for example cd, the annual cycle of foliage development must be known; with the beginning of bud burst, foliage biomass and leaf area as well as light availability and radiation absorption change. Thus, the date of foliage emergence in a tree not only determines its assimilation and respiration rate but also affects competition by changing the environmental conditions within the tree’s vicinity. In BALANCE, the beginning of bud burst is modelled by using a temperature sum model (Ro¨tzer et al. 2004b). This procedure determines a particular day for each tree species at which the new foliage and environmental properties are updated for all trees. Thus, the model accounts also for the better growth conditions of evergreens or early flushing species in spring compared with late flushing species. Foliage senescence is estimated in dependence of the respiration sum. The respective module is based on considerations of Kikuzawa (1995), who suggested an equation in which the optimal life span of a leaf is defined by its construction costs and by its cumulated net assimilation. Also Reich et al. (1999) and Reich (2001) found close relationships between the life span and the respiration of organisms independent of whether they are animals or plants. In BALANCE, it is assumed that without external influences, the life span of an organism, in our case a leaf, is equivalent to its potential life span, for which—according to Reich (2001)—a potential respiration sum can be defined. As life span is dependent on the region (i.e. climate), the potential respiration sum is also influenced by region. As the photosynthetic activity declines with increasing leaf age (Ellenberg et al. 1986; Mohren and Bartelink 1990; Zhang et al. 1994; Oleksyn
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et al. 1997), leaf age has to be considered by calculating photosynthesis, too. So, if gross assimilation is constant, respiration increases and net assimilation decreases with increasing leaf age. This means that leaves are shed when cumulated daily respiration of a specific leaf age class has reached a parameterised potential respiration sum. In principle, the impact of various stresses is considered by this procedure because they increase respiration and thus the potential respiration sum is attained earlier. Based on these calculations, a day is defined when the last leaf age class (for deciduous trees, these are the current year leaves) of the whole tree is shed. At this day, the light competition for this tree and all trees in the vicinity is again recalculated. Leaf density ld (m2 leaf area per cubic metre total crown volume) and specific leaf area sla (m2 leaf area per kilogramme) which are both calculated in BALANCE, are used for comparison with the cd percentage that is observed at the investigation plots. These variables were standardised by taking the simulated and observed values of 1996 as a 100% base. Furthermore, tree height and diameter are used as further indicators for the vitality of a tree because they represent the integrated response to environmental conditions.
Simulation set-up The initial data tree height, crown length and crown radii, necessary to start the model, were measured or observed at all level II plots. To avoid the impact of artificially produced favourable growing conditions at the boundary of model stands, all initialised trees are simulated but only trees inside the stand, which were measured and observed over the entire period were used for the evaluation. The negative feedback to central trees that is due to the better growing conditions of boundary trees is assumed to be negligible. At the beginning of the model runs, soil and nutrient status of the trees were set at their optimum and no nitrogen deposition was assumed. Tree death has not been simulated but defined by the user according to the individual tree information available from the investigated plots. All simulations are carried out using decadal time steps for physiological processes.
Results Water balance Since microclimate forms the boundary condition for all other processes, this has to be the first module to be assessed. Unfortunately, data for radiation, vapour pressure and wind profiles were not available. As such, we concentrated on the model performance with respect to temperature profiles and the development of the water availability. In Fig. 3, the vertical distribution of the
