SEARCH

SEARCH BY CITATION

Keywords:

  • foliage distribution;
  • leaf area index;
  • monoterpene emission;
  • Quercus ilex;
  • scaling;
  • stand density

Summary

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and Methods
  5. Results and Discussion
  6. Acknowledgements
  7. References
  • • 
    This paper investigates the dependence of monoterpene emissions at the canopy scale on total leaf area and leaf distribution. Simulations were carried out for a range of hypothetical but realistic forest canopies of the evergreen Quercus ilex (holm oak).
  • • 
    Two emission models were applied that either did (SIM-BIM2) or did not (G93) account for cumulative responses to temperature and light. Both were embedded into a canopy model that considered spatial and temporal variations of foliage properties. This canopy model was coupled to a canopy climate model (CANOAK) to determine the micrometeorological conditions at the leaf scale.
  • • 
    Structural properties considerably impacted monoterpene emission. The sensitivities to changes in total leaf area and to leaf area distribution were found to be of similar magnitude. The two different models performed similarly on a whole-year basis but showed clear differences during certain episodes.
  • • 
    The analysis showed that structural indices have to be carefully evaluated for proper scaling of emission from leaves to canopy. Further research is encouraged on seasonal dynamics of emission potentials.

Introduction

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and Methods
  5. Results and Discussion
  6. Acknowledgements
  7. References

The emission of biogenic volatile organic compounds (BVOC) plays an important role in air chemistry processes and affects ozone concentrations in the vicinity of emission sources (e.g. Solmon et al., 2004). Thus it is important to estimate its fluxes, particularly as BVOC emission depends strongly on temperature and can be expected to increase under climate warming. While the problem is of great political concern at the regional scale, response functions between climate and emission are almost exclusively developed at the leaf scale. This mismatch between scales originates from the fact that, at the leaf scale, measurements can be easily applied. Only relatively recently have methodologies such as Eddy-covariance techniques been developed that allow quantitative measurements of BVOC emission using micrometeorological methods (Ciccioli et al., 2003; Spirig et al., 2005). These are undoubtedly valuable for model validation but their application is limited to suitable sites with specific conditions. Thus, scaling procedures are needed to extrapolate from short-term responses at the leaf level to long-term changes visible at the stand or ecosystem level. These results can then be used as input for statistical procedures and/or mechanistic air chemistry models for further extrapolations. Such a scaling procedure involves application of a model to describe leaf-scale processes that depend on meteorological conditions, the determination of these meteorological conditions, and the determination of biomass and foliage distribution throughout the canopy (Lamb et al., 1993; Lenz et al., 1997; Baldocchi et al., 1999; Schaab et al., 2003).

Currently, stand-scale BVOC emissions are most commonly estimated using the model of Guenther et al. (1993, 1995). This response function is based on temperature and radiation. Other influences are occasionally considered using empirical correction factors (e.g. Geron et al., 2000; Guenther et al., 2006). Vegetation structure is generally accounted for by using a simple canopy stratification model that assumes empirically determined foliage distribution across a number of canopy layers (Lamb et al., 1993). This approach is probably the only one currently feasible for application to large regions. However, the inflexible stratification of foliage into layers has been highlighted as a major source of uncertainty in BVOC emission simulation. In comparison, the improvement that can be expected by increasing the complexity of the microclimate model is probably small (Caldwell et al., 1986; Lamb et al., 1996). Various model analyses (e.g. Larsen & Kershaw, 1996; Huber et al., 1999) have confirmed that the relative lack of knowledge of the spatial distribution of foliage increases the uncertainty in emission simulations, although this conclusion has been questioned by Geron et al. (1997). It therefore seems likely that the simulation of emission would benefit from more detailed analysis of canopy structure impacts. Such investigations are important if, for example, emission estimates are obtained for changes in environmental conditions that may have an impact on canopy structure. The resulting inconsistency is likely to increase with increasing environmental diversity because species-specific responses have to be considered (e.g. Penuelas & Llusia, 2001; Niinemets et al., 2002). In addition to these limitations on long-term applications, precedent weather events (Kuhn et al., 2004) or temperature oscillations (e.g. Ciccioli et al., 1997) that are not considered in the Guenther approach have been found to be important for emission releases.

Alternatively, process-based models have been developed (e.g. Niinemets, 1999; Martin et al., 2000; Zimmer et al., 2000) that describe emission on the leaf scale in relation to tissue temperature and additionally account for the available carbon and energy resources. However, only a few attempts at scaling emission in a suitable way to use these models at the canopy level can be found in the literature (Baldocchi et al., 1999; Harley et al., 2004). These studies show that the distributions of environmental conditions as well as foliage properties are important for scaling emission from the leaf to the canopy. Additionally, as the impact of climate is not necessarily immediate, different physiological adjustments to seasonal developments in different parts of the canopy have also to be considered. This problem has been approached by Lehning et al. (2001), who proposed an algorithm (the seasonal isoprenoid synthase model (SIM)) that dynamically describes the seasonal development of isoprenoid synthase. The model has been combined with the biochemical isoprenoid emission model (BIM) (Zimmer et al., 2000) and has been successfully evaluated at the leaf scale for different tree species (Zimmer et al., 2003; Grote et al., 2006). As a further step towards a mechanistic emission model that simulates stand-level volatile isoprenoid production in relation to instantaneous as well as integrated environmental conditions, an improved version of SIM-BIM (SIM-BIM2; Grote et al., 2006) has now been integrated in a canopy model. This coupled model has been applied here to investigate the sensitivity of BVOC emissions to the structural features of the canopy.

