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Agricultural and Forest Meteorology 151 (2011) 179–190

Contents lists available at ScienceDirect

Agricultural and Forest Meteorology

journa l homepage: www.e lsev ier .com/ locate /agr formet

odelling forest carbon balances considering tree mortality and removal

üdiger Grotea,∗, Ralf Kiesea, Thomas Grünwaldb, Jean-Marc Ourcival c, André Granierd

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

r t i c l e i n f o

rticle history:eceived 23 April 2010eceived in revised form9 September 2010ccepted 4 October 2010

eywords:hysiologically oriented modellingntegrated modellingddy-flux measurementsree growtharbon balanceshinning

a b s t r a c t

The determination of ecosystem carbon balances is a major issue in environmental research. Forest inven-tories and – more recently – Eddy covariance measurements have been set up to guide sustainabilityassessments as well as carbon accounting. A differentiation between ecosystem compartments of carbonsuch as soil and vegetation, or above- and belowground storages nevertheless requires further empiricalassumptions or model simulations. However, models to estimate carbon balances often do not account forcarbon export by management and the direct and indirect impacts of forest management. To overcomethis obstacle, we complemented a physiologically based process model (MoBiLE-PSIM) with routines fordimensional tree growth and mortality and evaluated the full model with measurements of water avail-ability, primary production, respiration fluxes and forest development (tree dimensions and numbersper hectare).

The model is applied to three forests representing different physiological types and climatic envi-ronments: Norway spruce, European beech and Mediterranean holm oak. Simulated carbon balancesare 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 belowground (incl. roots) – a share that is decreased if a part of the carbon is exported as timber. The role of fur-ther impacts, in particular carry-over effects in years that follow intense drought periods, is highlighted

appr

and the usefulness of the

. Introduction

In times of increasing global industrialization and acceleratingnvironmental changes, reliable methods for understanding theevelopment of forest ecosystems and their interaction with possi-le management activities are of uppermost interest. This includeshe uptake and sequestration of carbon into plant tissue or soil. Theevelopment and application of models that are able to representcosystem responses to these changes is therefore highly desir-ble. These models are complicated however by the multitude andoncurrence of possible impacts (such as increasing temperatures

nd atmospheric CO2 concentrations, nitrogen deposition, and alsohanges in light availability by thinning operations).

Accounting for environmental impacts on forests over long timeeriods (at least several years) requires the consideration of not

∗ Corresponding author. Tel.: +49 08821 183124; fax: +49 08821 183294.E-mail address: ruediger.grote@kit.edu (R. Grote).

168-1923/$ – see front matter © 2010 Elsevier B.V. All rights reserved.oi:10.1016/j.agrformet.2010.10.002

oach for highly resolved environmental change studies is discussed.© 2010 Elsevier B.V. All rights reserved.

only external environmental changes (e.g. climate, deposition) butalso changes in the vegetation itself that affect microclimatic con-ditions and carbon allocation. These changes are structural anddevelop from tree establishment, death and dimensional growth(height and diameter growth). Dimensional growth affects theamount, distribution, and properties of foliage in the canopy andfine roots in the soil and thus influences carbon gain as well asnutrient and water uptake. Additionally, dimensional changes aresuperimposed by management both directly by cutting larger orsmaller than average trees and indirectly by changes of the environ-mental conditions. Such feedbacks are particularly important whensingle trees or tree social classes are modelled, because the size ofa tree determines its demand as well as its competition strengthfor resources. Adding this feature to physiological models and pro-

ducing output that can be compared with meaningful data fromforest inventories has been identified as one of the main deficitsof existing models (Landsberg, 2003; King, 2005). In these reviewsit has been noted that many modelling exercises studying mat-ter balances based on physiological processes assume that stand

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80 R. Grote et al. / Agricultural and F

tructural changes such as height increase or a shift between socialree classes have no or only negligible feedback impacts through-ut the simulation period (e.g. Duursma et al., 2009; Tatarinov andienciala, 2009). At least for long-term growth predictions, standimensional development and the selection of underlying assump-ions in the simulations become crucially important. Each addedeature in physiological models generally increases the number ofarameters that are intrinsically uncertain. This may be the reasonhat tree dimensional development has only been acknowledged inew models yet (Peng et al., 2002; Deckmyn et al., 2008). Thereforee seek a simple but nevertheless general method with measurablearameters to approach this task.

In order to account for physiological changes that depend on treeimension, it is in fact not necessary to describe dimensions explic-

tly. Simple physiological models derive allometric relationshipsynamically from stem biomass (Landsberg and Waring, 1997).owever, this is an inadequate approach if more complex interac-

ions such as light competition between different canopy layers orree classes are to be assessed. A very interesting concept for mech-nistic description of dimensional growth is the pipe-model theoryShinozaki and Yoda, 1964) proposed by Valentine (1985) and usedy several authors since then (e.g. Sievänen, 1993; Valentine, 1990;äkelä, 1997, 2002). The drawback of this approach is that it

oes not predict height growth per se but uses further assump-ions to distribute substrate production. Another approach derivesimensional development directly from empirical relations to stemorm and height–diameter ratio assuming volume growth is con-istent with biomass growth. For example height–diameter ratioas been related to stand density (Bossel, 1996), diameter at breasteight (Friend et al., 1997), or (more process-based) to the activ-

ty of different meristems (Thornley, 1999) and canopy productionRobinson and Ek, 2003). Less frequently, empirical knowledge ofelations between stem volume, height, and diameter developments directly used to distribute stem growth in a model (Korol et al.,995; Kimmins et al., 1999). This approach is partly attributable tohe fact that the determination of these relations requires a con-iderable mensuration effort for each tree species. Since more andore of this kind of information is available in the literature (e.g.