temperature on a sunny summer day for three individual trees in a forest stand and the annual course of the temperature in different layers in a beech stand of a single year are shown. As a consequence of the variation of precipitation, radiation, temperature, humidity and wind speed, the daily values of the water balance parameters can also change to a great extent. The annual cycle of potential and actual evapotranspiration, precipitation, run off and soil water content for the beech stand at the level II plot Freising—calculated with BALANCE—is illustrated in Fig. 4 for the year 2003. In this dry and hot year, when the potential evapotranspiration reached daily values of up to 11 mm and when only little precipitation fell during the summer months, soil water content shows a drastic reduction. Depending on the soil water content, the daily actual evapotranspiration of the beech stand ranged from values of approximately 0 mm up to maximum values of 9.5 mm. In order to validate the water balance module, measured soil water content data of the years 2000–2002 resp. 2003 at all investigation sites were used. In Fig. 5, the simulated and measured values of the soil water content summed up over all soil layers are compared for a beech stand at the level II plot Freising and for a spruce stand at the level II plot Flossenbu¨rg. At the beech stand Freising soil water content (swc) declined from field capacity with 412 mm in the winter months to a minimum of approximately 300 mm in the summer months. These values in late summer were achieved both from the simulated and the measured swc in the years 2000, 2001 and 2002. For the dry year 2003 the decrease of the swc was much greater. The minimum of the simulated swc was clearly lower than the measured swc in this year indicating an overestimation of swc decline. At the spruce stand Flossenbu¨rg, on the other side, the course of the soil water content corresponds well to the measured values of the years 2000 and 2001. In the year 2002, the values of the measured as well as the simulated soil water contents show only slight fluctuations. The validation of the water balance, summarised over the six level II plots, is shown in Table 2. While the mean absolute errors (mae) of the swc with approximately 15.5 mm were similar at both beech stands in Freising and Mitterfels, the values of the spruce stands differ clearly with 7.6 mm in Ebersberg and 12.8 mm in Flossenbu¨rg. The oak stand in Riedenburg has a mae of 13.3 mm, the pine stand in Altdorf a value of 8.8 mm. When the mae values are calculated based on the field capacity (fc) of the soil at the respective forest stand, the best result is obtained for Ebersberg with 2.7% and for Riedenburg with 3.1% deviation based on fc. The highest deviation in percentage from fc with 6.9% was achieved for the pine stand in Altdorf. The differences of the percentage errors for the level II plots Freising, Flossenbu¨rg and Mitterfels with values between 3.8 and 4.3 are very small.
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Fig. 3 Vertical distribution of the temperature in a beech stand on a sunny summer day for three individual trees (left) and annual course of the temperature in different layers of a single year (right)
Fig. 4 Annual cycle of the water balance parameters potential and actual evapotranspiration, precipitation, run off and the soil water content for a beech stand (level II plot Freising) for the year 2003
Table 2 Validation results of the water balance module in BALANCE at six level II plots Site
Tree species
n
Mae (mm)
% fc
Altdorf Ebersberg Flossenbu¨rg Freising Mitterfels Riedenburg
Pine Spruce Spruce Beech Beech Oak
437 557 550 1271 557 526
8.8 7.6 12.8 15.5 15.6 13.3
6.9 2.7 4.1 3.8 4.3 3.1
Abbreviations: n number of comparisons, mae mean absolute error, % fc error based on field capacity
Vitality and growth An important growth parameter often used for the estimation of the productivity of forests is the dbh. This parameter was measured at each of the six level II plots in the years 1995 and 1999. Table 3 shows the measured as well as the simulated mean dbh values of six level II plots of these 2 years and the differences between the measured and simulated values as percentages of the measured dbh. The best results of the dbh were achieved for the plots Ebersberg, Flossenbu¨rg, Freising and Riedenburg, all
356 Fig. 5 Measured and simulated daily values of the soil water content swc for the beech stand at the level II plot Freising (soil depth 120 cm) for the period 2000–2003 (top figure) and for a spruce stand at the level II plot Flossenbu¨rg (soil depth 100 cm) for the period 2000–2002 (lower figure)
with differences smaller than or equal to 0.3% for the year 1995 and 0.9% for the year 1999. The difference of the dbh values for the beech stand at Mitterfels was 2.5% for the year 1995 and 6.1% for the year 1999. The greatest differences between the simulated and the measured dbh values were found for the pine stand at Altdorf with deviations of 12% after 4 years of modelling and of 22.6% after 8 years of modelling. Figure 6 compares the simulated and the measured dbh increments of all measured individual trees for the plots Ebersberg and Mitterfels in the year 1999, i.e. after 7 years of simulation. Compared to the good results for the mean values (Table 3), the measured and simulated dbh increments of the single trees show larger differences. For the spruce stand at the site Ebersberg the coefficient of correlation r was calculated with 0.49, for the beech stand at the site Mitterfels r was 0.35. Another growth parameter, which is measured at every level II plot, is the tree height. Because a part of the single tree height measurements shows a decline of the height growth over the examined period, the number of trees for the validation of height growth is reduced, although the measuring error might be considered onesided this way.
