Modelling forest carbon balances considering tree mortality and removal

Modelling forest carbon balances considering tree mortality and removal

Agricultural and Forest Meteorology 151 (2011) 179–190 Contents lists available at ScienceDirect Agricultural and Forest Meteorology journal homepag...

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Agricultural and Forest Meteorology 151 (2011) 179–190

Contents lists available at ScienceDirect

Agricultural and Forest Meteorology journal homepage: www.elsevier.com/locate/agrformet

Modelling forest carbon balances considering tree mortality and removal Rüdiger Grote a,∗ , Ralf Kiese a , Thomas Grünwald b , Jean-Marc Ourcival c , André Granier d a

Institute for Meteorology and Climate Research (IMK-IFU), Karlsruher Institut für Technologie (KIT), Kreuzeckbahnstr. 19, 82467 Garmisch-Partenkirchen, Germany Institute of Hydrology and Meteorology, Technische Universität Dresden, Pienner Str. 23, D-01737 Tharandt, Germany c Centre d’Ecologie Fonctionnelle et Evolutive - CNRS, 1919 Route de Mende, 34293 Montpellier CEDEX 5, France d UMR INRA-UHP Forest Ecology and Ecophysiology, 54820 Champenoux, France b

a r t i c l e

i n f o

Article history: Received 23 April 2010 Received in revised form 29 September 2010 Accepted 4 October 2010 Keywords: Physiologically oriented modelling Integrated modelling Eddy-flux measurements Tree growth Carbon balances Thinning

a b s t r a c t The determination of ecosystem carbon balances is a major issue in environmental research. Forest inventories and – more recently – Eddy covariance measurements have been set up to guide sustainability assessments as well as carbon accounting. A differentiation between ecosystem compartments of carbon such as soil and vegetation, or above- and belowground storages nevertheless requires further empirical assumptions or model simulations. However, models to estimate carbon balances often do not account for carbon export by management and the direct and indirect impacts of forest management. To overcome this obstacle, we complemented a physiologically based process model (MoBiLE-PSIM) with routines for dimensional tree growth and mortality and evaluated the full model with measurements of water availability, primary production, respiration fluxes and forest development (tree dimensions and numbers per hectare). The model is applied to three forests representing different physiological types and climatic environments: Norway spruce, European beech and Mediterranean holm oak. Simulated carbon balances are presented on a daily, annual and decadal time scale throughout the years 1998–2008 for all three stands. On average, gross primary production is 2.0, 1.7, and 1.4 and net ecosystem production 0.6, 0.6, and 0.3 kg C m−2 a−1 . Export of carbon by thinning is highest in the middle-aged beech stand (0.24 kg C m−2 a−1 ) which decreases net ecosystem production by 15% compared with an unthinned stand. Between 46 (spruce) and 72 (oak) % of carbon gained by net ecosystem production is sequestered below ground (incl. roots) – a share that is decreased if a part of the carbon is exported as timber. The role of further impacts, in particular carry-over effects in years that follow intense drought periods, is highlighted and the usefulness of the approach for highly resolved environmental change studies is discussed. © 2010 Elsevier B.V. All rights reserved.

1. Introduction In times of increasing global industrialization and accelerating environmental changes, reliable methods for understanding the development of forest ecosystems and their interaction with possible management activities are of uppermost interest. This includes the uptake and sequestration of carbon into plant tissue or soil. The development and application of models that are able to represent ecosystem responses to these changes is therefore highly desirable. These models are complicated however by the multitude and concurrence of possible impacts (such as increasing temperatures and atmospheric CO2 concentrations, nitrogen deposition, and also changes in light availability by thinning operations). Accounting for environmental impacts on forests over long time periods (at least several years) requires the consideration of not

∗ Corresponding author. Tel.: +49 08821 183124; fax: +49 08821 183294. E-mail address: [email protected] (R. Grote). 0168-1923/$ – see front matter © 2010 Elsevier B.V. All rights reserved. doi:10.1016/j.agrformet.2010.10.002

only external environmental changes (e.g. climate, deposition) but also changes in the vegetation itself that affect microclimatic conditions and carbon allocation. These changes are structural and develop from tree establishment, death and dimensional growth (height and diameter growth). Dimensional growth affects the amount, distribution, and properties of foliage in the canopy and fine roots in the soil and thus influences carbon gain as well as nutrient and water uptake. Additionally, dimensional changes are superimposed by management both directly by cutting larger or smaller than average trees and indirectly by changes of the environmental conditions. Such feedbacks are particularly important when single trees or tree social classes are modelled, because the size of a tree determines its demand as well as its competition strength for resources. Adding this feature to physiological models and producing output that can be compared with meaningful data from forest inventories has been identified as one of the main deficits of existing models (Landsberg, 2003; King, 2005). In these reviews it has been noted that many modelling exercises studying matter balances based on physiological processes assume that stand

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structural changes such as height increase or a shift between social tree classes have no or only negligible feedback impacts throughout the simulation period (e.g. Duursma et al., 2009; Tatarinov and Cienciala, 2009). At least for long-term growth predictions, stand dimensional development and the selection of underlying assumptions in the simulations become crucially important. Each added feature in physiological models generally increases the number of parameters that are intrinsically uncertain. This may be the reason that tree dimensional development has only been acknowledged in few models yet (Peng et al., 2002; Deckmyn et al., 2008). Therefore we seek a simple but nevertheless general method with measurable parameters to approach this task. In order to account for physiological changes that depend on tree dimension, it is in fact not necessary to describe dimensions explicitly. Simple physiological models derive allometric relationships dynamically from stem biomass (Landsberg and Waring, 1997). However, this is an inadequate approach if more complex interactions such as light competition between different canopy layers or tree classes are to be assessed. A very interesting concept for mechanistic description of dimensional growth is the pipe-model theory (Shinozaki and Yoda, 1964) proposed by Valentine (1985) and used by several authors since then (e.g. Sievänen, 1993; Valentine, 1990; Mäkelä, 1997, 2002). The drawback of this approach is that it does not predict height growth per se but uses further assumptions to distribute substrate production. Another approach derives dimensional development directly from empirical relations to stem form and height–diameter ratio assuming volume growth is consistent with biomass growth. For example height–diameter ratio has been related to stand density (Bossel, 1996), diameter at breast height (Friend et al., 1997), or (more process-based) to the activity of different meristems (Thornley, 1999) and canopy production (Robinson and Ek, 2003). Less frequently, empirical knowledge of relations between stem volume, height, and diameter development is directly used to distribute stem growth in a model (Korol et al., 1995; Kimmins et al., 1999). This approach is partly attributable to the fact that the determination of these relations requires a considerable mensuration effort for each tree species. Since more and more of this kind of information is available in the literature (e.g. Zianis et al., 2005) this option may be more frequently used. The approach presented below describes a calculation procedure for continuously updating height and stem diameter of a stand, tree class, or single tree with species specific taper/volume curves and wood density parameters. Since sapwood biomass production is used as an input, it is particularly suitable for physiology-oriented models that include carbon allocation procedures. We apply this approach in the physiologically based vegetation model PSIM and the biogeochemical DNDC model as a new implementation within the modelling framework MoBiLE (Modular Biosphere simuLation Environment; Grote et al., 2008, 2009a,b; Holst et al., 2010). This combination is an alternative implementation to the PnET-NDNDC model which has been widely used to estimate trace gas emissions from forest soils (e.g. Kesik et al., 2006; ButterbachBahl et al., 2009). The PSIM model calculates primary production, plant respiration, litterfall and allocation, including the increase of woody biomass. All these processes depend directly or indirectly on micro-climatic environmental conditions and the supply of water and nutrients (i.e. nitrogen). The DNDC (De-NitrificationDe-Composition) model accounts for the mineralization of litter and calculates water and nitrogen availability for the vegetation. This modelling approach enables a detailed view and characterization of carbon fluxes and pools within forest ecosystems. In order to prove the wide applicability of this approach we chose three example sites that cover coniferous and broad leaved as well as evergreen and deciduous forests under temperate and Mediterranean climatic conditions. For each of these sites, various long-term measurements of different resolutions (daily, weekly, annual) are available

that enable the evaluation of various parts of the model – and thus the certainty of ecosystem carbon balance calculations.