Materials and Methods

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and Methods
  5. Results and Discussion
  6. Acknowledgements
  7. References

Model description

The SIM-BIM2 model (Grote et al., 2006) calculates changes in the concentrations of a number of isoprene and monoterpene precursors within the chloroplast using a sequence of first-order Michaelis–Menten equations. Assuming the absence of specific storage structures, the production rate of isoprene and monoterpene is equal to the emission of these substances. The enzyme activities of isoprenoids that determine the production rates develop dynamically in relation to temperature and radiation at the leaf surface (Fig. 1).

image

Figure 1. Model overview: biochemical processes considered in biochemical isoprenoid emission model 2 (BIM2) together with links to photosynthesis and seasonal dynamics (phenology and seasonal isoprenoid synthase model (SIM) of enzyme activity). Dashed arrows, impacts; solid arrows, matter transport.

Download figure to PowerPoint

The enzyme-activity model is combined with a phenological model that calculates the date of bud-burst from the cumulative sum of daily air temperature starting from the beginning of the year. From the day of bud-burst, foliage development is described by an empirical function. The same function is used to simulate litter fall starting from an empirically defined date. The primary substrates for the emission model are pyruvate and NADPH. These are provided by the photosynthesis model (Farquhar et al., 1980, with implementation from Martin et al., 2000; see Table 1 for parameter values). Pyruvate is then divided empirically into phosphoglycerate and triose-phosphate. This implementation results in a positive but nonlinear correlation between emitted carbon and photosynthesis. The proportion of carbon used for emission increases particularly rapidly if temperature increases beyond the optimum for carbon assimilation up to the optimum temperatures of the kinetic reactions involved in isoprenoid production (Zimmer et al., 2000; Grote et al., 2006). Stomatal conductance has been estimated using a vapour pressure sensitive Jarvis-type model approach (Jarvis, 1976) that has been parametrized with values from the ECOCRAFT database (Medlyn & Jarvis, 1999; Table 1).

Table 1.  Parameters used in the simulations
Description (and abbreviation if used in the text)ValueReference
  1. RubP, Ribulose biphosphate.

Phenology model
Foliage age when litter fall starts (days) (DL0)  310 Navas et al. (2003)
Foliage age when half of the age class is gone (days) (DL05)  713 Navas et al. (2003)
Duration of flushing (days)   42 Navas et al. (2003)
Temperature sum necessary for flushing (Tc)  500 Grote et al. (2006)
Photosynthesis model
Light-saturated rate of electron transport at 25°C (µmol m−2 s−1)55100 Niinemets et al. (2002) (Quercus coccifera)
Maximum RubP-saturated rate of carboxylation at 25°C for sun leaves (µmol m−2 s−1)  100Estimated after Niinemets et al. (2002)
Maximum electron transport rate at 25°C92000Estimated after Medlyn et al. (2001)
Curvature parameter    0.95 Martin et al. (2000)
Michaelis–Menten constant for CO2 at 25°C (µmol mol−1)  460 Martin et al. (2000)
Michaelis–Menten constant for O2 at 25°C (mmol mol−1)  330 Martin et al. (2000)
Rate of dark respiration at 25°C (µmol m−2 s−1)    1.1 Martin et al. (2000)
Relation between RubP-saturated rate of oxygenation and carboxylation    0.21 Martin et al. (2000)
Activation energy for Michaelis–Menten constant for CO2 (J mol−1)65800 Martin et al. (2000)
Activation energy for Michaelis-Menten constant for O2 (J mol−1) 1400 Martin et al. (2000)
Activation energy for dark respiration (J mol−1)66405 Martin et al. (2000)
Activation energy for electron transport (J mol−1)28000 Martin et al. (2000)
Activation energy for RubP oxygenation (J mol−1)37530 Martin et al. (2000)
Stomatal conductivity model
Maximum stomatal conductivity (mmol H2O m−2 s−1)   76 Medlyn et al. (2001)
Minimum foliage conductivity (mmol H2O m−2 s−1)   30 Medlyn et al. (2001)
Slope parameter for foliage conductivity (no units)    3.7 Medlyn et al. (2001)
Reference vapor pressure (kPa)    8.6 Scarascia-Mugnozza et al. (1996)

The model system uses three different time steps. The enzyme activities and leaf development states are updated daily. The photosynthesis model is run in a subdaily time step that was set to 1 h in the present investigations. The carbon supply rate is linearly interpolated between time steps. Finally, the emission model uses a fixed time step of 7 s, throughout which the rate of precursor supply is assumed to be constant. The model has been evaluated on sunlit leaves of different oak species grown in the field or in glasshouses. More detailed descriptions of all parts of the model can be found in previous publications (Zimmer et al., 2000, 2003; Lehning et al., 2001; Grote et al., 2006).

For comparison, the basic algorithms of the Guenther model G93 (Guenther et al., 1993) for monoterpene emissions of plants without specific storage structures have also been implemented (Eqn 1). It should be noted that the G93 algorithm produces emission estimates in µg g−1 dry weight (DW) h−1 but the results are transformed into µmol m−2 d−1 to allow direct comparison with the SIM-BIM2 model outputs. For this transformation a molecular weight for monoterpenes of 136.24 has been used (Fuentes et al., 2000).

  • E = EsFlFt(Eqn 1a)
  • image( Eqn 1b)
  • image( Eqn 1c)

(I, layer-specific photosynthetically active radiation (µmol m−2 s−1); T, layer-specific foliage temperature (K); Tr, reference temperature (303 K); Tm, optimum temperature (314 K); α = 0.0027; Cl, constant that modifies the light response (1.066); Ct1 and Ct2, constants that modify the temperature response (95 000 and 230 000, respectively); R, general gas constant (8.3143 J K−1 mol−1); E, emission rate (µg g−1 DW h−1), Fl and Ft, light and temperature response functions (0–1). All parameter values are taken from Guenther et al. (1993). Es, species-specific standard emission factor (8.4 µmol µg g−1 DW h−1, after Staudt et al., 2002) at 1000 µmol m−2 s−1 and Tr.)