In Table 3, measured and simulated mean tree heights of the six level II plots are presented. The greatest differences between simulated and measured values were found again at Altdorf with 8.5% in 1995 and 14.8% in 1999. For the year 1995 as well as for the year 1999, tree height differences at the rest of the sites were smaller than 3%. Best results were obtained for the sites Ebersberg, Flossenbu¨rg, and Freising with a difference smaller than 2% in 1999. In order to check the plausibility of the leaf senescence and mortality simulation, these values of two beech stands in Freising and Mitterfels were compared to the values of the leaf colouring of beech observed at the phenology stations Freising and Viechtach. For the plot Mitterfels, Fig. 7 presents the results of the years 1993– 2001 (10-day periods), for which the mortality of the leaves are calculated, and the day of the year of the observed phenological phase ‘leaf colouring of beech’, which means per definition that 50% of the leaves are coloured. It can be seen from Fig. 7 that the course of the leaf colouring is similar to the course of the mortality of the leaves for the beech stand in Mitterfels. In some years (1993, 1995, 1998, 1999, 2000), leaf colouring of beech as observed, lies exactly within the range of the decade when the mortality of the leaves was simulated.
357 Table 3 Mean values, standard deviations (s.d.) and % differences (% dif) of measured and simulated diameter at breast height and tree height of the level II plots for the years 1995 and 1999 Altdorf Diameter at breast height (dbh) (cm) N 30 Measured (1995) 24.9 s.d.mes 4.4 Simulated (1995) 27.9 4 s.d.sim % diff (1995) 12 Measured (1999) 25.6 4.1 s.d.mes Simulated (1999) 31.3 4.9 s.d.sim % diff (1999) 22.6 Tree height (m) N 31 Measured (1995) 20 s.d.mes 1.5 Simulated (1995) 21.7 1.6 s.d.sim % diff (1995) 8.5 Measured (1999) 20.9 1.5 s.d.mes Simulated (1999) 24 1.8 s.d.sim % diff (1999) 14.8
Ebersberg
Flossenbg
Freising
Mitterfels
Riedenburg
30 44.6 8 44.7 7.9 0.2 45.9 8.2 45.5 8.1 0.9
26 34 5 34 4.9 0 35.6 5.4 35.6 5.4 0
14 50.4 8 50.5 8 0.2 53 8.2 53.1 8.5 0.2
35 28.2 6.3 28.9 6.5 2.5 29.5 6.6 31.3 7 6.1
27 39.1 4.9 39 4.9 0.3 40.3 5 40.5 5.1 0.5
32 31.2 1.9 30.3 1.8 2.9 31.3 1.9 30.8 2.1 1.6
13 25 2.2 24.8 2.1 0.8 26.2 2.7 25.7 2.2 1.9
3 33.6 2.9 32.9 1.9 2.1 35 2.9 34.5 2.1 1.4
27 18.8 1.4 18.6 1.2 1.1 19.4 1.4 19.8 1.6 2.1
13 32 0.6 31.7 1 0.9 33.2 1 32.5 1.2 2.4
Abbreviation: n number of individual trees
Fig. 6 Measured and simulated dbh increment of individual trees at the level II plots Ebersberg (left, spruce stand) and Mitterfels (right, beech stand) after 7 years of simulation
If the means of the simulated leaf mortality and the phenological phase ‘leaf colouring of beech’ for the level II sites Freising and Mitterfels are calculated over the period 1994–2001 resp. 1993–2001, the differences of simulated and observed values are small. In Freising, while the simulated date is 18 October, leaf colouring was observed on 12 October. At the site Mitterfels, on the other hand, the simulated date is 26 September, and the observed date 25 September. The coefficient of correlation between the simulated leaf mortality dates and the observed leaf colouring dates for beech is 0.78. The course of the simulated and the observed cd for the years 1996–2002 is illustrated in Fig. 8 for the level II
plot Freising. Simulated values were standardized on the leaf density and the specific leaf area. For values based on the ld and the sla as well as for observed data, a slight decline in the cd percentage over the years 1996–2002 can be seen. The development of the observed values, however, is reproduced better by the simulated leaf density. If the results of all six examined plots are compared, cd values estimated by using sla show the best accordance to the observations (Fig. 9). While for observed and simulated crown densities based on ld a coefficient of correlation of 0.60 could be found over all single year values, simulated values based