ianis et al., 2005) this option may be more frequently used.The approach presented below describes a calculation proce-

ure for continuously updating height and stem diameter of a stand,ree class, or single tree with species specific taper/volume curvesnd wood density parameters. Since sapwood biomass productions used as an input, it is particularly suitable for physiology-oriented

odels that include carbon allocation procedures. We apply thispproach in the physiologically based vegetation model PSIM andhe biogeochemical DNDC model as a new implementation withinhe modelling framework MoBiLE (Modular Biosphere simuLa-ion Environment; Grote et al., 2008, 2009a,b; Holst et al., 2010).his combination is an alternative implementation to the PnET-N-NDC model which has been widely used to estimate trace gasmissions from forest soils (e.g. Kesik et al., 2006; Butterbach-ahl et al., 2009). The PSIM model calculates primary production,lant respiration, litterfall and allocation, including the increasef woody biomass. All these processes depend directly or indi-ectly on micro-climatic environmental conditions and the supplyf water and nutrients (i.e. nitrogen). The DNDC (De-Nitrification-e-Composition) model accounts for the mineralization of litternd calculates water and nitrogen availability for the vegetation.his modelling approach enables a detailed view and characteriza-ion of carbon fluxes and pools within forest ecosystems. In order to

rove the wide applicability of this approach we chose three exam-le sites that cover coniferous and broad leaved as well as evergreennd deciduous forests under temperate and Mediterranean climaticonditions. For each of these sites, various long-term measure-ents of different resolutions (daily, weekly, annual) are available

eteorology 151 (2011) 179–190

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

2. Model description

2.1. General

Simulations were performed using a combination of five mod-els (covering microclimate, water cycle, physiology, soil nutrientdynamics, and dimensional changes as described further down indetail) that are combined in the MoBiLE framework. Due to 1-D col-umn modelling, any simulation is site-specific and only verticallydifferentiated information is exchanged between time steps. Thevertical spatial scale extends from the uppermost top of the veg-etation down to the total rooting depth in the soil. Therefore the(1-D) model space is stratified into a flexible number of vegetationlayers with equal height intervals in the canopy (between 2 and40 depending on total vegetation height, with the maximum num-ber of layers reached at 20 m stand height – if the stand is larger,the width of the layers are increased accordingly) and a variablenumber of soil layers. Information to initialize vegetation includesspecies, 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 thesite initialization file: carbon and nitrogen content, field capacityand wilting point, pH, saturated conductivity, bulk density, clay andstone content can be directly prescribed if available.

Initialization of biomass as well as carbon and nitrogen con-tent in various living and dead tissues that are handled by themodel are derived from the available initial information. The totalamount and distribution of foliage and fine roots are calculatedfrom species-specific parameters (see Grote, 2003). Additional soilcompartments (such as five different litter pools) are derived fromthe initialized carbon and nitrogen amounts and iterative proce-dures 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 mod-ule are integrating forward in time can either be ‘sub-daily’, daily,or any multiple of a day (e.g. annually for stand development). Theindividual time steps of the modules are kept constant through-out the simulation. The smallest time step in the simulation is thebase-time step which is selected according to input data availabil-ity and/or module requirement (generally one hour). Modules runon longer time steps (daily, yearly) are called after the sum of base-time 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 thatdescribe in particular the cycling of water, carbon and nitrogen inthe biosphere as well as exchange processes of these elements withthe atmosphere and hydrosphere. Furthermore, it provides climatedata in the time step needed by the respective model, including afiner time step than the original data so that daily temperature andradiation values can be distributed throughout the day to providehourly or smaller time step resolution if these are not a direct inputinto the model. MoBiLE assumes sinusoidal distribution schemesfor temperature (De Wit et al., 1978) and radiation (Berninger,1994).

Comparable simulation frameworks with similar objectiveshave been published in the past. For example SONCHES

(Knijnenburg et al., 1984), GAPS (Butler and Riha, 1989; Rossiterand Riha, 1999), EXPERT-N (Engel and Priesack, 1993; Priesacket al., 2006), APSIM (McCown et al., 1996; Huth et al., 2003), andCOINS (Roxburgh and Davies, 2006) all represent flexible envelopesfor previously published models or biosphere related process simu-

R. Grote et al. / Agricultural and Forest Meteorology 151 (2011) 179–190 181

F en linei e simo ums d

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ig. 1. Flowchart of the simulation. Grey boxes represent modules; boxes with broks selected. Abbreviations ts and nd stand for number of time step and day during thne day (set to 24). Input variables for the daily loop are calculated as averages or s

ations. Although some of these frameworks include process-basedorest models, forest growth and development is generally notddressed because the primary focus is on agricultural applicationsbut see Liu et al., 2002).