2. Model description 2.1. General Simulations were performed using a combination of five models (covering microclimate, water cycle, physiology, soil nutrient dynamics, and dimensional changes as described further down in detail) that are combined in the MoBiLE framework. Due to 1-D column modelling, any simulation is site-specific and only vertically differentiated information is exchanged between time steps. The vertical spatial scale extends from the uppermost top of the vegetation down to the total rooting depth in the soil. Therefore the (1-D) model space is stratified into a flexible number of vegetation layers with equal height intervals in the canopy (between 2 and 40 depending on total vegetation height, with the maximum number of layers reached at 20 m stand height – if the stand is larger, the width of the layers are increased accordingly) and a variable number of soil layers. Information to initialize vegetation includes species, height and canopy length, average diameter at 1.3 m height, total stem volume, total aboveground biomass, and tree number (all in hectare-based units). Soil layers are explicitly defined by the site initialization file: carbon and nitrogen content, field capacity and wilting point, pH, saturated conductivity, bulk density, clay and stone content can be directly prescribed if available. Initialization of biomass as well as carbon and nitrogen content in various living and dead tissues that are handled by the model are derived from the available initial information. The total amount and distribution of foliage and fine roots are calculated from species-specific parameters (see Grote, 2003). Additional soil compartments (such as five different litter pools) are derived from the initialized carbon and nitrogen amounts and iterative procedures based on the parameterized C/N ratios of the compartments (described in more detail by De Bruijn et al., 2009). The time step at which the fundamental equations of each module are integrating forward in time can either be ‘sub-daily’, daily, or any multiple of a day (e.g. annually for stand development). The individual time steps of the modules are kept constant throughout the simulation. The smallest time step in the simulation is the base-time step which is selected according to input data availability and/or module requirement (generally one hour). Modules run on longer time steps (daily, yearly) are called after the sum of basetime steps (i.e. hourly) has reached the module specific time step (see also Fig. 1 and the following sections). The exchange of variables is managed by the MoBiLE framework (Fig. 1). It is used to combine the different models/modules that describe in particular the cycling of water, carbon and nitrogen in the biosphere as well as exchange processes of these elements with the atmosphere and hydrosphere. Furthermore, it provides climate data in the time step needed by the respective model, including a finer time step than the original data so that daily temperature and radiation values can be distributed throughout the day to provide hourly or smaller time step resolution if these are not a direct input into the model. MoBiLE assumes sinusoidal distribution schemes for temperature (De Wit et al., 1978) and radiation (Berninger, 1994). Comparable simulation frameworks with similar objectives have been published in the past. For example SONCHES (Knijnenburg et al., 1984), GAPS (Butler and Riha, 1989; Rossiter and Riha, 1999), EXPERT-N (Engel and Priesack, 1993; Priesack et al., 2006), APSIM (McCown et al., 1996; Huth et al., 2003), and COINS (Roxburgh and Davies, 2006) all represent flexible envelopes for previously published models or biosphere related process simu-

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Fig. 1. Flowchart of the simulation. Grey boxes represent modules; boxes with broken lines indicate process-groups. Output files are only written when the respective option is selected. Abbreviations ts and nd stand for number of time step and day during the simulation, respectively; tsMax is the number of sub-daily time steps executed within one day (set to 24). Input variables for the daily loop are calculated as averages or sums depending on the variable.

lations. Although some of these frameworks include process-based forest models, forest growth and development is generally not addressed because the primary focus is on agricultural applications (but see Liu et al., 2002). 2.2. Biogeochemical models The simple Empirical-based Canopy Model (ECM, Grote et al., 2009a) is applied to provide hourly climatic information for each canopy layer. Soil temperature which drives the biogeochemical calculations is derived from the DNDC model (Li et al., 1992). The hourly climate data (air temperature and global radiation) drive a common photosynthesis model that calculates carbon uptake with dependence on light, temperature, enzyme activity based on Farquhar et al. (1980) and the water constraint according to Ball et al. (1987). This approach determines carbon gain by iteratively adjusting stomata conductivity and thus transpiration which is limited by water availability. Furthermore, the light saturated rate of carboxylation is reduced (a) when nitrogen concentration in the leaf tissue is below optimum, (b) relative water availability falls below a species-specific threshold (Grote et al., 2009a), or (c) cumulative cold temperatures provoke a physiological dormancy (Bergh et al., 1998). Further vegetation processes are calculated with the physiologically based PSIM model that simulates phenology (Grote, 2007), plant respiration (Thornley and Cannell, 2000), senescence and allocation of carbon and nitrogen (Grote, 1998), and nitrogen uptake. The allocation process is sink-strength driven, which means that carbon and nitrogen are distributed according to the relative demand within a given plant compartment: foliage, fine roots, reproductive tissue, and sapwood. The demand for carbon results from optimum ratios between foliage and fine roots, sapwood and reproductive tissue, and foliage area and sapwood area respectively. The demand for nitrogen is defined by the ratio between actual and optimum nitrogen concentration in each compartment.

Senescence is a function of turnover rates, considering a retranslocation of nitrogen from dying foliage (but no other tissues) back into the plant as long as nitrogen demand is not fully established. Fine roots take up nitrogen from the soil as a function of nitrogen availability and species specific uptake rate, rendering the model sensitive to nitrogen distribution as well as fine root distribution within the soil profile. The PSIM model has been previously coupled to the soil process model DNDC (Li et al., 1992) by Grote et al. (2009b). The linkage between the two models is established by the plant uptake of nitrogen that is limited by the availability of nitrate and ammonium in the soil solution. Above- and belowground litter is mineralized, nitrified and denitrified based on DNDC calculations. Water availability is calculated by the DNDC water balance model (see also Li et al., 2000; Stange, 2001). In contrast to the original version, the adopted DNDC model in MoBiLE is able to calculate water pools and fluxes throughout the total rooted soil profile with soil layer specific parameterizations (Holst et al., 2010). Thus, soil hydraulic properties such as field capacity or wilting point can be initialized using flexible soil layer numbers and extensions according to available measurements. 2.3. Dimensional growth model To describe tree growth from seedlings to mature trees, a general formulation for the development of stem form (=stem volume relative to a column volume with a defined reference diameter) is required. Although stem diameter at 1.3 m height (DBH) is generally available for initialization and evaluation purposes, it is not suitable to be directly related to stem form because stem taper changes during stand development. However, as proposed by Hohenadl (1922), it can be assumed that the form of a tree is approximately constant in relation to a reference diameter at 10% tree height (D01). D01 in turn can be roughly estimated from DBH by linear extrapolation using the current height/diameter relation and the height

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difference between the two stem positions. This calculation is purely empirical and is only used here for transferring initialized DBH to D01 and D01 back into DBH in order to provide evaluation data that can be compared with repeated DBH measurements. It does not affect the model calculations. The new stemwood volume of a stand (Vs ) is calculated from wood density (Dw ) and the dry matter of total wood biomass (Mw ) considering the fractions of branch (fbra ) and coarse roots (fugw ): Vs =

Mw (1.0/(1.0 − fbra ) + fugw ) × Dw

(1)

Woody biomass is determined from allocation into the woody compartment (gMw ) and loss of woody material. The latter is the sum of two processes: tree mortality and death of branches. While tree mortality is treated as a time-dependent fraction of the total wood biomass (fmort ), branch senescence is calculated as a fraction of sapwood senescence (sMw ; the rest of the senescence is forming heartwood tissue that remains a fraction of Mw ) according to:  Mw = Mw × (1 − fmort ) − (sMw × fbra ) + gMw

(2)

Tree mortality is expressed specifically either for a closed canopy (0.02 a−1 ) or for the conditions when canopy is not closed (0.002 a−1 ) as in young stages of development or after thinning. Alternatively, it can be defined explicitly by the user together with a specific date, representing a thinning event. The fraction of branches is calculated dynamically depending on tree size: increasing when trees are very young and decreasing when trees are nearing maturity. In our approach, fbra is defined as the minimum of three values: development of small trees estimated from base diameter (DBAS) which is derived from tree height assuming that stem form is equal to a cone (Eq. (3)a), development of trees taller than 2.5 m estimated from the fraction of branches dynamically calculated from DBH according to Bossel (1996) (Eq. (3)b) according to: fbra = 1.0 − exp(−fBRAY × DBAS) fbra

10



DBH = fBRAO + (1.0 − fBRAO ) × exp −10.0 × DBHmax



(3a) (3b)

with fBRAY and fBRAO being parameters for young and old trees, respectively, and “exp” references to an exponential function, and a maximum value of fbra = 0.5 for the mature trees. The stem volume increment for the single (average) tree (dVt ) is simply determined by the difference between new and old stand volume divided by stem number. Height and diameter growth is calculated as suggested by Bossel (1996) for unrestricted canopy space conditions with D01 instead of the originally used DBH: dD01 = dH =

dVt 3.0 × a × D012 × HD01 dVt

3.0 × a × D012 × HD01

(4a) (4b)

with: ˛ =  × 0.25 × FORM01 × Dw . FORM01 is the ‘true’ form factor (Hohenadl, 1922), which is the relation between stem volume and an ideal column volume over the cross sectional area at D01. It is assumed to be relatively constant with age or tree size and varies approximately between 0.4 and 0.5. HD01 in Eqs. (4a) and (4b) is the ratio between previous height and D01 and  is the number Pi. The update of tree dimension can be applied independent of the time step. Here, we apply the procedure once a year, using the annual cumulative wood growth as input. Harvesting can be indicated for any day in the simulation. Furthermore, it is possible to choose a thinning that removes predominantly small, large or medium sized trees. The parameters used in our simulations are listed in Table 1.