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Fig. 7 Simulated mortality of leaves at the beech stand Mitterfels (10-day period, median of all tree individuals) and day of the year of leaf colouring for beech observed in the region of Viechtach, approximately 25 km away from Mitterfels
Fig. 8 Simulated and observed crown density (cd) at the level II site Freising; simulated crown density is based on the leaf density (ld) resp. on the specific leaf area (sla)
on sla show a relationship with a coefficient of correlation of 0.70. If only beech trees are considered, the coefficient of correlation rises to 0.88. For the cd of spruce trees, a coefficient of correlation for simulated and observed values of 0.94 was calculated. Fig. 9 Simulated and observed crown density percentages of six level II plots (open circle, beech; open square, spruce; closed triangle, oak; cross, pine); simulated values based on leaf density (ld) (left) and on specific leaf area (sla) (right)
In order to check the sensitivity of BALANCE on environmental changes, several scenarios were composed. In the first step, climate was changed. According to the IPCC (2000) temperature was increased by 3°C and radiation by 10% over the entire periods of the sites. Precipitation, on the other side, was decreased by 20% (= scenario 1). For scenario 2, additionally, a doubling of the CO2 concentration was assumed from the beginning of the periods. All scenarios were carried out for the beech stand at the plot Freising. Figure 10 shows the course of the dbh and the sla over 10 years for the control run (1994–2003) and for two scenarios. If temperature and radiation rise while precipitation is decreasing (scenario 1), the growth of trees expressed as dbh increment decreases. After 10 years, dbh in scenario 1 is clearly smaller than the dbh of the control run. But when CO2 concentration is increased as well, both factors cause an increase of the dbh compared to the control run. The decline of the sla observed for the control run can also be seen in scenario 1 and 2, whereas the drop of both scenarios is not so steep. Compared to the steady course of the dbh, the course of the sla for all the three runs is unsteady. After 10 years, the lowest value was calculated for the control run and the highest value for scenario 2. When different environmental changes are combined, the interactions and the direct effects on the vitality and growth of a forest stand can be studied. Here, the environmental parameters species mixture and stand density were combined with the present climate (control run) as well as with scenario 2. To estimate the impact of the competition between the trees, the number of trees at the level II plot Freising was reduced by approximately 50%, which means less competition for light, water and nutrients. A further sensitivity run was done by a partial substitution of the beech trees with spruce trees to show the influence of tree species. All trees that have been excluded in the former sensitivity run were inserted again as spruce trees. In Fig. 11, changes of the dbh increment and of the sla after 10 years are shown for stand density and tree
359 Fig. 10 Development of diameter at breast height (dbh) (left) and specific leaf area (sla) (right) over 10 years at the level II site Freising (beech) for the control run, for scenario 1 (temperature +3°C, radiation +10%, precipitation 20%) and for scenario 2 (=scenario 1 and doubled CO2 concentration)
species impacts, all simulated for the present climate and for scenario 2. To see the influence of the single environmental changes, the control run resp. scenario 2 without a change was set as 100%. When beech trees are partly exchanged by spruce trees, dbh increment increases by 4.7% for the present climate, but decreases by 5.7% in scenario 2, both after 10 years of simulation. If stand density is reduced, i.e. less trees per plot, dbh—as expected—rises by 18% in the control run resp. 27% in scenario 2. Also, the values of sla increase for the control run (13%) and the scenario 2 run (32%), if stand densitiy is reduced (Fig. 11, right). A partial exchange of the tree species from beech to spruce produces a drastic decline of the sla values of 29 and 43% for the control run and for scenario 2, respectively.