Both leaf-scale emission models were coupled to the same upscaling scheme. This program reads basic vegetation and soil properties from files. More detailed vegetation conditions such as biomass distribution within layers are calculated using species-specific routines, as described in the next paragraph. The program also reads daily climatic data (temperature, radiation and precipitation) which are assumed to reflect conditions above the canopy. The daily data are used to drive foliage development (SIM), whereas the physical conditions (CANOAK model) and the emission models (G93 and BIM2) are called in subdaily (hourly) time steps.

Upscaling of the isoprenoid emission rates requires the distribution of stand-level foliage biomass and leaf area index into a number of canopy layers. Foliage biomass distribution is described with a simple one-parametric distribution function formerly used for crown shape description (Grote, 2003):

  • image( Eqn 2a)
  • image( Eqn 2b)
  • image( Eqn 2c)

(rIH, inverse relative height within the canopy (from 1 at the crown base to 0 at stand height); h, actual height within the canopy (m); hc, canopy depth (m); mfl, foliage biomass in canopy layer fl (kg DW m−2 ground); mt, total foliage biomass (kg DW m−2 ground); P, distribution parameter.)

Figure 2(a) and (b) show the impact of P varying between 1.5 and 3 on relative foliage distribution. The leaf area in each canopy layer is derived from foliage biomass and specific leaf area (SLA) in m2 kg−1. SLA determined in the middle of each canopy layer and is assumed to change linearly from maximum values occurring at crown base to minimum values at the top of the stand. It should be noted that, as a result of this weighting procedure, the same amount of foliage biomass will produce different total leaf area indices (LAIs) with different foliage distributions. For example, a total foliage biomass of 0.5 kg m−2 together with a SLA range between 4 and 10 results in LAI values between 4.1 and 2.9 if a change of P from 1.0 to 2.0 is assumed.

image

Figure 2. Dependence of the foliage distribution of Quercus ilex (holm oak) on the distribution parameter P (a) and comparison with measurements, redrawn after Sala et al. (1994) (b). ‘Valley’ and ‘ridge’ indicate two different sites investigated. Each symbol represents the amount of foliage relative to the total leaf mass per unit ground area (LMA) for one-tenth of the relative crown height.

Download figure to PowerPoint

Furthermore, the parts of the model that deal with seasonality have been adjusted to fit the requirements of evergreen plants. This required dividing total foliage biomass into a number of leaf age classes calculated from the species-specific maximum longevity. The fraction of foliage that is lost from the original leaf biomass of each age class and thus the remaining foliage biomass are determined according to Eqn 3:

  • image( Eqn 3)

(sn, fraction of foliage in a specific age class that has been lost (0–1); d, age of the foliage class (days); DL0, foliage age when litter fall starts (days); DL05, foliage age when half of the age class is gone (days) (see Table 1 for parameter values).)

The bud-burst model was taken from Lehning et al. (2001). All holm-oak-specific parameters except the growing degree day temperature necessary for flushing (Tc) were found in the literature (Table 1). Tc was fitted to enzyme activities measured on Quercus ilex trees in Montpellier during the years 1998 and 1999 (Fischbach et al., 2002; see also Grote et al., 2006).

Figure 3 shows the resulting development of foliage age classes together with the development of total foliage biomass in two years. For simplification, it is assumed that all foliage age classes are equally distributed across canopy layers. At the end of each year, all leaves in one age class are transferred into the next one. Calculated enzyme activities are applied to each age class using an age-specific reduction factor. This factor is equal to 1 for the first age class and is decreased for each precedent age class. For holm oak the enzyme activity is calculated by multiplying the previous year factor by 0.35 (Fischbach et al., 2002).

image

Figure 3. Relative development of foliage age classes (dvs) and total foliage biomass in two simulation years (age 0 is the age class that develops within the year in which the graph starts; other age classes are numbered accordingly). The initial foliage biomass was 0.55 kg dry weight (DW) m−2 ground (reflecting an annual average leaf area index (LAI) of approx. 4.0).

Download figure to PowerPoint

Climatic parameters within the canopy and layer-specific foliage temperature were calculated on the basis of physical principles employing the model CANOAK (Baldocchi et al., 1999, 2002). This model has formerly been evaluated on oak, Eucalyptus saligna and mixed forest canopies (e.g. Funk et al., 2006) and has been adjusted to meet the requirements of the evergreen oak stands investigated here. Stand dimensions, leaf area and leaf area distributions are thus provided by this canopy/phenology model and updated in daily time steps. Additionally, holm-oak-specific physical properties of the foliage were parametrized using values from the literature (Inclan et al., 1999). As the CANOAK model also calculates soil temperatures and soil water content to close the energy balance of the stand, soil properties had to be defined (see ‘site description and simulations’). It should be noted that the CANOAK model calculates photosynthesis and stomatal conductance to consider the effect of water vapor fluxes on energy balance. Because these functions are necessary for the integrative calculations applied by CANOAK they were not abandoned in the coupled system but were parametrized using literature values as far as species-specific parameters were required (Inclan et al., 1999; Ghouil et al., 2003). However, the carbon supply for emission simulations is calculated by the SIM-BIM2 inherent photosynthesis model that was previously evaluated for holm oak (Grote et al., 2006). The inconsistency, however, is judged to be negligible as both assimilation algorithms are based on the same principle (Farquhar et al., 1980) and the impact of carbon supply is assumed to be small under nonstressed conditions (Grote et al., 2006).