.2. Biogeochemical models

The simple Empirical-based Canopy Model (ECM, Grote et al.,009a) is applied to provide hourly climatic information for eachanopy layer. Soil temperature which drives the biogeochemicalalculations is derived from the DNDC model (Li et al., 1992). Theourly climate data (air temperature and global radiation) drivecommon photosynthesis model that calculates carbon uptakeith dependence on light, temperature, enzyme activity based on

arquhar et al. (1980) and the water constraint according to Ballt al. (1987). This approach determines carbon gain by iterativelydjusting stomata conductivity and thus transpiration which is lim-ted by water availability. Furthermore, the light saturated rate ofarboxylation is reduced (a) when nitrogen concentration in theeaf tissue is below optimum, (b) relative water availability fallselow a species-specific threshold (Grote et al., 2009a), or (c) cumu-

ative cold temperatures provoke a physiological dormancy (Berght al., 1998).

Further vegetation processes are calculated with the physi-logically based PSIM model that simulates phenology (Grote,007), plant respiration (Thornley and Cannell, 2000), senescencend allocation of carbon and nitrogen (Grote, 1998), and nitro-en uptake. The allocation process is sink-strength driven, whicheans that carbon and nitrogen are distributed according to the rel-

tive demand within a given plant compartment: foliage, fine roots,

eproductive tissue, and sapwood. The demand for carbon resultsrom optimum ratios between foliage and fine roots, sapwood andeproductive tissue, and foliage area and sapwood area respec-ively. The demand for nitrogen is defined by the ratio betweenctual and optimum nitrogen concentration in each compartment.

s indicate process-groups. Output files are only written when the respective optionulation, respectively; tsMax is the number of sub-daily time steps executed withinepending on the variable.

Senescence is a function of turnover rates, considering a retranslo-cation of nitrogen from dying foliage (but no other tissues) backinto the plant as long as nitrogen demand is not fully established.Fine roots take up nitrogen from the soil as a function of nitrogenavailability and species specific uptake rate, rendering the modelsensitive to nitrogen distribution as well as fine root distributionwithin the soil profile.

The PSIM model has been previously coupled to the soil processmodel DNDC (Li et al., 1992) by Grote et al. (2009b). The linkagebetween the two models is established by the plant uptake of nitro-gen that is limited by the availability of nitrate and ammoniumin the soil solution. Above- and belowground litter is mineralized,nitrified and denitrified based on DNDC calculations. Water avail-ability is calculated by the DNDC water balance model (see also Liet al., 2000; Stange, 2001). In contrast to the original version, theadopted DNDC model in MoBiLE is able to calculate water pools andfluxes throughout the total rooted soil profile with soil layer specificparameterizations (Holst et al., 2010). Thus, soil hydraulic proper-ties such as field capacity or wilting point can be initialized usingflexible soil layer numbers and extensions according to availablemeasurements.

2.3. Dimensional growth model

To describe tree growth from seedlings to mature trees, a gen-eral formulation for the development of stem form (=stem volumerelative to a column volume with a defined reference diameter) isrequired. Although stem diameter at 1.3 m height (DBH) is generallyavailable for initialization and evaluation purposes, it is not suitableto be directly related to stem form because stem taper changes dur-

ing stand development. However, as proposed by Hohenadl (1922),it can be assumed that the form of a tree is approximately constantin relation to a reference diameter at 10% tree height (D01).

D01 in turn can be roughly estimated from DBH by linear extrap-olation using the current height/diameter relation and the height

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82 R. Grote et al. / Agricultural and F

ifference between the two stem positions. This calculation isurely empirical and is only used here for transferring initializedBH to D01 and D01 back into DBH in order to provide evaluationata that can be compared with repeated DBH measurements. Itoes not affect the model calculations.

The new stemwood volume of a stand (Vs) is calculated fromood density (Dw) and the dry matter of total wood biomass (Mw)

onsidering the fractions of branch (fbra) and coarse roots (fugw):

s = Mw

(1.0/(1.0 − fbra) + fugw) × Dw(1)

oody biomass is determined from allocation into the woody com-artment (gMw) and loss of woody material. The latter is the sumf two processes: tree mortality and death of branches. While treeortality is treated as a time-dependent fraction of the total wood

iomass (fmort), branch senescence is calculated as a fraction ofapwood senescence (sMw; the rest of the senescence is formingeartwood tissue that remains a fraction of Mw) according to:

′w = Mw × (1 − fmort) − (sMw × fbra) + gMw (2)

ree mortality is expressed specifically either for a closed canopy0.02 a−1) or for the conditions when canopy is not closed0.002 a−1) as in young stages of development or after thinning.lternatively, it can be defined explicitly by the user togetherith a specific date, representing a thinning event. The fraction of

ranches is calculated dynamically depending on tree size: increas-ng when trees are very young and decreasing when trees areearing maturity. In our approach, fbra is defined as the minimumf three values: development of small trees estimated from baseiameter (DBAS) which is derived from tree height assuming thattem form is equal to a cone (Eq. (3)a), development of trees tallerhan 2.5 m estimated from the fraction of branches dynamically cal-ulated from DBH according to Bossel (1996) (Eq. (3)b) accordingo:

bra = 1.0 − exp(−fBRAY × DBAS)10 (3a)

bra = fBRAO + (1.0 − fBRAO) × exp(

−10.0 × DBHDBHmax

)(3b)

ith fBRAY and fBRAO being parameters for young and old trees,espectively, and “exp” references to an exponential function, andmaximum value of fbra = 0.5 for the mature trees.