3. Description of sites and simulations 3.1. Sites 3.1.1. Anchor Station Tharandt The Anchor Station Tharandt (50◦ 57 49 N, 13◦ 34 01 E, 380 m a.s.l.) is located in the eastern part of a large forested area (60 km2 ) near the city of Tharandt, about 25 km SW of Dresden, Germany. According to the long-term records (Grünwald and Bernhofer, 2007) of the adjacent weather station (1959–2005), the mean annual air temperature is 7.8 ◦ C (maximum and minimum annual means are 9.4 ◦ C in the year 2000 and 6.0 ◦ C in the year 1996) and the mean annual precipitation is 823 mm (maximum and minimum yearly sums are 1287 mm in the year 1981 and 501 mm in the year 2003). The spruce stand at the Anchor Station was established by seeding in 1887. A special inventory in 1999 focused on the area within a 500 m radius of the flux tower (Grünwald and Bernhofer, 2007) indicated the following stand inventory: tree density was 477 trees ha−1 , the above ground biomass 213 t ha−1 , mean canopy height 26.5 m, and mean breast height diameter 33 cm, one-sided leaf area index was estimated to be 7.6, and the ground was mainly covered by young Fagus sylvatica (20%) and Deschampsia flexuosa (50%). The canopy is now dominated by coniferous evergreen species (72% Picea abies, 15% Pinus sylvestris) with a small percentage of deciduous species present (10% Larix decidua, 1% Betula spec., 2% others). In March/April 2002 a thinning resulted in a reduction of tree density, LAI and basal area (30, 11 and 14%, respectively) as well as in an increase of mean diameter at breast height (11%, Gerold, 2004). The exported wood volume from the thinning was 43 m3 ha−1 . 3.1.2. State Forest Hesse (see also Granier et al., 2000, 2008) The experimental plot (Euroflux site FR02) is in the state forest of Hesse, France (48◦ 40N, 7◦ 05E), in a stand composed mainly (90%) of naturally established ca. 40-yr-old European beech (Fagus sylvatica L.). The stand is on a site of good productivity in the first class of Schober’s yield table for Beech. The experimental plot (0.6 ha) was in the central part of a 65 ha area composed mainly of 30–60-yrold Beech. The plot and the surrounding stands were thinned 2 years before C flux measurements began. Two other thinnings were performed in 1999 and 2005. Soil water content was periodically measured (7–20 days) using a neutron probe in eight 1.6–2.6 m long aluminum tubes distributed in the experimental plot. Periodic girth measurements (1–3 weeks frequency) were performed on a 500-tree sample. Volume and biomass at tree and stand scales were derived from circumference measurements using allometric relationships established on 20 trees from the same stand. 3.1.3. State Forest Puéchabon The study site is located 35 km NW of Montpellier (Southern France) in the Puéchabon State Forest (3◦ 35 45 E, 43◦ 44 29 N, 270 m a.s.l.) on a flat plateau (see also e.g. Allard et al., 2008). The soil type is a clay-loam above limestone and has a high stone fraction (75% in the upper 50 cm, 90% below) so that the storage capacity of plant available water within the whole profile of 4.5 m is only approximately 150 mm. The mean annual rainfall and temperature are 902 mm and 13.2 ◦ C for the period of 1984–2009. This holm oak (Quercus ilex L.) forest was managed as a coppice for centuries and was submitted to the last clear cut in 1942. It has a dense canopy with a maximum height of approximately 6 m. Soil water storage was measured from 10 cm down to 450 cm depth in intervals of 20 cm using a neutron moisture gauge (CPN503 Campbell Pacific) with 6 repetitions per depth. Stem diameter at breast height (DBH) was measured with a diameter tape every winter from 1984 on 436

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Table 1 Parameters for tree growth model (DW = dry weight, FW = fresh weight). Parameter Fugw Dw Pbra fBRAF DBHmax FORM01 a b c d e *

Description

Spruce −1

Below ground wood fraction (kgDW kgDW ) Wood density (kgDW dm FW−3 ) Empirical parameter for branchwood estimation of small trees (−) Final branchwood fraction of total aboveground wood (kgDW kgDW−1 ) Diameter at which the final branch fraction is reached (m) Stem volume relative to volume of a column with diameter at 0.1 tree height (0–1)

a

0.2 0.41a 70 0.2* 0.8* 0.5

Beech b

0.24 0.58d 70 0.18b 0.4a 0.5

Holm oak 0.49c 0.90c 130* 0.6* 0.25e 0.5

Bossel (1996). Granier et al. (2000). Unpublished data. Grote et al. (2003). Canadell et al. (1988). Derived from biomass measurements at the sites of this study.

trees of Q. ilex larger than 1 cm DBH and present in nine subplots situated in a 300 m radius around the flux tower. 3.2. Eddy flux measurements For all sites the calculation and correction of turbulent fluxes derived by the EC method were based on a general EUROFLUX methodology (Aubinet et al., 2000). Gap filling and flux partitioning was performed by the “Online Eddy-Covariance Data Gap-Filling and Flux-Partitioning Tool” (Reichstein et al., 2005) including u*-correction to replace unreliable data due to low turbulent conditions. The gap filling algorithm was based on methods described by Falge et al. (2001) and merges the sensitivity of meteorological variables with the auto-correlation of fluxes in time (Reichstein et al., 2005). This procedure provided consistent data treatment. At Puéchabon, high frequency losses due to the closed-path system were quantified by spectral analysis and the corresponding corrections (6% for H2 O and 1% for CO2 ) applied (Allard et al., 2008). 3.3. Simulation setup All simulations were run with a 1-hour base-time step. Although for most of the simulated periods, measured hourly or even halfhourly values would have been available, the hourly data were calculated from aggregated daily values (see Section 2.1). The daily input represents a widely available and reliable data source used in many ecosystem models (e.g. PnET, 3-PG, BGC, and families; Aber and Federer, 1992; Landsberg and Waring, 1997; Prentice et al., 1992) and enables computational efficient simulation runs. The simulation results were compared with results obtained using halfhourly measured input data for single years but the differences were small (not shown). We initialized the stands with the first inventory measurements. Similarly, the soil profiles were initialized with measurements from the earliest investigations. The soil properties provided in the model initialization included field capacity, wilting point, soil organic carbon content, bulk density, and stone content distribution for the whole soil profile (= rooted soil). Climate data input into the model included the daily representations of global radiation, mean air temperature, relative humidity, and precipitation provided from the CARBO-EUROPE database (Level 4 aggregation from half hourly measurements and standard gap filling routines). The background carbon dioxide air concentration was assumed to be 370 ppm and constant throughout the simulation period. Total annual nitrogen deposition for these sites was converted into a virtual concentration value of precipitation (total annual deposition in kg N ha−1 /total average annual precipitation in mm ha−1 ) based on values reported in the literature. The resulting load of nitrogen per rainfall event was supplied into the uppermost soil layer.