The available climatic data (see ‘site description and simulations’) represent daily averages or sums of climate variables. However, both emission models as well as the canopy climate model need weather input at a higher temporal resolution. Therefore, instantaneous values for air temperature and radiation at the top of the canopy are calculated for each hour of the day from daily average temperature and radiation sum. For air temperature with algorithms from De Wit et al. (1978) have been used, while instantaneous radiation is described by a sine wave calculation, similar to that described in Berninger (1994):

  • image( Eqn 4a)
  • t sr = 12 − 0.5hd( Eqn 4b)

(ri, instantaneous radiation during daytime hours (W m−2); r0, daily radiation sum (J m−2); hd, day length (h); t, time during the day; tsr, time of sunrise; π, 3.1416.)

Site description and simulations

The sensitivity analysis of the coupled model was carried out on a virtual stand of holm oak (Quercus ilex L.), a species for which the parameters of the monoterpene emission models are available (Grote et al., 2006). All model runs were carried out with a CO2 air concentration of 370 ppm. As further driving forces, daily average air temperature and the daily sums of global radiation and precipitation were used from the Montpellier, Centre d’Ecologie Fonctionnelle et Evolutive/Centre National de la Recherche Scientifique experimental station for the years 1998 and 1999 (Staudt et al., 2002). The average annual temperature at this site is 13.5°C and the average annual precipitation is 883 mm. Other boundary conditions were selected from the description of a typical Mediterranean holm oak stand in the same region (Hoff & Rambal, 2003). Thus, all simulations were carried out using a stand height of 11 m, a crown base height of 1 m and a soil and root depth of 2.5 m. The soil is assumed to have a relatively small total water-holding capacity of 190 mm, resulting from a high volumetric fraction of stones (> 75%).

Foliage distribution and initial foliage biomass were varied to show the impact on microclimate, photosynthesis and monoterpene emission. If no other values are given, the foliage biomass was initialized as 0.55 kg dry matter m−2 ground, which was estimated after Lopez et al. (2001). Similar values have been reported by Cutini (2002) and Hoff & Rambal (2003). The foliage distribution parameter P was adjusted to literature data presented in Sala et al. (1994), as shown in Fig. 2(c,d). However, it should be noted that this parameter is likely to change with stand density and site, as indicated by studies on other species (e.g. Cermak, 1998; Parker & Russ, 2004). Therefore, this parameter was subjected to a sensitivity test and varied between 1.5 and 3.0.

Results and Discussion

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and Methods
  5. Results and Discussion
  6. Acknowledgements
  7. References

The influence of leaf area and leaf area distribution on radiation and foliage temperature within the canopy is demonstrated in Fig. 4, and the resulting rates of photosynthesis and monoterpene emissions are shown in Fig. 5. Both physical and physiological traits are presented against relative cumulative LAI to focus on the actual differences among the scenarios. Three different P values (1.5, 2.0, and 3.0) were applied and simulations for each P value were calculated with two LAI values (2 and 4). As the same foliage biomass leads to different LAI values with different foliage distribution, it was necessary to adjust the initial foliage biomass in order to obtain the same leaf area for each P value. The simulated profiles represent a sunny summer day (9 July 1999 in the weather data set for Montpellier) at noon.

image

Figure 4. Simulated vertical within-canopy distribution of temperature (a) and radiation (b) in relation to crown shape factor and leaf area index (LAI) of Quercus ilex (holm oak). Profiles were created for a canopy of depth 10 m with 20 layers and a total LAI of 4.0, assuming summer weather data and midday conditions for the Montpellier site in 1999 (global radiation 574 W m−2, air temperature 30.98°C, and wind speed 1 m s−1).

Download figure to PowerPoint

image

Figure 5. Simulated responses of photosynthesis (a) and monoterpene emission (b) to within-canopy variations of microclimatic conditions resulting from variation of leaf area distribution and total leaf area index (LAI; values per m2 leaf area) of Quercus ilex (holm oak). Profiles were created for a canopy of depth 10 m with 20 layers and a total LAI of 4.0, assuming summer weather data and midday conditions for the Montpellier site in 1999. Emission was based on the temperature distribution shown in Fig. 4 and simulated monoterpene synthase activity for this day (see Fig. 8a).

Download figure to PowerPoint

Air temperature during the day is usually higher above than below the canopy, and this difference depends on previous conditions and the total LAI (for the example shown it was approx. 0.5°C for a LAI of 4 and 0.1°C for a LAI of 2). Leaf surface temperature, however, shows more variation and is a more complex function of LAI, boundary conditions and leaf area distribution. In the example presented it was higher than the outside temperature in the upper canopy fractions and similar to the air temperature in the lower canopy fractions. With increasing canopy depth, the decrease in surface temperature was greater if the LAI was high. However, this decline was less steep if the leaf area was clustered in the upper part of the canopy (the temperature in the upper 10% of foliage in the high-LAI scenario was relatively low because the large amount of foliage here already had a considerable shading effect). With respect to radiation, the patterns were quite similar, although the total difference between upper and lower canopy fractions was much larger. It should be noted that with a LAI of 2 the light intensity at ground level was still c. 30 W m−2 and thus approx. 6 times higher than with a LAI of 4. Accordingly, photosynthesis was positive throughout the canopy profile on this day only for a LAI of 2, whereas it was negative for the lower 10% of the foliage in the high-LAI scenario (Fig. 5). With only a small effect of LAI and leaf area distribution, photosynthesis declined slowly throughout the upper 20% of foliage because sensitivity to changes in the still favorable light conditions was low. In the lower 80% of foliage, photosynthesis declined more rapidly. This decline was sharper at higher LAI and with a more evenly distributed foliage area. It was small, however, in the lowest parts of the canopy, where radiation no longer changes to any great extent.