The stem volume increment for the single (average) tree (dVt)s simply determined by the difference between new and old standolume divided by stem number. Height and diameter growth isalculated as suggested by Bossel (1996) for unrestricted canopypace conditions with D01 instead of the originally used DBH:

D01 = dVt

3.0 × a × D012 × HD01(4a)

H = dVt

3.0 × a × D012 × HD01(4b)

ith: ˛ = � × 0.25 × FORM01 × Dw.FORM01 is the ‘true’ form factor (Hohenadl, 1922), which is the

elation between stem volume and an ideal column volume over theross sectional area at D01. It is assumed to be relatively constantith age or tree size and varies approximately between 0.4 and 0.5.D01 in Eqs. (4a) and (4b) is the ratio between previous height and01 and � is the number Pi.

The update of tree dimension can be applied independent ofhe time step. Here, we apply the procedure once a year, using

he annual cumulative wood growth as input. Harvesting can bendicated for any day in the simulation. Furthermore, it is possibleo choose a thinning that removes predominantly small, large or

edium sized trees. The parameters used in our simulations areisted in Table 1.

eteorology 151 (2011) 179–190

3. Description of sites and simulations

3.1. Sites

3.1.1. Anchor Station TharandtThe 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 meanannual air temperature is 7.8 ◦C (maximum and minimum annualmeans are 9.4 ◦C in the year 2000 and 6.0 ◦C in the year 1996) andthe mean annual precipitation is 823 mm (maximum and minimumyearly sums are 1287 mm in the year 1981 and 501 mm in the year2003).

The spruce stand at the Anchor Station was established byseeding in 1887. A special inventory in 1999 focused on the areawithin a 500 m radius of the flux tower (Grünwald and Bernhofer,2007) indicated the following stand inventory: tree density was477 trees ha−1, the above ground biomass 213 t ha−1, mean canopyheight 26.5 m, and mean breast height diameter 33 cm, one-sidedleaf area index was estimated to be 7.6, and the ground was mainlycovered by young Fagus sylvatica (20%) and Deschampsia flexu-osa (50%). The canopy is now dominated by coniferous evergreenspecies (72% Picea abies, 15% Pinus sylvestris) with a small per-centage of deciduous species present (10% Larix decidua, 1% Betulaspec., 2% others). In March/April 2002 a thinning resulted in a reduc-tion 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 was43 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%) ofnaturally established ca. 40-yr-old European beech (Fagus sylvaticaL.). The stand is on a site of good productivity in the first class ofSchober’s yield table for Beech. The experimental plot (0.6 ha) wasin the central part of a 65 ha area composed mainly of 30–60-yr-old Beech. The plot and the surrounding stands were thinned 2years before C flux measurements began. Two other thinnings wereperformed in 1999 and 2005.

Soil water content was periodically measured (7–20 days) usinga neutron probe in eight 1.6–2.6 m long aluminum tubes distributedin the experimental plot. Periodic girth measurements (1–3 weeksfrequency) were performed on a 500-tree sample. Volume andbiomass at tree and stand scales were derived from circumfer-ence measurements using allometric relationships established on20 trees from the same stand.

3.1.3. State Forest PuéchabonThe study site is located 35 km NW of Montpellier (South-

ern 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 soiltype is a clay-loam above limestone and has a high stone fraction(75% in the upper 50 cm, 90% below) so that the storage capacityof plant available water within the whole profile of 4.5 m is onlyapproximately 150 mm. The mean annual rainfall and temperatureare 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 andwas submitted to the last clear cut in 1942. It has a dense canopy

with a maximum height of approximately 6 m. Soil water storagewas measured from 10 cm down to 450 cm depth in intervals of20 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

R. Grote et al. / Agricultural and Forest Meteorology 151 (2011) 179–190 183

Table 1Parameters for tree growth model (DW = dry weight, FW = fresh weight).

Parameter Description Spruce Beech Holm oak

Fugw Below ground wood fraction (kgDW kgDW−1) 0.2a 0.24b 0.49c

Dw Wood density (kgDW dm FW−3) 0.41a 0.58d 0.90c

Pbra Empirical parameter for branchwood estimation of small trees (−) 70 70 130*

fBRAF Final branchwood fraction of total aboveground wood (kgDW kgDW−1) 0.2* 0.18b 0.6*

DBHmax Diameter at which the final branch fraction is reached (m) 0.8* 0.4a 0.25e

FORM01 Stem volume relative to volume of a column with diameter at 0.1 tree height (0–1) 0.5 0.5 0.5

a Bossel (1996).b Granier et al. (2000).

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c Unpublished data.d Grote et al. (2003).e Canadell et al. (1988).* Derived from biomass measurements at the sites of this study.

rees of Q. ilex larger than 1 cm DBH and present in nine subplotsituated in a 300 m radius around the flux tower.

.2. Eddy flux measurements

For all sites the calculation and correction of turbulent fluxeserived by the EC method were based on a general EUROFLUXethodology (Aubinet et al., 2000). Gap filling and flux partitioningas performed by the “Online Eddy-Covariance Data Gap-Filling

nd Flux-Partitioning Tool” (Reichstein et al., 2005) including*-correction to replace unreliable data due to low turbulent con-itions. The gap filling algorithm was based on methods describedy Falge et al. (2001) and merges the sensitivity of meteorologi-al variables with the auto-correlation of fluxes in time (Reichsteint al., 2005). This procedure provided consistent data treatment.t Puéchabon, high frequency losses due to the closed-path sys-

em were quantified by spectral analysis and the correspondingorrections (6% for H2O and 1% for CO2) applied (Allard et al., 2008).