Assuming that the physiological processes can be well represented by the selected modelling approach, we used the Eddy-covariance data measured at the sites and adjusted the two main physiological parameters that determine photosynthesis and respiration in the PSIM model (Fig. 2). The adjusted parameters were the saturated carboxylation capacity (VCmax , see Farquhar et al., 1980 for further description) and the maintenance respiration coefficient at reference temperature (KMmax , see Thornley and Cannell, 2000 for further descriptions). The adjustment was carried out by iteratively starting at 0.1 for KMmax and the lowest values for each species found in the literature for VCmax (these were for beech 27.1 ␮mol m−2 s−1 Medlyn et al., 2002, 21.2 ␮mol m−2 s−1 Bergh et al., 2003 for spruce, and 31.0 ␮mol m−2 s−1 Pena-Rojas et al., 2004 for oak) and increasing these stepwise (with a step width of 0.5 ␮mol m−2 s−1 for VCmax and 0.1 for KMmax ) until the slope of simulated vs. measured values was one (±0.005). Additionally, we used measured soil water and forest inventory data for further model evaluation (Figs. 3 and 4). Soil water content, the major driver for primary production, is also a key variable driving C and N mineralization and thus nutrient availability. Forest inventories are the integrated measure of aboveground carbon accumulation and are easy to measure, making them a valuable general source of information. The periods for which these data sets were available are given directly in the figures. We ran the model with and without thinning operations in order to investigate the impact of slow stand development on small-scale processes (and their feedback to forest growth) (Fig. 5).

4. Results The parameters obtained from the calibration/adjustment procedure were VCmax = 60, 55, and 46 ␮mol m−2 s−1 and KMmax = 0.25, 0.27, and 0.20 [dimensionless] for spruce (Tharandt), beech (Hesse), and oak (Puchéabon), respectively. These were well within the range observed in field measurements (see Medlyn et al., 2002). For example very similar VCmax values have been reported for spruce (e.g. Grassi and Bagnaresi, 2001), beech (e.g. Op de Beeck et al., 2007), and holm oak (e.g. Reichstein et al., 2003). However, the adjusted respiration activity parameters were considerably higher than the value of 0.1 recommended by Thornley and Cannell (2000). Forest inventory properties of all three stands revealed some uncertainties in the carbon balance (see Fig. 4). The absolute difference of simulated annual woody aboveground biomass increment compared to the biomass estimated from tree measurements was 72.5 gC m−2 a−1 (+26%), −14.6 gC m−2 a−1 (−4.0%), and −8.5 gC m−2 a−1 (−11%) for Tharandt, Hesse, and Puchéabon, respectively. These differences were especially large for the Tharandt site. However this site also had the highest measurement uncertainties since these were derived from a selection of stem

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16

GPP, Tharandt

simulated [gC m -2 day-1 ]

simulated [gC m -2 day-1 ]

25 20 15 10

R² = 0.79

5

2007: y = 1.145x 0

5

-5

10

20

simulated [gC m -2 day-1 ]

R² = 0.8515

2004: y = 1.2176x 0

5

10

15

6 4

R² = 0.7606

2

2005: y = 0.9001x 0

2

4

-5

measured [gC m -2 day-1 ]

12

GPP, Puechabon

simulated [gC m -2 day-1 ]

6 4

R² = 0.6288 2005: y = 1.2183x 0

2

4

6

8

10

12

14

16

8 6 4 R² = 0.6995

2

2008: y = 0.9287x 0

2

4

6

8

10

12

measured [gC m -2 day-1 ]

8

-2

8

TER, Hesse

TER, Puechabon

8

2

6

10

0

20

10

-2

8

12

GPP, Hesse

5

0

10

measured [gC m -2 day-1 ]

10

0

12

0

25

15

-5

14

measured [gC m -2 day-1 ]

20

simulated [gC m -2 day-1 ]

15

simulated [gC m -2 day-1 ]

0

-5

TER, Tharandt

10

7 6 5 4 3 2

0

12

R² = 0.4861

1

2006: y= 1.1365x 0

1

measured [gC m -2 day-1 ]

2

3

4

5

6

7

8

measured [gC m -2 day-1 ]

Fig. 2. Measured vs. simulated daily gross primary production (GPP) and ecosystem respiration (TER) fluxes for all three investigated sites. The year with the highest GPP overestimation due to impacts not covered in the model (see Section 5) and the year with the highest TER bursts that lead to underestimations in simulations are indicated as red dots. Lines show the slope of 1 and represent the trend line of all points. The slope of the point representing the exceptional year is given in red colour. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of the article.)

Tharandt (0-100 cm) 1998-2006

25

simulated (vol. %)

simulated (vol. %)

45

20 15 10 y = 1.0172x R² = 0.4664

5 0

0

5

10

15

20

25

measured (vol. %)

Hesse (0-50 cm) 1997-2002

B

40 35 30 25 y = 0.9762x R² = 0.9064

20 30

30

15

15

20

25

30

35

40

45

simulated (vol. %)

A

30

C

Puechabon (0-400 cm) 1998-2006

25 20 15 10 y = 1.0183x R² = 0.8038

5 0

0

5

measured (vol. %)

Fig. 3. Measured vs. simulated soil water content at the three selected forest sites.

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185

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Fig. 4. Measured (points) and simulated (lines) stand growth parameters at the three selected forest sites. Woody biomass includes all aboveground parts of the stem and branches.

core analyses and not from dimensional measurements of all individuals. 4.1. Tharandt Although the parameters were adjusted for the whole simulation period, gas fluxes as well as aboveground woody biomass growth were very well represented (Table 2, Fig. 4). Gross primary production in summer was slightly overestimated which was compensated by an underestimation in winter and spring (Normalized Mean Bias, NMB = 0.08). Similarly, high total ecosystem respiration ‘bursts’ on some summer days could not be represented, leading to a slightly biased simulation (NMB = 0.07). The differences in GPP as well as TER were more concentrated in particular years. For example, only about 6–10 days with exceptional respiration rates in 2005 were responsible for a calculated bias of approximately 10% that year (Fig. 2). Simulated monthly and annual carbon flux components were still well in accordance with those derived from measurements (Table 2, Fig. 5). Compared with the correlation based on daily integrated values, the correlation measures (for daily deviations) were better when integrated on a monthly basis (since high deviations at single days are averaged out) but less good when integrated on an annual basis (due to less sample points). Nevertheless, a few years showed overestimated GPP- (i.e. 2001, 2007) and TER fluxes (2001–2003, 2007). In some years these overestimations compensated each other, leading to a closer match between measured and simulated net ecosystem exchange (NEP) than obtained for GPP.

The impact of thinning in 2002, which led to a reduction of leaf area by approximately 10%, seemed to be fully compensated by improved climate conditions. However, the dry years in 2003 and 2006, including their after-effects, lead to a decreased growth that was not completely covered by the model. Consequently a masked growth depression due to the loss of trees cannot completely be excluded. 4.2. Hesse The GPP fluxes at Hesse were more influenced by drought than observed at the spruce stand in Tharandt (Fig. 3; see also Granier et al., 2007). The GPP fluxes were generally well-represented by the model, resulting in similar correlation and error measures (Table 2) and an even smaller bias than those at Tharandt (NMB for GPP = −0.02 and for TER = 0.002). Exceptions to this improved performance were the years 2004, 2007, and 2008. In 2004 (the year following the exceptional dry year 2003), GPP, TER, and NEP were overestimated (Fig. 2). This phenomenon was likely a result of irreversible physiological damages and has been described before as the ‘lagged effect of drought’ (Granier et al., 2007, 2008) or ‘carryover effects’ (Thomas et al., 2009). The model error for this year was 403 g C m−2 (+35%) in GPP and 231 g C m−2 (+30%) in TER. These errors compensated for each other to some degree resulting in a 172 g C m−2 (+47%) overestimation for NEP. Interestingly, an overestimation of NEP was also obtained for the very moist year 2007 (132 g C m−2 , +21%). However, in this case the difference originated from the added effect of a GPP overestimation (43 g C m−2 , +2%)

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and a TER underestimation (−98 g C m−2 , −8%). In 2008, the underestimation of TER was similar (Fig. 4, −87 g C m−2 ) but the effect on NEP was smaller because the GPP estimations were well in line with measurements. The impact of the thinning in 2005 resulted in an even stronger decrease in GPP and NEP than calculated for the spruce forest (app. 200 and 300 g C m−2 , respectively). However, the faster growth and canopy closure compensated for the effect after only 3 years (Fig. 5).

4.3. Puéchabon At the warmest site of this study fluxes were more heterogeneous. Interestingly, GPP fluxes in summer that were highly drought-impacted were well met (see Fig. 3), while the large heterogeneity during the winter months was not as well simulated. Also, early summer fluxes in 2005 and 2006 were overestimated.