When photosynthesis is positive, it provides enough carbon to supply monoterpene production, which is considered to be a potential limitation factor in the SIM-BIM2 model. This is the reason for the fast decline to zero emissions in the lower 10% of foliage in the high-LAI scenarios. Apart from this, the decline in emission with canopy depth reflects the trends in temperature and radiation: emission decreased with increasing shading and the decrease was more marked with high LAI and evenly distributed foliage. The shape of the decline was obviously a result of a combination of temperature and radiation responses, although instantaneous monoterpene emission depended only on temperature. However, it must be noted that the monoterpene synthase activity was separately simulated for each foliage layer in relation to daily averages of light and temperature. As shown in Fig. 6(a), the daily average temperature was almost the same in each layer because the contrasting patterns during the day and the night cancelled each other out. Therefore, the differences in synthase activity and thus in instantaneous emission originated from the cumulative differences in the light regime among the canopy layers (Fig. 6b). Figure 6(c) presents monoterpene synthase (mono-TPS) activity, which was approx. 75% lower in the lowest than in the uppermost canopy layer.

image

Figure 6. Simulated annual development of daily average temperature (a) and daily radiation sum (b) in the lowest (dashed line) and uppermost (solid line) canopy layers (for temperature, different canopy layers cannot be distinguished) of Quercus ilex (holm oak). (c) Monoterpene synthase (mono-TPS) activity for different canopy layers (solid line, upper layer; dashed line, lower layer) in the course of a year. Climatic variables are based on weather data from Montpellier for 1999, considering the foliage development presented in Fig. 3 and a foliage distribution factor of 2.6.

Download figure to PowerPoint

To test the dependence of simulated monoterpene emission on total leaf area, the model was run with the crown shape parameter P = 2.6 for 1 yr with the Montpellier climate data of 1999. In Fig. 7, the resulting emission is shown for three different LAI values scenario (values given represent the leaf area at day 200 with the seasonal development presented in Fig. 3). The monoterpene emission efficiency (emission rate per m2 leaf area) scaled almost linearly with leaf area index, resulting in 177% and 127% emission with LAIs of 2 and 4 compared with the simulation carried out with an LAI of 6 (Fig. 7a). This reflects the better overall light and temperature conditions in less foliated crowns. However, the presence of more leaves generally overcompensated this effect, leading to increasing total emissions with increasing LAI (Fig. 7b). From the relation between the annual emission sums (15.4, 22.2, and 26.3 mmol m−2 yr−1) it can also be deduced that the sensitivity of BVOC emission to increasing leaf area decreases with LAI and probably approaches zero at LAI values of approx. 6. The simulated range of BVOC emission differing by a factor of 1.5 indicates a large uncertainty connected with the estimation of LAI, considering that LAI values between 2 and 6 are well within the range of field observations (Damesin et al., 1998).

image

Figure 7. Impact of leaf area index (LAI) of Quercus ilex (holm oak) on monoterpene emission during the course of a year. (a) Emission per m−2 projected leaf area (LA) with different stand densities but dynamic leaf area development; (b) total canopy emission per m−2 ground area, again with different stand densities but dynamic leaf area development. Calculations were carried out with weather data from Montpellier for 1999, considering the foliage development presented in Fig. 3 with adjusted foliage biomass and a foliage distribution factor of 2.6.

Download figure to PowerPoint

The model is compared to the G93 algorithm using the whole Montpellier data set from the beginning of 1998 to the end of 1999 (with P = 2.6 and the foliage biomass development presented in Fig. 3). For this period, the development of mono-TPS activity was previously evaluated on single leaves that were fully sun exposed (Grote et al., 2006). This evaluation is presented in Fig. 8(a). It is apparent that short-term decreases and increases of mono-TPS activity that depend on actual temperature and irradiation developments occur during the course of the year. The dynamic development of total monoterpene emission per unit area ground calculated on the basis of emission activity by SIM-BIM2 is slightly smaller than that estimated with G93 (Fig. 8b) and produces less day-to-day variation. The absolute difference can be explained by the uncertainty related to the emission factor used in G93. Actually, this factor has been found to be highest in early or late summer and under sunny compared with shade conditions (Penuelas & Llusia, 1999; Staudt et al., 2002, 2003). Therefore, the use of a fixed emission potential has been criticized by several authors (Nunez et al., 2002; Plaza et al., 2005). The lower day-to-day variation in emission can be explained with the inherent assumption of the SIM-BIM2 model that the emission rate depends not only on instantaneous climate conditions but also on the potential emission rate. This potential rate is an integrated measure of the conditions present on preceding days. In Table 2, annual emission per unit ground area is given with different P values for the two models to summarize the results obtained to this point. The table shows that different leaf area distribution patterns lead to different (simulated) BVOC emissions from canopies with the same LAI and that a canopy with a foliage distribution that is skewed upwards is likely to produce more monoterpenes.

image

Figure 8. Simulated and measured Quercus ilex (holm oak) monoterpene synthase (mono-TPS) activity at the top of the canopy (a) and total daily monoterpene emission per day and m−2 ground calculated with the seasonal isoprenoid synthase model combined with the biochemical isoprenoid emission model 2 (SIM-BIM2) (solid line) and the Guenther algorithm (G93) (dashed line) (b). Calculations were carried out with weather data from Montpellier for 1998–1999, considering the foliage development presented in Fig. 3 and a foliage distribution factor of 2.6.