.3. Simulation setup

All simulations were run with a 1-hour base-time step. Althoughor most of the simulated periods, measured hourly or even half-ourly values would have been available, the hourly data werealculated from aggregated daily values (see Section 2.1). The dailynput represents a widely available and reliable data source used in

any ecosystem models (e.g. PnET, 3-PG, BGC, and families; Abernd Federer, 1992; Landsberg and Waring, 1997; Prentice et al.,992) and enables computational efficient simulation runs. Theimulation results were compared with results obtained using half-ourly measured input data for single years but the differencesere small (not shown).

We initialized the stands with the first inventory measurements.imilarly, the soil profiles were initialized with measurementsrom the earliest investigations. The soil properties provided inhe model initialization included field capacity, wilting point, soilrganic carbon content, bulk density, and stone content distribu-ion for the whole soil profile (= rooted soil).

Climate data input into the model included the daily representa-ions of global radiation, mean air temperature, relative humidity,nd precipitation provided from the CARBO-EUROPE databaseLevel 4 aggregation from half hourly measurements and standardap filling routines). The background carbon dioxide air concen-ration was assumed to be 370 ppm and constant throughout theimulation period. Total annual nitrogen deposition for these sites

as converted into a virtual concentration value of precipitation

total annual deposition in kg N ha−1/total average annual precipi-ation in mm ha−1) based on values reported in the literature. Theesulting load of nitrogen per rainfall event was supplied into theppermost soil layer.

Assuming that the physiological processes can be well rep-resented by the selected modelling approach, we used theEddy-covariance data measured at the sites and adjusted thetwo main physiological parameters that determine photosyn-thesis and respiration in the PSIM model (Fig. 2). The adjustedparameters were the saturated carboxylation capacity (VCmax, seeFarquhar et al., 1980 for further description) and the mainte-nance respiration coefficient at reference temperature (KMmax,see Thornley and Cannell, 2000 for further descriptions). Theadjustment was carried out by iteratively starting at 0.1 forKMmax and the lowest values for each species found in the liter-ature for VCmax (these were for beech 27.1 �mol m−2 s−1 Medlynet al., 2002, 21.2 �mol m−2 s−1 Bergh et al., 2003 for spruce, and31.0 �mol m−2 s−1 Pena-Rojas et al., 2004 for oak) and increasingthese 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 valueswas one (±0.005).

Additionally, we used measured soil water and forest inventorydata for further model evaluation (Figs. 3 and 4). Soil water con-tent, the major driver for primary production, is also a key variabledriving C and N mineralization and thus nutrient availability. For-est inventories are the integrated measure of aboveground carbonaccumulation and are easy to measure, making them a valuablegeneral source of information. The periods for which these data setswere available are given directly in the figures. We ran the modelwith and without thinning operations in order to investigate theimpact of slow stand development on small-scale processes (andtheir feedback to forest growth) (Fig. 5).

4. Results

The parameters obtained from the calibration/adjustment pro-cedure 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 therange observed in field measurements (see Medlyn et al., 2002). Forexample 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, theadjusted respiration activity parameters were considerably higherthan the value of 0.1 recommended by Thornley and Cannell (2000).

Forest inventory properties of all three stands revealed someuncertainties in the carbon balance (see Fig. 4). The absolutedifference of simulated annual woody aboveground biomassincrement compared to the biomass estimated from tree mea-

surements 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 Tha-randt site. However this site also had the highest measurementuncertainties since these were derived from a selection of stem

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

R² = 0.79

2007: y = 1.145x

-5

0

5

10

15

20

25

-5 2520151050

sim

ulat

ed [g

C m

-2da

y-1]

measured [gC m -2 day-1]

GPP, Tharandt

R² = 0.7606

2005: y = 0.9001x0

2

4

6

8

10

12

14

16

1614121086420

sim

ulat

ed [g

C m

-2da

y-1]

measured [gC m -2 day-1]

TER, Tharandt

R² = 0.8515

2004: y = 1.2176x

-5

0

5

10

15

20

-5 20151050

sim

ulat

ed [g

C m

-2da

y-1]

measured [gC m -2 day-1]

GPP, Hesse

R² = 0.6995

2008: y = 0.9287x0

2

4

6

8

10

12

121086420

sim

ulat

ed [g

C m

-2da

y-1]

measured [gC m -2 day-1]

TER, Hesse

R² = 0.4861

2006: y= 1.1365x0

1

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8

876543210

sim

ulat

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C m

-2 d

ay-1

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TER, Puechabon

R² = 0.6288

2005: y = 1.2183x

-2

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4

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8

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12

-2 121086420

sim

ulat

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C m

-2da

y-1]

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GPP, Puechabon

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 GPPoverestimation 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 asred 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 interpretationof the references to color in this figure legend, the reader is referred to the web version of the article.)

y = 1.0172xR² = 0.4664

0

5

10

15

20

25

30

302520151050

sim

ulat

ed (v

ol. %

)

measured (vol. %)

Tharandt (0-100 cm)1998-2006

y = 0.9762xR² = 0.9064

15

20

25

30

35

40

45

45403530252015

sim

ulat

ed (v

ol. %

)

measured (vol. %)

Hesse (0-50 cm)1997-2002

y = 1.0183xR² = 0.8038

0

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25

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302520151050

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ulat

ed (v

ol. %

)

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Puechabon (0-400 cm)1998-2006

A B C

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

R. Grote et al. / Agricultural and Forest Meteorology 151 (2011) 179–190 185

0

100

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s(gbaay(ss

2010200520001995 21995

ig. 4. Measured (points) and simulated (lines) stand growth parameters at the thranches.

ore analyses and not from dimensional measurements of all indi-iduals.