Both effects lead to a slightly smaller correlation and larger error measures (Table 2) as well as a slightly larger bias (NMB for GPP = −0.8 and for TER = −0.08) than at the other sites. As mentioned before, ‘respiration burst’ were underestimated but compensated by overestimations in spring (2006, 2007, 2008) and summer when the impact of drought on respiration is not quite as well represented than in case of GPP simulation. At the annual scale, the overestimation of GPP in 2005 and 2006 (Fig. 5) is approximately 150–200 g C m−2 per year, although the average annual simulated GPP was only about 5% higher than that estimated from measurements. The higher GPP was more than compensated for in the NEP by the overestimation of respiration. 5. Discussion Sophisticated physiologically based models have been criticized because the underlying causes for their results are hard to

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187

Table 2 Correlation coefficient and error measures given for daily fluxes of GPP, TER, and NPP from daily, monthly and annual integration steps for each investigated site. R2 values are derived without forcing through the zero point which explains the differences to values indicated in Fig. 2. Tharandt

Hesse

Puéchabon

GPP

TER

NPP

GPP

TER

NPP

GPP

TER

NPP

Daily R2 SE ME RMSE

0.81 1.81 −0.34 1.58

0.81 1.05 −0.27 0.91

0.43 1.69 −0.07 1.55

0.85 1.91 −0.11 2.03

0.70 1.05 −0.01 1.25

0.78 1.60 −0.10 1.61

0.65 1.22 −0.47 1.34

0.61 0.87 −0.37 0.91

0.42 1.17 −0.09 1.23

Monthly R2 SE ME RMSE

0.92 1.05 −0.34 1.10

0.90 0.70 −0.27 0.75

0.72 0.90 −0.07 0.95

0.93 1.27 −0.11 1.37

0.81 0.77 −0.01 0.94

0.91 0.95 −0.10 0.95

0.52 1.25 −0.43 1.51

0.47 0.99 −0.35 1.07

0.49 0.70 −0.08 0.87

0.42 0.34 0.34 0.50

0.21 0.32 0.27 0.41

0.18 0.19 0.07 0.27

0.69 0.43 0.10 0.40

0.67 0.31 −0.04 0.34

0.74 0.17 0.14 0.24

0.47 0.40 0.16 0.37

0.63 0.20 0.15 0.21

0.13 0.30 0.01 0.26

Annual R2 SE ME RMSE

understand (Van Nes and Scheffer, 2005). The main reasons for this have been highlighted by Medlyn et al. (2005) as equifinality, insensitivity and uncertainty. We tried to minimize these problems by combining established models where most parameters for the species of interest were available from independent sources or derived from previous investigations. Furthermore, we evaluated the same model with measurements of various ecosystem pools and fluxes (water availability, carbon gain, respiration, stem growth and mortality); covering a wide range of climatic conditions and scales. Nevertheless, it has to be considered that the inherent uncertainty in the parameters used and the inevitable equifinality that might evolve from the uncertainty of parametercounterparts have not been investigated here. It thus remains to be shown how robust the parameter set is for other sites and if any key variables can be defined to simplify the model (Larocque et al., 2008). Despite the overall good agreement between simulations and measurements, deviations indicated that particular modelling deficiencies need to be addressed and addressing these deficiencies might spur improvements in future models and guide further experimental designs and the choice of measurements. We noted that GPP simulations were too high at the site Hesse in the year 2004 and – to a smaller degree – in the year 2007 (Figs. 2 and 5): 2004 was the year after the stand experienced the most severe drought in the simulation period, and 2007 was an exceptionally humid year at the after the 2003–2006 dry years (Granier et al., 2008). This supports the assumption of Verbeeck et al. (2008) that a long-term effect of drought is the most important driver of the inter-annual variability in GPP. The particular process responsible for this phenomenon is still unclear but may be related to an increased allocation into carbohydrate storages after depletion in the previous year(s) (Barbaroux and Bréda, 2002). The defoliation by insects in spring at Puéchabon was the most probable reason for the overestimated GPP flux in spring 2005 at this site (Fig. 2). An environmental impact that is particularly important for evergreen species is the consideration of stress induced impacts on the biochemistry of leaves. In our simulations, we considered the low temperatures in winter that initiate a dormancy effect for spruce species as described in Bergh et al. (1998). The inclusion of this process, however, was not able to improve the representation of the variable assimilation rates in winter at the Puéchabon site. The after-effects of drought that are reported to extend over several days (Bengtson, 1980) are a possible reason for the overestimation of GPP at Puéchabon in spring and early summer 2006 when rainfall was very low (February through July of only

97 mm – less than one third of the 323 mm recorded for an average year) (Fig. 2). With respect to respiration, we have to admit that even the combined soil process/physiology model was not able to fully represent the large variability and sometimes high respiration ‘bursts’ that were evident in the measurements. This may be related to the socalled ‘Birch Effect’ that describes unusual respiration rates from mineralization after re-wetting of dry soils (see Jarvis et al., 2007). This effect has been particularly observed in dry climates of Africa and the Mediterranean region but might be responsible also for occasional bursts observed at the temperate sites of Hesse and Tharandt (Fig. 2). It is, however, also possible that these observations are more related to the errors in the eddy flux measurements or the gap-filling routines for these measurements rather than to a missing model sensitivity. More obviously related to a model deficiency is the overestimation of respiration in the years of harvest. Even if the day of harvest was captured correctly, the supply of material for mineralization into the soil was immediate in the model while it takes a longer period of time for the piled debris from tree harvest to be actually integrated into the litter and soil layers. Another uncertainty in the simulation was the relatively high respiration parameter obtained by adjusting overall respiration at the sites. It might be suspected that the autotrophic respiration was overestimated by the model because heterotrophic respiration was not evaluated. Until further measurements of the heterotrophic respiration become available to improve the models, we rely on evaluations of soil respiration simulated by the DNDC model from other sites (e.g. Kurbatova et al., 2008). The simulations also demonstrate a statistical scaling problem. Although the fluxes show a good correlation on a daily basis, annual sums are less well represented. In the current exercise, relatively few very high respiration events caused a slight overestimation of the respiration parameter. Consequently, respiration was overestimated in years were these respiration rates were seldom or non-existent. This was especially a problem at the Puéchabon site where the correlation between measurements and simulation results was relatively low (see Reichstein et al., 2002 for further discussion of respiration processes). The measurements of gas exchange used in this study have already been presented elsewhere (Allard et al., 2008; Grünwald and Bernhofer, 2007; Granier et al., 2008). A fraction of these data (usually 3–5 years) have been used for the evaluation of biosphere models at all three sites (e.g. Churkina et al., 2003; Davi et al., 2005, 2006; Verbeeck et al., 2008; Wang et al., 2003) but thinning impacts have never been included in the analyses. Verbeeck et al. (2008)

188

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Table 3 Simulated carbon sinks considering natural mortality with thinning (nat. + thin.) and without thinning (nat.). Given are the average annual values (g C m−2 ) for a 10-year period (1999–2008) of each site together with the standard variation (sd) between the years. Carbon transported out of the forest by thinning during the 10-year period is separately indicated as ‘export’ for Tharandt and Hesse. (Numbers in bold typing are net gain, in- and outflow of carbon; normal typing indicates sub-divisions of bold numbers.). Tharandt

GPP Respiration Soil (heterotroph) Vegetation Below ground Above ground NEP Soil Vegetation Below ground Above ground Export

Hesse

Puéchabon

avg (nat. + thin.)

sd

avg (nat.)

sd

avg (nat. + thin.)

sd

avg (nat.)

sd

avg (nat.)