Discussion Model validation It has been demonstrated that the model BALANCE is able to describe sufficiently the vitality and growth of Fig. 11 Changes in percentage of diameter of breast height (dbh) (left) and specific leaf area (sla) (right) increment based on environmental changes (stand density and tree species) for the control and scenario 2 runs at the level II site Freising (beech) after 10 years
forest stands at different sites in southern Germany. Starting with the estimation of the daily water balance, which is the base for all physiological processes, the validation of simulated vs measured soil water content data produced good results with mae values based on the field capacity from 2.7 to 6.9%. With the water balance model HyMo, developed for the calculation of hydrological parameters for crops, forests and other land covers (Ro¨tzer et al. 1997), similar results with mae values of 2.8 and 4% for a spruce resp. a beech stand in southern Germany were achieved (Ro¨tzer et al. 2004a). The validation Schultze (personal communication) made for several level II plots in Bavaria with a modified BROOK90 water balance model (Federer 1995) show in nearly all cases the same courses as the computed swc done within this study. Wegehenkel and Jochheim (2003) found RMSE values of two different water balance models between 3.1 and 5.5 vol% for six level II plots in northern Germany. Calculating the RMSE of each plot and transforming the results into volume percentages, the validation results (0.9–2.3 vol%) are smaller than those of Wegehenkel and Jochheim (2003). The results of Gusev and Nasova (2003) for a boreal spruce forest in Russia with RMSE values of 11, 25 and 35 mm corre-
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spond well to the validation results of the spruce stands Ebersberg and Flossenbu¨rg with 10 and 15 mm. Whereas the rates in the summer months are reproduced very well, the soil water content in autumn is often overestimated (Fig. 5). At the site Freising, for example, mae of the period May–August is 15.7 mm, whereas for the period September–October it is 28.1 mm. As this can be seen in most of the six level II plots, the simulated refilling of the soil water content in this period seems to be systematically too fast. Furthermore, BALANCE does not allow swc values above field capacity. However, the measured swc values can exceed field capacity, particularly in the winter month (Fig. 5), which in turn degrades the validation results, but is of less relevance for the simulation of plant growth. When the dbh and tree height values attained in this study are compared to the corresponding values of the yield tables (Guttenberg 1897; Schober 1975), measurements and simulations conform well. An exception is the simulation at the site Altdorf, which means that the calibration of the site conditions or of the species pine is not yet satisfactory. The adjustment for spruce and oak and in particular for beech can be used to simulate vitality and growth for forest stands in central Europe (see also Grote and Pretzsch 2002). The results for the growth simulations of single trees, however, show larger differences from the measurements (Fig. 6). The reason could be that the initialisation, the estimation of the driving forces and/or the growth algorythms (e.g. competition index) for the growth calculation of the single trees, are insufficient. A fundamental precondition for an exact calculation of the crown condition is the correct simulation of the beginning of leafing resp. may shoot and leaf fall. The simulation of the bud burst of the leaves resp. needles in BALANCE based on a temperature sum model is already documented in Ro¨tzer et al. (2004b). The leaf senescence model was validated on the phenological phases of the leaf fall of beech (Fig. 7). In numerous cases the date of the leaf colouring is equivalent to the decade, for which BALANCE simulated the mortality of the leaves. The means over the period 1993/1994–2001 show that the vegetation period ends clearly earlier at the site Mitterfels (1025 m altitude), compared to the site Freising (508 m altitude). Calculating the length of the vegetation period from the beginning of leafing and the mortality of the leaves, the means of the two sites are 162 days for Freising and 131 days for Mitterfels. The length of the vegetation periods Ro¨tzer and Chmielewski (2001) published in their phenological maps of Europe are 190–195 days for the region of Freising and 165– 170 days for the region of Mitterfels. Taking into account that the time periods (1994–2003 vs 1961–1998) as well as the plant species used for the calculation of the length of the vegetation period are not the same, the difference between the two sites with 31 (this study) resp. 25 days (Ro¨tzer and Chmielewski 2001) is small. With these plausible calculations of the vegetation period, cd estimation improves as well. A further refinement of the
model might be to adjust the potential respiration of the tree species to the respective region. Additionally, the simulation results of spruce, pine and oak have to be compared to the observed data. Causal analysis Vitality in our context means the property of being able to survive and grow, expressed by parameters like crown condition, dbh or height increment. As can be seen in the sensitivity studies, estimated vitality parameters react differently on environmental changes. Model simulations can help to understand and explain the reactions and interactions. The simulation results are plausible and conform with the results found in literature. For example, Ma¨kinen et al. (2003) found close relationships between different climate variables and the radial increment of P. abies (L.) depending on site, Hamilton et al. (2002) showed that an increase of the CO2-concentration increases the growth rate of pine and Pretzsch (2002) gave an account of species-specific growth trends in pure and mixed spruce/beech stands. The analyses of the effects of single factors are simple in most cases, whereas multiple factor influences are complex and often produce quite different results (e.g. the effects on dbh when tree species were exchanged, the climate is changed and the CO2 concentration is doubled). These findings were supported by the studies of Barbo et al. (2002) at a pine stand under different ozone, CO2 and competition rates. In some cases, cd values differ from the observed values (Figs. 8, 9). The differences could in parts be due to the subjectivity and deficiencies when observing crown condition (Mayer 1999). Regarding these aspects and excluding the not yet satisfying calibration of pine, the results achieved here can be considered as good. However, other components of the biomass simulation, as for example the leaf density of the green volume have to be checked, as to whether they can improve the estimation of the crown condition. It is remarkable that for the site Freising leaf density on base of total crown volume is the best parameter for the estimation of cd, while for all other sites the specific leaf area reproduces cd best. The reason for this might be the estimation of the total crown volume of an individual tree, which must be known for the calculation of ld. The estimation of the total crown volume, however, includes uncertainties. Dbh growth and sla, which—in this study—is an indicator for cd and can be transformed in crown condition values, are not consistently influenced by external changes at the site Freising (Fig. 11). This corresponds with the investigations of Pretzsch (1996) who found no uniform correlations between radial growth and crown condition surveys. The sensitivity simulations are the first approach for a causal analysis of the influence of environmental changes on the vitality of forest stands. Next step will be to run simulations for other level II plots and other tree species for a better understanding of the effects, the reactions and feedbacks of environmental influences.