.1. Tharandt

Although the parameters were adjusted for the whole simu-ation period, gas fluxes as well as aboveground woody biomassrowth were very well represented (Table 2, Fig. 4). Gross primaryroduction in summer was slightly overestimated which was com-ensated by an underestimation in winter and spring (Normalizedean Bias, NMB = 0.08). Similarly, high total ecosystem respiration

bursts’ on some summer days could not be represented, leading toslightly biased simulation (NMB = 0.07). The differences in GPP asell as TER were more concentrated in particular years. For exam-le, only about 6–10 days with exceptional respiration rates in 2005ere responsible for a calculated bias of approximately 10% that

ear (Fig. 2).Simulated monthly and annual carbon flux components were

till well in accordance with those derived from measurementsTable 2, Fig. 5). Compared with the correlation based on daily inte-rated values, the correlation measures (for daily deviations) wereetter when integrated on a monthly basis (since high deviationst single days are averaged out) but less good when integrated on

n annual basis (due to less sample points). Nevertheless, a fewears showed overestimated GPP- (i.e. 2001, 2007) and TER fluxes2001–2003, 2007). In some years these overestimations compen-ated each other, leading to a closer match between measured andimulated net ecosystem exchange (NEP) than obtained for GPP.

20102005 2010200520001995

lected forest sites. Woody biomass includes all aboveground parts of the stem and

The impact of thinning in 2002, which led to a reduction of leafarea by approximately 10%, seemed to be fully compensated byimproved climate conditions. However, the dry years in 2003 and2006, including their after-effects, lead to a decreased growth thatwas not completely covered by the model. Consequently a maskedgrowth depression due to the loss of trees cannot completely beexcluded.

4.2. Hesse

The GPP fluxes at Hesse were more influenced by drought thanobserved at the spruce stand in Tharandt (Fig. 3; see also Granieret al., 2007). The GPP fluxes were generally well-represented bythe model, resulting in similar correlation and error measures(Table 2) and an even smaller bias than those at Tharandt (NMBfor GPP = −0.02 and for TER = 0.002). Exceptions to this improvedperformance were the years 2004, 2007, and 2008. In 2004 (theyear following the exceptional dry year 2003), GPP, TER, and NEPwere overestimated (Fig. 2). This phenomenon was likely a result ofirreversible physiological damages and has been described beforeas the ‘lagged effect of drought’ (Granier et al., 2007, 2008) or ‘carry-over effects’ (Thomas et al., 2009). The model error for this year was403 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 a172 g C m−2 (+47%) overestimation for NEP. Interestingly, an over-estimation of NEP was also obtained for the very moist year 2007(132 g C m−2, +21%). However, in this case the difference originatedfrom the added effect of a GPP overestimation (43 g C m−2, +2%)

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

1000

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aeow

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gdeA

00 01 02 03 04 05 06 07 08

ig. 5. Measured (points) and simulated (lines) annual gross primary productiononsidered, – indicates simulations without management, vertical arrows indicate

nd a TER underestimation (−98 g C m−2, −8%). In 2008, the under-stimation of TER was similar (Fig. 4, −87 g C m−2) but the effectn NEP was smaller because the GPP estimations were well in lineith measurements.

The impact of the thinning in 2005 resulted in an even strongerecrease in GPP and NEP than calculated for the spruce forest (app.00 and 300 g C m−2, respectively). However, the faster growth andanopy closure compensated for the effect after only 3 years (Fig. 5).

.3. Puéchabon

At the warmest site of this study fluxes were more hetero-eneous. Interestingly, GPP fluxes in summer that were highlyrought-impacted were well met (see Fig. 3), while the large het-rogeneity during the winter months was not as well simulated.lso, early summer fluxes in 2005 and 2006 were overestimated.

00 01 02 03 04 05 06 07 08

and net ecosystem production (NEP) (++ indicates simulations with managementning event).

Both effects lead to a slightly smaller correlation and largererror measures (Table 2) as well as a slightly larger bias (NMBfor GPP = −0.8 and for TER = −0.08) than at the other sites. Asmentioned before, ‘respiration burst’ were underestimated butcompensated by overestimations in spring (2006, 2007, 2008) andsummer when the impact of drought on respiration is not quite aswell 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 theaverage annual simulated GPP was only about 5% higher than thatestimated from measurements. The higher GPP was more thancompensated for in the NEP by the overestimation of respiration.