sd

1995 −1397 −561 −835 −908 −489 599 176 305 244 241 −117

175 94 48 66 68 34 381 131 500 49 379 –

2024 −1372 −524 −849 −876 −496 652 174 478 279 373 0

149 85 42 52 64 27 82 39 91 35 62 0

1651 −1146 −406 −740 −632 −514 505 146 116 174 88 −243

159 64 78 63 61 40 602 194 775 80 579 –

1719 −1125 −342 −783 −594 −531 593 158 436 262 331 0

126 54 28 36 32 26 91 42 94 44 71 1

1393 −1133 −402 −731 −604 −529 260 121 144 188 72 0

149 89 37 70 46 50 82 48 49 64 28 –

already pointed out that this was a weak point in their long-term simulations at Hesse forest. Davi et al. (2006) systematically overestimated aboveground growth although NEP was generally well representing, indicating export of wood may play an important part in the ecosystem balance. In fact, the overestimation at Hesse beech forests matches almost exactly the carbon amount that we calculated to be the average annual export at this site. It should also be noted that consideration of forest thinning also includes an occasionally increased litter fall since leaves, roots and branches of the felled trees are usually not removed from the forest. This effect on mineralization and soil respiration has usually not been considered. Our results indicate that they may be at least partly responsible for the generally poor representation of ecosystem respiration processes (e.g. Churkina et al., 2003). As shown in Table 3, a considerable proportion of the net carbon fixation was attributed to soil storages. Due to the relatively large amount of foliage and fine root senescence this was greatest for the spruce stand in Tharandt (30%), least for the beech stand in Hesse (8%) and intermediate for the Mediterranean oak stand (18%). If living parts of the trees are considered to be belonging to the soil, the model estimates 74% of the oak stand’s NEP was below ground. This was due to a high coarse root proportion of app. 50% of total woody biomass (Hoff et al., 2002, see Table 1) and a high fine root turnover which was approximately 3-fold that of the other species (Lopez et al., 1998). For the spruce and the beech forests we estimated 49 and 32% total below ground allocation under unthinned conditions. This is consistent with general estimates for European forests (see Janssens et al., 2003 and literature therein) and a little bit lower than estimates for old growth forests (Luyssaert et al., 2008). The results showed that a large portion of NEP can be exported by harvest in young as well as old stands. Thinning decreased the relationship between NEP and GPP from 32 to 30% (spruce) and 35 to 31% (beech) within the investigated decade. This was mainly due to increased litter availability and thus higher heterotrophic respiration. A large proportion of the NEP was sequestered in the woody compartment (57% for spruce and 56% for beech) from which approximately 30 (spruce) and 69% (beech) was exported by thinning. Of the fraction of NEP that stayed in the forest, 40% (spruce) and 17% (beech) was distributed below ground between 13 and 34% less than when without thinning. The larger above ground proportion originated from the additional buds and foliage needed to close the gaps originating from thinning. When considering these results it should be noted that the beech stand was younger than the spruce stand and the approximate decrease of 30% of the stems was distributed into two management events within the investigated decade while the larger spruce stems were reduced by 30% in one event.

6. Conclusions The MoBiLE-DNDC, equipped with new forest dimensional routines, has been shown suitable to simulate carbon fluxes in various types of pure forests, ranging from young to old, and covering needle- and broad leaved evergreen as well as deciduous species. The simulation was carried out continuously for more than ten years on a hourly time step with climatic driving data derived from daily values. Since the model accounts for losses from thinning, it was possible to quantify real management impacts on the carbon balance. The integration of small and large scale processes also enabled the quantification of feedbacks of management effects that included an increased above ground growth due to more space and available resources. To our knowledge this is the first such exercise. Acknowledgements Modelling was supported by the DFG Project ‘Modeling of beech-dominated deciduous forest development based on competitive mechanisms of water and nitrogen partitioning (Bu 1173/8-1). Eddy fluxes at all sites were supported by CARBOEUROPE-IP (GOCE-CT-2003-505572) with additional support by the European Commission projects MEDEFLU (ENV4-CT98-0455) and CARBOEUROFLUX (EVK2-CT-1999-00032) at the Puéchabon site and French GIP-Ecofor at the Hesse site. We additionally thank the technical staff in Tharandt (Uwe Eichelmann, Heiko Prasse, Horst Hebentanz and Udo Postel) very much for their help and support in guaranteeing continuous high quality data. Finally, we thank Richard Grant (Purdue Climate Change Research Centre) for improving the English of this manuscript. References Aber, J.D., Federer, C.A., 1992. A generalized, lumped parameter model of photosynthesis, evaporation and net primary production in temperate and boreal forest ecosystems. Oecologia 92, 463–474. Allard, V., Ourcival, J.M., Rambal, S., Joffre, R., Rocheteau, A., 2008. Seasonal and annual variation of carbon exchange in an evergreen Mediterranean forest in southern France. Global Change Biol. 14, 714–725. Aubinet, M., Grelle, A., Ibrom, A., Rannik, Ü., Moncrieff, J., Foken, T., Kowalski, A.S., Martin, P.H., Bernhofer, C., Clement, R., Elbers, J., Granier, A., Grünwald, T., Morgenstern, K., Pilegaard, K., Rebmann, C., Snijders, W., Valentini, R., Vesala, T., 2000. Estimates of the annual net carbon and water exchange of forests: the EUROFLUX methodology. Adv. Ecol. Res. 30, 113–175. Ball, J.T., Woodrow, I.E., Berry, J.A., 1987. A model predicting stomatal conductance and its contribution to the control of photosynthesis under different environmental conditions. In: Biggins, J. (Ed.), Progress in Photosynthesis Research. Martinus-Nijhoff Publishers, Dordrecht, The Netherlands, pp. 221–224. Barbaroux, C., Bréda, N., 2002. Contrasting distribution and seasonal dynamics of carbohydrate reserves in stem wood of adult ring-porous sessile oak and diffuseporous beech trees. Tree Physiol. 22, 1201–1210.