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Since BALANCE can simulate vitality and growth of forest stands sufficiently and the sensitivity of environmental changes on vitality has been demonstrated, it is possible to model growth and vitality also for level I sites. If initial values and driving forces can be transferred to level I sites, the observed cd could be useful to calibrate and validate the simulations.
Conclusion This study shows that BALANCE is capable of realistically simulating the vitality of forest stands on the base of single tree modelling. Nevertheless, some modules have to be improved; for example, the nutrient cycle module or the water balance module (i.e. the overestimation of the soil water content in the autumn months). The description of the Geman level II sites reveal contrasting nutritional supply values of the six level II plots (BMELF 1997). Altdorf, for example, for which the growth was simulated unsufficiently, is a site with very poor nutritional supply. Simulating the actual nutrient conditions will certainly be a further refinement of the growth simulations. In the growth modules, the estimation of the wood density, which has a strong influence on stem growth, the height:diameter ratio, the competition factor, and the biomass estimation have to be examined more specifically and—if possible,—must be validated. New processes such as a fructification module would further improve the growth simulation. A base for this module is the studies of Paar et al. (2000), Gruber (2002), Gruber (2003) and Dohrenbusch et al. (2002). As shown for six level II plots BALANCE can reproduce well the vitality and growth for spruce, beech and oak trees, while the simulations for pine trees conform less with the measurements. Since the simulation was done for one site only, it must be scrutinised as to whether site-specific conditions and/or tree species caused the differences. Although the general simulation results are in accordance with the measurements, growth simulations of single trees need refinements. To simulate realistically growth and vitality of different forest stands, the individual environmental conditions of each tree as well as the reactions of the single tree and the feedback of the environment have to be known. These kinds of simulations can only be done by single tree models as, for example, BALANCE. We see that BALANCE is a powerful tool for the simulation of vitality and growth on central European level II sites. By validating further German and European sites, the area of application can be extended. As cd is also estimated in a—initially—sufficient way, a next step will be to apply BALANCE for level I plots. However, at level I plots besides cd only rare information is available. Therefore, initial data (e.g. stand structure) and the driving forces (e.g. the climate) for a plot have to be estimated first. While the stand structure can be
composed using the structure generator of the forest management model SILVA (Pretzsch 1997), climate data for the plot can be calculated from nearby situated climate stations by using the method of Ro¨tzer (2000) for the spatial transformation of meteorological data. The sensitivity of the model on environmental changes and on combinations of these parameters could be demonstrated. The response of the forest stands has been quite different. To exactly analyse the multiple influences and the interactions and feedbacks, model refinements and more simulation runs at other forest stands have to be done. But already at the present status BALANCE can be used for estimating the impacts of environmental changes (e.g. climate change, stress influences, etc.) and for analysing the development of forest stands, as was shown in this study. Acknowledgements The investigation was funded by German Federal Agency for Agriculture and Food (BLE-00HS041). The basic model BALANCE was developed in the framework of the special research program SFB 607 ‘growth and parasite defence’. The authors are indebted to C. Pitkanen for the contribution in data processing and analysis and T. Seifert for his critical review of the article. The authors thank Bavarian State Institute of Forestry (LWF) for providing the data of the Bavarian level II plots, especially W. Grimmeisen for the soil moisture data and B. Schultze for the actual climate data. Thanks also to German Weather Service (DWD) for providing phenological data.
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