5. Discussion

Sophisticated physiologically based models have been criti-cized because the underlying causes for their results are hard to

R. Grote et al. / Agricultural and Forest Meteorology 151 (2011) 179–190 187

Table 2Correlation 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 valuesare 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

DailyR2 0.81 0.81 0.43 0.85 0.70 0.78 0.65 0.61 0.42SE 1.81 1.05 1.69 1.91 1.05 1.60 1.22 0.87 1.17ME −0.34 −0.27 −0.07 −0.11 −0.01 −0.10 −0.47 −0.37 −0.09RMSE 1.58 0.91 1.55 2.03 1.25 1.61 1.34 0.91 1.23

MonthlyR2 0.92 0.90 0.72 0.93 0.81 0.91 0.52 0.47 0.49SE 1.05 0.70 0.90 1.27 0.77 0.95 1.25 0.99 0.70ME −0.34 −0.27 −0.07 −0.11 −0.01 −0.10 −0.43 −0.35 −0.08RMSE 1.10 0.75 0.95 1.37 0.94 0.95 1.51 1.07 0.87

AnnualR2 0.42 0.21 0.18 0.69 0.67 0.74 0.47 0.63 0.13

utiltoapgtiecsk2

mcmet22dh2aibit

paliwmTrPetm

SE 0.34 0.32 0.19 0.43ME 0.34 0.27 0.07 0.10RMSE 0.50 0.41 0.27 0.40

nderstand (Van Nes and Scheffer, 2005). The main reasons forhis have been highlighted by Medlyn et al. (2005) as equifinality,nsensitivity and uncertainty. We tried to minimize these prob-ems by combining established models where most parameters forhe species of interest were available from independent sourcesr derived from previous investigations. Furthermore, we evalu-ted the same model with measurements of various ecosystemools and fluxes (water availability, carbon gain, respiration, stemrowth and mortality); covering a wide range of climatic condi-ions and scales. Nevertheless, it has to be considered that thenherent uncertainty in the parameters used and the inevitablequifinality that might evolve from the uncertainty of parameter-ounterparts have not been investigated here. It thus remains to behown how robust the parameter set is for other sites and if anyey variables can be defined to simplify the model (Larocque et al.,008).

Despite the overall good agreement between simulations andeasurements, deviations indicated that particular modelling defi-

iencies need to be addressed and addressing these deficienciesight spur improvements in future models and guide further

xperimental designs and the choice of measurements. We notedhat GPP simulations were too high at the site Hesse in the year004 and – to a smaller degree – in the year 2007 (Figs. 2 and 5):004 was the year after the stand experienced the most severerought in the simulation period, and 2007 was an exceptionallyumid year at the after the 2003–2006 dry years (Granier et al.,008). This supports the assumption of Verbeeck et al. (2008) thatlong-term effect of drought is the most important driver of the

nter-annual variability in GPP. The particular process responsi-le for this phenomenon is still unclear but may be related to an

ncreased allocation into carbohydrate storages after depletion inhe previous year(s) (Barbaroux and Bréda, 2002).

The defoliation by insects in spring at Puéchabon was the mostrobable reason for the overestimated GPP flux in spring 2005t this site (Fig. 2). An environmental impact that is particu-arly important for evergreen species is the consideration of stressnduced impacts on the biochemistry of leaves. In our simulations,

e considered the low temperatures in winter that initiate a dor-ancy effect for spruce species as described in Bergh et al. (1998).

he inclusion of this process, however, was not able to improve the

epresentation of the variable assimilation rates in winter at theuéchabon site. The after-effects of drought that are reported toxtend over several days (Bengtson, 1980) are a possible reason forhe overestimation of GPP at Puéchabon in spring and early sum-

er 2006 when rainfall was very low (February through July of only

0.31 0.17 0.40 0.20 0.30−0.04 0.14 0.16 0.15 0.01

0.34 0.24 0.37 0.21 0.26

97 mm – less than one third of the 323 mm recorded for an averageyear) (Fig. 2).

With respect to respiration, we have to admit that even the com-bined soil process/physiology model was not able to fully representthe large variability and sometimes high respiration ‘bursts’ thatwere evident in the measurements. This may be related to the so-called ‘Birch Effect’ that describes unusual respiration rates frommineralization after re-wetting of dry soils (see Jarvis et al., 2007).This effect has been particularly observed in dry climates of Africaand the Mediterranean region but might be responsible also foroccasional bursts observed at the temperate sites of Hesse and Tha-randt (Fig. 2). It is, however, also possible that these observationsare more related to the errors in the eddy flux measurements or thegap-filling routines for these measurements rather than to a miss-ing model sensitivity. More obviously related to a model deficiencyis the overestimation of respiration in the years of harvest. Evenif the day of harvest was captured correctly, the supply of mate-rial for mineralization into the soil was immediate in the modelwhile it takes a longer period of time for the piled debris fromtree harvest to be actually integrated into the litter and soil lay-ers. Another uncertainty in the simulation was the relatively highrespiration parameter obtained by adjusting overall respiration atthe sites. It might be suspected that the autotrophic respiration wasoverestimated by the model because heterotrophic respiration wasnot evaluated. Until further measurements of the heterotrophicrespiration become available to improve the models, we rely onevaluations of soil respiration simulated by the DNDC model fromother sites (e.g. Kurbatova et al., 2008).