R. Grote et al. / Agricultural and Forest Meteorology 151 (2011) 179–190 Bengtson,C., 1980. Ecological Bulletins No. 32, Structure and Function of Northern Coniferous Forests: An Ecosystem Study. Oikos Editorial Office Stable, Stockholm, pp. 205–213. Bergh, J., McMurtrie, R.E., Linder, S., 1998. Climatic factors controlling the productivity of Norway spruce: a model-based analysis. For. Ecol. Manage. 110, 127–139. Bergh, J., Freeman, M., Sigurdsson, B.D., Kellomäki, S., Laitinen, K., Niinistö, S., Peltola, H., Linder, S., 2003. Modelling the short-term effects of climate change on the productivity of selected tree species in Nordic countries. For. Ecol. Manage. 183, 327–340. Berninger, F., 1994. Simulated irradiance and temperature estimates as a possible source of bias in the simulation of photosynthesis. Agric. Forest Meteorol. 71, 19–32. Bossel, H., 1996. TREEDYN3 forest simulation model. Ecol. Model. 90, 187–227. Butler, I.W., Riha, S.J., 1989. GAPS: A General Purpose Formulation Model of the Soil–Plant–Atmosphere System Version 1.1 User’s Manual. Department of Agronomy, Cornell University, Ithaca, New York. Butterbach-Bahl, K., Kahl, M., Mykhayliv, L., Werner, C., Kiese, R., Li, C., 2009. A European-wide inventory of soil NO emissions using the biogeochemical models DNDC/Forest-DNDC. Atmos. Environ. 43, 1392–1402. Canadell, J., Riba, M., Andres, P., 1988. Biomass equations for Quercus ilex L. in the Montseny Massif, Northeastern Spain. Forestry 61, 137–147. Churkina, G., Tenhunen, J., Thornton, P., Falge, E.M., Elbers, J.A., Erhard, M., Grünwald, T., Kowalski, A.S., Rannik, Ü., Sprinz, D.F., 2003. Analyzing the ecosystem carbon dynamics of four European coniferous forests using a biogeochemistry model. Ecosystems 6, 168–184. Davi, H., Dufrene, E., Granier, A., Le Dantec, V., Barbaroux, C., Francois, C., Bréda, N., 2005. Modelling carbon and water cycles in a beech forest. Part II: Validation of the main processes from organ to stand scale. Ecol. Model. 185, 387–405. Davi, H., Dufrene, E., Francois, C., Le Maire, G., Loustau, D., Bosc, A., Rambal, S., Granier, A., Moors, E., 2006. Sensitivity of water and carbon fluxes to climate changes from 1960 to 2100 in European forest ecosystems. Agric. Forest Meteorol. 141, 35–56. De Bruijn, A.M.G., Butterbach-Bahl, K., Blagodatsky, S., Grote, R., 2009. Model evaluation of different mechanisms driving freeze–thaw N2 O emissions. Agric. Ecosyst. Environ. 133, 196–207. De Wit, C.T., Goudriaan, J., van Laar, H.H., Penning de Vries, F.W.T., Rabbinge, R., van Keulen, H., 1978. Simulation of Assimilation, Respiration and Transpiration of Crops. Centre for Agricultural Publishing and Documentation (Pudoc), Wageningen, The Netherlands. Deckmyn, G., Verbeeck, H., Op de Beeck, M., Vansteenkiste, D., Steppe, K., Ceulemans, R., 2008. ANAFORE: a stand-scale process-based forest model that includes wood tissue development and labile carbon storage in trees. Ecol. Model. 215, 345–368. Duursma, R.A., Kolari, P., Peramaki, M., Pulkkinen, M., Mäkelä, A., Nikinmaa, E., Hari, P., Aurela, M., Berbigier, P., Bernhofer, Ch., Grünwald, T., Loustau, D., Molder, M., Verbeeck, H., Vesala, T., 2009. Contributions of climate, leaf area index and leaf physiology to variation in gross primary production of six coniferous forests across Europe: a model-based analysis. Tree Physiol. 29, 621–639. Engel, Th., Priesack, E., 1993. Expert-N, a building-block system of nitrogen models as resource for advice, research water management and policy. In: Eijsackers, H.J.P., Hamers, T. (Eds.), Integrated Soil and Sediment Research: A Basis for Proper Protection. Kluwer Academic, Dodrecht, Netherlands, pp. 503–507. Falge, E., Baldocchi, D., Olson, R., Anthoni, P., Aubinet, M., Bernhofer, C., Burba, G., Ceulemans, R., Clement, R., Dolman, H., Granier, A., Gross, P., Grünwald, T., Hollinger, D., Jensen, N.O., Katul, G., Keronen, P., Kowalski, A., Lai, C.T., Law, B.E., Meyers, T., Moncrieff, J., Moors, E., Munger, J.W., Pilegaard, K., Rannik, Ü., Rebmann, C., Suyker, A., Tenhunen, J., Tu, K., Verma, S., Vesala, T., Wilson, K., Wofsy, S., 2001. Gap filling strategies for defensible annual sums of net ecosystem exchange. Agric. Forest Meteorol. 107, 43–69. Farquhar, G.D., von Caemmerer, S., Berry, J.A., 1980. A biochemical model of photosynthetic CO2 assimilation in leaves of C3 species. Planta 149, 78–90. Friend, A.D., Stevens, A.K., Knox, R.G., Cannell, M.G.R., 1997. A process-based, terrestrial biosphere model of ecosystem dynamics (Hybrid v3.0). Ecol. Model. 95, 249–287. Gerold, D., 2004. Zuwachs und Ertrag der Fichte. AFZ/Der Wald 22, 1223–1226. Granier, A., Reichstein, M., Bréda, N., Janssens, I.A., Falge, E., Ciais, P., Grünwald, T., Aubinet, M., Berbigier, P., Bernhofer, C., Buchmann, N., Facini, O., Grassi, G., Heinesch, B., Ilvesniemi, H., Keronen, P., Knohl, A., Köstner, B., Lagergren, F., Lindroth, A., Longdoz, B., Loustau, D., Mateus, J., Montagnani, L., Nys, C., Moors, E., Papale, D., Peiffer, M., Pilegaard, K., Pita, G., Pumpanen, J., Rambal, S., Rebmann, C., Rodrigues, A., Seufert, G., Tenhunen, J., Vesala, T., Wang, Q., 2007. Evidence for soil water control on carbon and water dynamics in European forests during the extremely dry year: 2003. Agric. Forest Meteorol. 143, 123–145. Granier, A., Bréda, N., Longdoz, B., Gross, P., Ngao, J., 2008. Ten years of fluxes and stand growth in a young beech forest at Hesse, North-eastern France. Ann. For. Sci. 64, doi:10.1051/forest:2008052. Granier, A., Ceschia, E., Damesin, C., Dufrene, E., Epron, D., Gross, P., Lebaube, S., Dantec, V.L., Le Goff, N., Lemoine, D., Lucot, E., Ottorini, J.-M., Pontailler, J.Y., Saugier, B., 2000. The carbon balance of a young Beech forest. Funct. Ecol. 14, 312–325. Grassi, G., Bagnaresi, U., 2001. Foliar morphological and physiological plasticity in Picea abies and Abies alba saplings along a natural light gradient. Tree Physiol. 21, 959–967. Grote, R., 1998. Integrating dynamic morphological properties into forest growth modeling. II. Allocation and mortality. For. Ecol. Manage. 111, 193–210.

189

Grote, R., Schuck, J., Block, J., Pretzsch, H., 2003. Oberirdische holzige Biomasse in Kiefern-/Buchen- und Eichen-/Buchen-Mischbeständen. Forstw. Cbl. 122, 287–301. Grote, R., 2007. Sensitivity of volatile monoterpene emission to changes in canopy structure – a model based exercise with a process-based emission model. New Phytol. 173, 550–561. Grote, R., 2003. Estimation of crown radii and crown projection area from stem size and tree position. Ann. For. Sci. 60, 393–402. Grote, R., De Bruijn, A.M.G., Kiese, R., 2008. A New Modular Biosphere Simulation Framework. Geophysical Research Abstracts, vol. 10. European Geoscience Union, EGU General Assembly, Vienna, 4-18-2008. Grote, R., Lavoir, A.V., Rambal, S., Staudt, M., Zimmer, I., Schnitzler, J.-P., 2009a. Modelling the drought impact on monoterpene fluxes from an evergreen Mediterranean forest canopy. Oecologia 160, 213–223. Grote, R., Lehmann, E., Brümmer, C., Brüggemann, N., Szarzynski, J., Kunstmann, H., 2009b. Modelling and observation of biosphere–atmosphere interactions in natural savannah in Burkina Faso, West Africa. Phys. Chem. Earth 34, 251–260. Grünwald, T., Bernhofer, C., 2007. A decade of carbon, water and energy flux measurements of an old spruce forest at the Anchor Station Tharandt. Tellus B 59, 387–396. Hoff, C., Rambal, S., Joffre, R., 2002. Simulating carbon and water flows and growth in a Mediterranean evergreen Quercus ilex coppice using the FOREST-BGC model. For. Ecol. Manage. 164, 121–136. Hohenadl, W., 1922. Neue Grundlagen der Holzmessung. Forstw. Cbl. Holst, J., Grote, R., Offermann, C., Ferrio, J.P., Gessler, A., Mayer, H., Rennenberg, H., 2010. Water fluxes within beech stands in complex terrain. Int. J. Biometeorol. 54, 23–36. Huth, N.I., Carberry, P.S., Poulton, P.L., Brennan, L.E., Keating, B.A., 2003. A framework for simulating agroforestry options for the low rainfall areas of Australia using APSIM. Eur. J. Agron. 18, 171–185. Janssens, I.A., Freibauer, A., Ciais, P., Smith, P., Nabuurs, G.-J., Folberth, G., Schlamadinger, B., Hutjes, R.W.A., Ceulemans, R., Schulze, E.-D., Valentini, R., Dolman, A.J., 2003. Europe’s terrestrial biosphere absorbs 7 to 12% of European anthropogenic CO2 emissions. Science 300, 1538–1542. Jarvis, P., Rey, A., Petsikos, C., Wingate, L., Rayment, M., Pereira, J., Banza, J., David, J., Miglietta, F., Borghetti, M., Manca, G., Valentini, R., 2007. Drying and wetting of Mediterranean soils stimulates decomposition and carbon dioxide emission: the “Birch effect”. Tree Physiol. 27, 929–940. Kesik, M., Brüggemann, N., Forkel, R., Kiese, R., Knoche, R., Li, C., Seufert, G., Simpson, D., Butterbach-Bahl, K., 2006. Future scenarios of N2 O and NO emissions from European forest soils. J. Geophys. Res. 111, doi:10.1029/2005JG000115. Kimmins, J.P., Mailly, D., Seely, B., 1999. Modelling forest ecosystem net primary production: the hybrid simulation approach used in FORECAST. Ecol. Model. 122, 195–224. King, D.A., 2005. Linking tree form, allocation and growth with an allometrically explicit model. Ecol. Model. 185, 77–91. Knijnenburg, A., Matthäus, E., Wenzel, V., 1984. Concept and usage of the interactive simulation system for ecosystems SONCHES. Ecol. Model. 26, 51–76. Korol, R.L., Running, S.W., Milner, K.S., 1995. Incorporating intertree competition into an ecosystem model. Can. J. For. Res. 25, 413–424. Kurbatova, J., Li, C., Varlagin, A., Xiao, X., Vygodskaya, N., 2008. Modeling carbon dynamics in two adjacent spruce forests with different soil conditions in Russia. Biogeosciences 5, 969–980. Landsberg, J., 2003. Physiology in forest models: history and the future. FBMIS 1, 49–63. Landsberg, J.J., Waring, R.H., 1997. A generalised model of forest productivity using simplified concepts of radiation-use efficiency, carbon balance and partitioning. For. Ecol. Manage. 95, 209–228. Larocque, G.R., Bhatti, J.S., Boutin, R., Chertov, O., 2008. Uncertainty analysis in carbon cycle models of forest ecosystems: research needs and development of a theoretical framework to estimate error propagation. Ecol. Model. 219, 400–412. Li, C., Aber, J., Stange, F., Butterbach-Bahl, K., Papen, H., 2000. A process-oriented model of N2 O and NO emissions from forest soils. 1. Model development. J. Geophys. Res. 105, 4369–4384. Li, C., Frolking, S., Frolking, T.A., 1992. A model of nitrous oxide evolution from soil driven by rainfall events: 1. Model structure and sensitivity. J. Geophys. Res. 97, 9759–9776. Liu, J., Peng, C., Dang, Q., Apps, M., Jiang, H., 2002. A component object model strategy for reusing ecosystem models. Comput. Electron. Agric. 35, 17–33. Lopez, B., Sabaté, S., Gracia, C., 1998. Fine roots dynamics in a Mediterranean forest: effects of drought and stem density. Tree Physiol. 18, 601–606. Luyssaert, S., Schulze, E.-D., Borner, A., Knohl, A., Hessenmoller, D., Law, B.E., Ciais, P., Grace, J., 2008. Old-growth forests as global carbon sinks. Nature 455, 213–215. Mäkelä, A., 1997. A carbon balance model of growth and self-pruning in trees based on structural relationships. Forest Sci. 43, 7–24. Mäkelä, A., 2002. Derivation of stem taper from the pipe theory in a carbon balance framework. Tree Physiol. 22, 891–905. McCown, R.L., Hammer, G.L., Hargreaves, J.N.G., Holzworth, D.P., Freebairn, D.M., 1996. APSIM: a novel software system for model development, model testing and simulation in agricultural system research. Agric. Syst. 50, 255–271. Medlyn, B.E., Dreyer, E., Ellsworth, D., Forstreuter, M., Harley, P.C., Kirschbaum, M.U.F., Le Roux, X., Montpied, P., Strassmeyer, J., Walcroft, A., Wang, K., Loustau, D., 2002. Temperature response of parameters of a biochemically based model of photosynthesis. II. A review of experimental data. Plant Cell Environ. 25, 1167–1179.