Download figure to PowerPoint

Table 2.  Monoterpene emissions obtained in the year 1999 with two models (the seasonal isoprenoid synthase model combined with the biochemical isoprenoid emission model 2 (SIM-BIM2) and the Guenther model (G93)) using three different foliage distributions (P) for Quercus ilex (holm oak)
P valueSIM-BIM2G93
Emission sum% of P = 1.5Emission sum% of P = 1.5
  1. Monoterpene emissions were measured in mmol m−2 ground yr−1.

1.513.810017.6100
2.016.3711921.8124
3.018.613525.8147

The seasonal differences between the model results are more clearly outlined in Fig. 9. This figure shows that G93 produces emissions whereas SIM-BIM2 does not because monoterpene synthase activity is zero (in winter and spring). On the other hand SIM-BIM2 produces larger emission rates than G93 because synthase activity is only slowly responding to unfavorable conditions (mainly in autumn). Overall the figure is skewed towards the G93 results at higher emission rates, which reflects the immediate responses of the G93 model to days with high light and/or temperature (in summer).

image

Figure 9. Comparison of emission rates produced by the Guenther (G93) model and the seasonal isoprenoid synthase model combined with the biochemical isoprenoid emission model 2 (SIM-BIM2). Results are based on the simulations presented in Fig. 8, using a foliage distribution parameter (P) value of 2.6 for both models.

Download figure to PowerPoint

Conclusions

It has been shown that SIM-BIM2 can be used for extrapolating monoterpene emissions from the leaf to the stand scale. Simulated emissions based on the long-term development of enzyme activity as well as on immediate weather conditions were somewhat smaller than those obtained with the Guenther algorithm (G93). This can be explained by the uncertain determination of the emission factor as well as the more immediate response of G93 to environmental conditions. The explicitly calculated acclimation of potential emission rate (synthase activity) in SIM-BIM2 makes it particularly suitable for scaling BVOC emission in time and space and for investigating the effect of canopy structure on emission.

However, scaling requires reliable short-term information about temperature and radiation at the scale of the canopy layer. Leaf area distribution has a strong effect on these variables. Thus, it is not surprising that total emission estimates are quite sensitive to this parameter. As it is shown that leaf area and foliage distribution should be considered in estimate of monoterpene emission, it is important to emphasize that LAI is influenced by site conditions, management and natural disturbances. Literature values for holm oak range between 2 and 6 (Damesin et al., 1998), but the investigations in which these values were obtained generally focused on mature stands without major disturbances. Thus, scaling up to the regional level without considering the regional distribution of LAI might lead to erroneous results. Also, vertical foliage distribution is not only species-specific but depends on tree age, tree social state (e.g. Morales et al., 1996; Parker & Russ, 2004), and stand density (Sala et al., 1994; Grote & Reiter, 2004). Therefore, a simple procedure to determine foliage distribution in relation to these influences is urgently needed.

Acknowledgements

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and Methods
  5. Results and Discussion
  6. Acknowledgements
  7. References

I am deeply grateful to Dennis Baldocchi who provided the source code of the CANOAK model and Alexander Knohl for discussion of the implementation problems that always arise when a model is applied in a new context. I also would like to thank Michael Staudt who provided the original climate data, and the anonymous reviewers of a previous manuscript for their helpful and inspiring comments. The work was financially supported by the German Federal Ministry of Education and Research (BMBF) in the framework of the national joint research project AFO2000.