The simulations also demonstrate a statistical scaling prob-lem. 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 overesti-mation of the respiration parameter. Consequently, respiration wasoverestimated in years were these respiration rates were seldomor non-existent. This was especially a problem at the Puéchabonsite where the correlation between measurements and simulationresults was relatively low (see Reichstein et al., 2002 for furtherdiscussion of respiration processes).

The measurements of gas exchange used in this study havealready 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 biospheremodels 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 impactshave never been included in the analyses. Verbeeck et al. (2008)

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

Table 3Simulated 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-yearperiod (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 periodis 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 boldnumbers.).

Tharandt Hesse Puéchabon

avg (nat. + thin.) sd avg (nat.) sd avg (nat. + thin.) sd avg (nat.) sd avg (nat.) sd

GPP 1995 175 2024 149 1651 159 1719 126 1393 149Respiration −1397 94 −1372 85 −1146 64 −1125 54 −1133 89

Soil (heterotroph) −561 48 −524 42 −406 78 −342 28 −402 37Vegetation −835 66 −849 52 −740 63 −783 36 −731 70Below ground −908 68 −876 64 −632 61 −594 32 −604 46Above ground −489 34 −496 27 −514 40 −531 26 −529 50

NEP 599 381 652 82 505 602 593 91 260 82Soil 176 131 174 39 146 194 158 42 121 48

aseriflnsfmOfc

fias(imwbweaTJt

br3drwwb(aptrtwgi

Vegetation 305 500 478 91Below ground 244 49 279 35Above ground 241 379 373 62Export −117 – 0 0

lready pointed out that this was a weak point in their long-termimulations at Hesse forest. Davi et al. (2006) systematically over-stimated aboveground growth although NEP was generally wellepresenting, indicating export of wood may play an important partn the ecosystem balance. In fact, the overestimation at Hesse beechorests matches almost exactly the carbon amount that we calcu-ated to be the average annual export at this site. It should also beoted that consideration of forest thinning also includes an occa-ionally increased litter fall since leaves, roots and branches of theelled trees are usually not removed from the forest. This effect on

ineralization and soil respiration has usually not been considered.ur results indicate that they may be at least partly responsible

or the generally poor representation of ecosystem respiration pro-esses (e.g. Churkina et al., 2003).

As shown in Table 3, a considerable proportion of the net carbonxation was attributed to soil storages. Due to the relatively largemount of foliage and fine root senescence this was greatest for thepruce stand in Tharandt (30%), least for the beech stand in Hesse8%) and intermediate for the Mediterranean oak stand (18%). If liv-ng parts of the trees are considered to be belonging to the soil, the

odel estimates 74% of the oak stand’s NEP was below ground. Thisas due to a high coarse root proportion of app. 50% of total woody

iomass (Hoff et al., 2002, see Table 1) and a high fine root turnoverhich was approximately 3-fold that of the other species (Lopez

t al., 1998). For the spruce and the beech forests we estimated 49nd 32% total below ground allocation under unthinned conditions.his is consistent with general estimates for European forests (seeanssens et al., 2003 and literature therein) and a little bit lowerhan estimates for old growth forests (Luyssaert et al., 2008).

The results showed that a large portion of NEP can be exportedy harvest in young as well as old stands. Thinning decreased theelationship between NEP and GPP from 32 to 30% (spruce) and5 to 31% (beech) within the investigated decade. This was mainlyue to increased litter availability and thus higher heterotrophicespiration. A large proportion of the NEP was sequestered in theoody compartment (57% for spruce and 56% for beech) fromhich approximately 30 (spruce) and 69% (beech) was exported

y thinning. Of the fraction of NEP that stayed in the forest, 40%spruce) and 17% (beech) was distributed below ground between 13nd 34% less than when without thinning. The larger above groundroportion originated from the additional buds and foliage neededo close the gaps originating from thinning. When considering these

esults it should be noted that the beech stand was younger thanhe spruce stand and the approximate decrease of 30% of the stemsas distributed into two management events within the investi-

ated decade while the larger spruce stems were reduced by 30%n one event.

116 775 436 94 144 49174 80 262 44 188 6488 579 331 71 72 28−243 – 0 1 0 –

6. Conclusions

The MoBiLE-DNDC, equipped with new forest dimensional rou-tines, has been shown suitable to simulate carbon fluxes in varioustypes of pure forests, ranging from young to old, and coveringneedle- and broad leaved evergreen as well as deciduous species.The simulation was carried out continuously for more than tenyears on a hourly time step with climatic driving data derived fromdaily values. Since the model accounts for losses from thinning,it was possible to quantify real management impacts on the car-bon balance. The integration of small and large scale processes alsoenabled the quantification of feedbacks of management effects thatincluded an increased above ground growth due to more space andavailable resources. To our knowledge this is the first such exercise.

Acknowledgements

Modelling was supported by the DFG Project ‘Modeling ofbeech-dominated deciduous forest development based on compet-itive 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 EuropeanCommission projects MEDEFLU (ENV4-CT98-0455) and CARBOEU-ROFLUX (EVK2-CT-1999-00032) at the Puéchabon site and FrenchGIP-Ecofor at the Hesse site. We additionally thank the technicalstaff in Tharandt (Uwe Eichelmann, Heiko Prasse, Horst Hebentanzand Udo Postel) very much for their help and support in guarantee-ing continuous high quality data. Finally, we thank Richard Grant(Purdue Climate Change Research Centre) for improving the Englishof this manuscript.

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