190

R. Grote et al. / Agricultural and Forest Meteorology 151 (2011) 179–190

Medlyn, B.E., Berbigier, P., Clement, R., Grelle, A., Loustau, D., Linder, S., Wingate, L., Jarvis, P.G., Sigurdsson, B.D., McMurtrie, R.E., 2005. Carbon balance of coniferous forests growing in contrasting climates: model-based analysis. Agric. Forest Meteorol. 131, 97–124. Op de Beeck, M., Löw, M., Verbeeck, H., Deckmyn, G., 2007. Suitability of a combined stomatal conductance and photosynthesis model for calculation of leaf-level ozone fluxes. Plant Biol. (Stutt) 9, 331–341. Pena-Rojas, K., Aranda, X., Fleck, I., 2004. Stomatal limitation to CO2 assimilation and down-regulation of photosynthesis in Quercus ilex resprouts in response to slowly imposed drought. Tree Physiol. 24, 813–822. Peng, C., Liu, J., Dang, Q., Apps, M.J., Jiang, H., 2002. TRIPLEX: a generic hybrid model for predicting forest growth and carbon and nitrogen dynamics. Ecol. Model. 153, 109–130. Prentice, I.C., Cramer, W., Harriso, S.P., Leemans, R., Monserud, R.A., Solomon, A.M., 1992. A global biome model based on plant physiology and dominance, soil properties and climate. J. Biogeogr. 19, 117–134. Priesack, E, Gayler, S., Hartmann, H.P., 2006. The impact of crop growth sub-model choice on simulated water and nitrogen balances. Nutr. Cycl. Agroecos. 75, 1–13. Reichstein, M., Tenhunen, J.D., Roupsard, O., Ourcival, J.-M., Rambal, S., Dore, S., Valentini, R., 2002. Ecosystem respiration in two Mediterranean evergreen Holm Oak forests: drought effects and decomposition dynamics. Funct. Ecol. 16, 27–39. Reichstein, M., Tenhunen, J.D., Roupsard, O., Ourcival, J.M., Rambal, S., Miglietta, F., Peressotti, A., Pecchiari, M., Tirone, G., Valentini, R., 2003. Inverse modeling of seasonal drought effects on canopy CO2 /H2 O exchange in three Mediterranenan ecosystems. J. Geophys. Res. Atmos. 108, 1–16. Reichstein, M., Falge, E., Baldocchi, D., Papale, D., Aubinet, M., Berbigier, P., Bernhofer, C., Buchmann, N., Gilmanov, T., Granier, A., Grünwald, T., Havranokova, K., Ilvesniemi, H., Janous, D., Knohl, A., Laurila, T., Lohila, A., Loustau, D., Matteucci, G., Meyers, T., Miglietta, F., Ourcival, J.-M., Pumpanen, J., Rambal, S., Rotenberg, E., Sanz, M., Tenhunen, J., Seufert, G., Vaccari, F., Vesala, T., Yakir, D., Valentini, R., 2005. On the separation of net ecosystem exchange into assimilation and ecosystem respiration: review and improved algorithm. Global Change Biol. 11, 1–16. Robinson, A.P., Ek, A.R., 2003. Description and validation of a hybrid model of forest growth and stand dynamics for the Great Lakes region. Ecol. Model. 170, 73–104. Rossiter, D.G., Riha, S.J., 1999. Modeling plant competition with the GAPS objectoriented dynamic simulation model. Agron. J. 91, 773–783.

Roxburgh, S.H., Davies, I.D., 2006. COINS: an integrative modelling shell for carbon accounting and general ecological analysis. Environ. Model. Softw. 21, 359–374. Shinozaki, K., Yoda, K., 1964. A quantitative analysis of plant form – the pipe model theory. I. Basic analyses. Jpn. J. Ecol. 14, 97–105. Sievänen, R., 1993. A process-based model for the dimensional growth of even-aged stands. Scand. J. For. Res. 8, 28–48. Stange, C.F., 2001. Entwicklung und Anwendung eines prozeßorientierten Modells zur Beschreibung der N2 O- und NO-Emissionen aus Böden temperater Wälder, vol. 69. Garmisch-Partenkirchen, Shaker Verlag. Schriftenreihe des FrauenhoferInstituts Atmosphärische Umweltforschung, p. 156. Tatarinov, F.A., Cienciala, E., 2009. Long-term simulation of the effect of climate changes on the growth of main Central-European forest tree species. Ecol. Model. 220, 3081–3088. Thomas, C.K., Law, B.E., Irvine, J., Martin, J.G., Pettijohn, J.C., Davis, K.J., 2009. Seasonal hydrology explains interannual and seasonal variation in carbon and water exchange in a semi-arid mature Ponderosa Pine forest in Central Oregon. J. Geophys. Res. Biogeosci., doi:10.1029/2009JG001010. Thornley, J.H.M., 1999. Modelling stem height and diameter growth in plants. Ann. Bot. 84, 195–205. Thornley, J.H.M., Cannell, M.G.R., 2000. Modelling the components of plant respiration: representation and realism. Ann. Bot. 85, 55–67. Valentine, H., 1990. A carbon-balance model of tree growth with a pipe-model framework. In: Dixon, R., Meldahl, R., Ruark, G., Warren, W. (Eds.), Forest Growth: Process Modeling of Responses to Environmental Stress. Timber Press, Portland, pp. 33–40. Valentine, H.T., 1985. Tree-growth models: derivations employing the pipe-model theory. J. Theor. Biol. 117, 579–585. Van Nes, E.H., Scheffer, M., 2005. A strategy to improve the contribution of complex simulation models to ecological theory. Ecol. Model. 185, 153–164. Verbeeck, H., Samson, R., Granier, A., Montpied, P., Lemeur, R., 2008. Multi-year model analysis of GPP in a temperate beech forest in France. Ecol. Model. 210, 85–103. Wang, Q., Tenhunen, J., Falge, E., Bernhofer, C., Granier, A., Vesala, T., 2003. Simulation and scaling of temporal variation in gross primary production for coniferous and deciduous temperate forests. Global Change Biol. 10, 37–51. Zianis, D., Muukkonen, P., Mäkipää, R., Mencuccini, M., 2005. Biomass and stem volume equations for tree species in Europe. Silva Fenn. Monogr. 4, 1–63.