References

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and Methods
  5. Results and Discussion
  6. Acknowledgements
  7. References
  • Baldocchi DD, Fuentes JD, Bowling DR, Turnipseed AA, Monson RK. 1999. Scaling isoprene fluxes from leaves to canopies: test cases over a boreal aspen and a mixed species temperate forest. Journal of Applied Meteorology 38: 885898.
  • Baldocchi DD, Wilson KB, Gu L. 2002. How the environment, canopy structure and canopy physiological functioning influence carbon, water and energy fluxes of a temperate broad-leaved deciduous forest – an assessment with the biophysical model CANOAK. Tree Physiology 22: 10651077.
  • Berninger F. 1994. Simulated irradiance and temperature estimates as a possible source of bias in the simulation of photosynthesis. Agricultural and Forest Meteorology 71: 1932.
  • Caldwell MM, Meister H-P, Tenhunen JD, Lange OL. 1986. Canopy structure, light microclimate and leaf gas exchange of Quercus coccifera L. in a Portuguese macchia: measurements in different canopy layers and simulations with a canopy model. Trees – Structure and Function 1: 2541.
  • Cermak J. 1998. Leaf distribution in large trees and stands of the floodplain forest in southern Moravia. Tree Physiology 18: 727737.
  • Ciccioli P, Brancaleoni E, Frattoni M, Marta S, Brachetti A, Vitullo M, Tirone G, Valentini R. 2003. Relaxed eddy accumulation, a new technique for measuring emission and deposition fluxes of volatile organic compounds by capillary gas chromatography and mass spectrometry. Journal of Chromatography A 985: 283296.
  • Ciccioli P, Fabozzi C, Brancaleoni E, Cecinato A, Frattoni M, Loreto F, Kesselmeier J, Schäfer L, Bode K, Torres L, Fugit J-L. 1997. Use of the isoprene algorithm for predicting the monoterpene emission from the Mediterranean holm oak Quercus ilex L. Performance and limits of this approach. Journal of Geophysical Research 102: 2331923328.
  • Cutini A. 2002. Litterfall and leaf area index in the CONECOFOR permanent monitoring plots. Journal of Limnology 61: 6268.
  • Damesin C, Rambal S, Joffre R. 1998. Cooccurrence of trees with different leaf habit: a functional approach on Mediterranean oaks. Acta Oecologia 19: 195204.
  • De Wit CT, Goudriaan J, van Laar HH, Penning de Vries FWT, Rabbinge R, van Keulen H. 1978. Simulation of assimilation, respiration and transpiration of crops. Wageningen, the Netherlands: PUDOC.
  • Farquhar GD, von Caemmerer S, Berry JA. 1980. A biochemical model of photosynthetic CO2 assimilation in leaves of C3 species. Planta 149: 7890.
  • Fischbach RJ, Staudt M, Zimmer I, Rambal S, Schnitzler J-P. 2002. Seasonal pattern of monoterpene synthase activities in leaves of the evergreen tree Quercus ilex. Physiologia Plantarum 114: 354360.
  • Fuentes JD, Lerdau M, Atkinson R, Baldocchi D, Bottenheim JW, Ciccioli P, Lamb B, Geron C, Gu L, Guenther A, Sharkey TD, Stockwell W. 2000. Biogenic hydrocarbons in the atmosphere boundary layer: a review. Bulletin of the American Meteorological Society 81: 15371575.
  • Funk JL, Giardina CP, Knohl A, Lerdau MT. 2006. Influence of nutrient availability, stand age, and canopy structure on isoprene flux in a Eucalyptus saligna experimental forest. Journal of Geophysical Research. doi: 10.1029/2005JG000085.
  • Geron C, Guenther A, Sharkey T, Arnts RR. 2000. Temporal variability in basal isoprene emission factor. Tree Physiology 20: 799805.
  • Geron CD, Nie D, Arnts RR, Sharkey TD, Singsaas EL, Vanderveer PJ, Guenther A, Sickles IIJE, Kleindienst TE. 1997. Biogenic isoprene emission: model evaluation in a southeastern United States bottomland deciduous forest. Journal of Geophysical Research 102: 1890318916.
  • Ghouil H, Montpied P, Epron D, Ksontini M, Hanchi B, Dreyer E. 2003. Thermal optima of photosynthetic functions and thermostability of photochemistry in cork oak seedlings. Tree Physiology 23: 10311039.
  • Grote R. 2003. Estimation of crown radii and crown projection area from stem size and tree position. Annals of Forest Science 60: 393402.
  • Grote R, Mayrhofer S, Fischbach RJ, Steinbrecher R, Staudt M, Schnitzler J-P. 2006. Process-based modelling of isoprenoid emissions from evergreen leaves of Quercus ilex (L.). Atmospheric Environment 40: 152165.
  • Grote R, Reiter IM. 2004. Competition-dependent modelling of foliage biomass in forest stands. Trees – Structure and Function 18: 596607.
  • Guenther A, Hewitt CN, Erickson D, Fall R, Geron C, Graedel T, Harley P, Klinger L, Lerdau M, McKay WA, Pierce T, Scholes B, Steinbrecher R, Tallamraju R, Taylor J, Zimmerman P. 1995. A global model of natural volatile organic compound emissions. Journal of Geophysical Research 100: 88738892.
  • Guenther A, Karl T, Harley P, Wiedinmyer C, Palmer PI, Geron C. 2006. Estimates of global terrestrial isoprene emissions using MEGAN (Model of Emissions of Gases and Aerosols from Nature). Atmospheric Chemistry and Physics 6: 31813210.
  • Guenther A, Zimmerman P, Haley P, Monson R, Fall R. 1993. Isoprene and monoterpene emission rate variability: model evaluations and sensitivity analysis. Journal of Geophysical Research 98: 1260912617.
  • Harley P, Vasconcellos P, Vierling L, Pinheiro CCDS, Greenberg J, Guenther A, Klinger L, De Almeida SS, Neill D, Baker T, Phillips O, Malhi Y. 2004. Variation in potential for isoprene emissions among neotropical forest sites. Global Change Biology 10: 630650.
  • Hoff C, Rambal S. 2003. An examination of the interaction between climate, soil and leaf area index in a Quercus ilex ecosystem. Annals of Forest Science 60: 153161.
  • Huber L, Laville P, Fuentes JD. 1999. Uncertainties in isoprene emissions from a mixed deciduous forest estimated using a canopy microclimate model. Journal of Applied Meteorology 38: 899912.
  • Inclan R, Ribas A, Penuelas J, Gimeno BS. 1999. The relative sensitivity of different Mediterranean plant species to ozone exposure. Water, Air, and Soil Pollution 116: 273277.
  • Jarvis PG. 1976. The interpretation of leaf water potential and stomatal conductance found in canopies in the field. Philosophical Transactions of the Royal Society of London, Series B 273: 593610.
  • Kuhn U, Rottenberger S, Biesenthal T, Wolf A, Schebeske G, Ciccioli P, Brancaleoni E, Frattoni M, Tavares TM, Kesselmeier J. 2004. Seasonal differences in isoprene and light-dependent monoterpene emission by Amazonian tree species. Global Change Biology 10: 663682.
  • Lamb B, Gay D, Westberg H, Pierce T. 1993. A biogenic hydrocarbon emission inventory for the U.S. using a simple forest canopy model. Atmospheric Environment Part A 27: 16731690.
  • Lamb B, Thomas P, Baldocchi D, Allwine E, Dilts S, Westberg H, Geron C, Guenther A, Lee K, Harley P, Zimmerman P. 1996. Evaluation of forest canopy models for estimating isoprene emissions. Journal of Geophysical Research 101: 2278722798.
  • Larsen DR, Kershaw JA. 1996. Influence of canopy structure assumptions on predictions from Beer's law. A comparison of deterministic and tochastic simulations. Agricultural and Forest Meteorology 81: 6177.
  • Lehning A, Zimmer W, Zimmer I, Schnitzler J-P. 2001. Modeling of annual variations of oak (Quercus robur L.) isoprene synthase activity to predict isoprene emission rates. Journal of Geophysical Research 106: 31573166.
  • Lenz R, Selige T, Seufert G. 1997. Scaling up the biogenic emissions from test sites at Castelporziano. Atmospheric Environment 31: 239250.
  • Lopez B, Sabaté S, Gracia CA. 2001. Annual and seasonal changes in fine root biomass of a Quercus ilex L. forest. Plant and Soil 230: 125134.
  • Martin MJ, Stirling CM, Humphries SW, Long SP. 2000. A process-based model to predict the effects of climatic change on leaf isoprene emission rates. Ecological Modelling 131: 161174.
  • Medlyn BE, Barton CVM, Broadmeadow MSJ, Ceulemans R, Angelis PD, Forstreuter M, Freeman M, Jackson SB, Kellomäki S, Laitat E, Rey A, Roberntz P, Sigurdsson BD, Strassemeyer J, Wang K, Curtis PS, Jarvis PG. 2001. Stomatal conductance of forest species after long-term exposure to elevated CO2 concentration: a synthesis. New Phytologist 149: 247264.
  • Medlyn BE, Jarvis PG. 1999. Design and use of a database of model parameters from elevated [CO2] experiments. Ecological Modelling 124: 6983.
  • Morales D, Jimenez MS, Gonzales-Rodriguez AM, Cermak J. 1996. Laurel forests in Tenerife, Canary Islands. II. Leaf distribution patterns in individual trees. Trees – Structure and Function 11: 4146.
  • Navas M-L, Ducout B, Roumet C, Richarte J, Garnier J, Garnier E. 2003. Leaf life span, dynamics and construction cost of species from Mediterranean old-fields differing in successional status. New Phytologist 159: 213228.
  • Niinemets Ü. 1999. Components of leaf dry mass per area thickness and density alter leaf photosynthetic capacity in reverse directions in woody plants. New Phytologist 144: 3547.
  • Niinemets Ü, Hauff K, Bertin N, Tenhunen JD, Steinbrecher R. 2002. Monoterpene emissions in relation to foliar photosynthetic and structural variables in Mediterranean evergreen Quercus species. New Phytologist 153: 243256.
  • Nunez L, Plaza J, Perez-Pastor R, Pujadas M, Gimeno BS, Bermejo V, Garcia-Alonso S. 2002. High water vapour pressure deficit influence on Quercus ilex and Pinus pinea field monoterpene emission in the central Iberian Peninsula (Spain). Atmospheric Environment 36: 44414452.
  • Parker GG, Russ ME. 2004. The canopy surface and stand development: assessing forest canopy structure and complexity with near-surface altimetry. Forest Ecology and Management 189: 307315.
  • Penuelas J, Llusia J. 1999. Seasonal emission of monoterpenes by the Mediterranean tree Quercus ilex in field conditions: relations with photosynthetic rates, temperature and volatility. Physiologia Plantarum 105: 641647.
  • Penuelas J, Llusia J. 2001. The complexity of factors driving volatile organic compound emissions by plants. Biologia Plantarum 44: 481487.
  • Plaza J, Nunez L, Pujadas M, Perrez-Pastor R, Bermejo V, Garcia-Alonso S, Elvira S. 2005. Field monoterpene emission of Mediterranean oak (Quercus ilex) in the central Iberian Peninsula measured by enclosure and micrometeorological techniques: observation of drought stress effect. Journal of Geophysical Research 110: 1105.
  • Sala A, Sabaté S, Gracia C, Tenhunen JD. 1994. Canopy structure within a Quercus ilex forested watershed: variations due to location, phenological development, and water availability. Trees – Structure and Function 8: 254261.
  • Scarascia-Mugnozza GE, De Angelis P, Matteucci G, Kuzminsky E. 1996. Carbon metabolism and plant growth under elevated CO2 in a natural Quercus ilex‘macchia stand’. In: Koch GW, Mooney HA , eds. Carbon dioxide and terrestrial ecosystems. San Diego, CA, USA: Academic Press, 209230.
  • Schaab G, Steinbrecher R, Lacaze B. 2003. Influence of seasonality, canopy light extinction, and terrain on potential isoprenoid emission from a Mediterranean-type ecosystem in France. Journal of Geophysical Research 108 (D13). doi: 10.1029/2002JD002899.
  • Solmon F, Sarrat C, Serca D, Tulet P, Rosset R. 2004. Isoprene and monoterpenes biogenic emissions in France: modeling and impact during a regional pollution episode. Atmospheric Environment 38: 38533865.
  • Spirig C, Neftel A, Ammann C, Dommen J, Grabmer W, Thielmann A, Schaub A, Beauchamp J, Wisthaler A, Hansel A. 2005. Eddy covariance flux measurements of biogenic VOCs during ECHO 2003 using proton transfer reaction mass spectrometry. Atmospheric Chemistry and Physics 5: 465481.
  • Staudt M, Joffre R, Rambal S. 2003. How growth conditions affect the capacity of Quercus ilex leaves to emit monoterpenes. New Phytologist 158: 6173.
  • Staudt M, Rambal S, Joffre R, Kesselmeier J. 2002. Impact of drought on seasonal monoterpene emissions from Quercus ilex in southern France. Journal of Geophysical Research 107: 46024608.
  • Zimmer W, Brüggemann N, Emeis S, Giersch C, Lehning A, Steinbrecher R, Schnitzler J-P. 2000. Process-based modelling of isoprene emission by oak leaves. Plant, Cell & Environment 23: 585595.
  • Zimmer W, Steinbrecher R, Körner C, Schnitzler JP. 2003. The process-based SIM-BIM model: towards more realistic prediction of isoprene emissions from adult Quercus petraea forest trees. Atmospheric Environment 37: 16651671.