H Davi

E Dufrêne

A Granier

V Le Dantec

C Barbaroux

C Franc ¸ois A

N Bréda

Modelling carbon and water cycles in a beech forest Part II.: Validation of the main processes from organ to stand scale

Keywords: Model, Carbon fluxes, Carbon balance, Water balance, Growth, Eddy fluxes, Net ecosystem exchange, Net primary production, Soil, Beech forest, Ecosystem respiration

A forest ecosystem model (CASTANEA) simulating the carbon balance (canopy photosynthesis, autotrophic and heterotrophic respirations, net ecosystem exchange, wood and root growth) and the water cycle (transpiration, soil evaporation, interception, drainage and soil water status) is tested with data from a young beech forest (Fagus sylvatica L.). For this purpose, the model validity is assessed by comparison between net CO 2 and H 2 O fluxes simulated and measured by the eddy flux technique over one year. In addition, most of the sub-models describing the processes mentioned above are tested using independent measurements from the same forest stand: tree growth, branch photosynthesis, wood and soil respirations, sap flow and soil water content. Most of the input parameters (both weather and plant characteristics) are measured in the same experimental site (i.e. Hesse forest) independently of the validation dataset (none has been fitted to match the output data, except rainfall interception parameters); some are from other beech sites or from literature. Concerning the radiative transfer, the model reproduces the measured exponential PAR extinction and provides a good estimate of the net radiative budget, except during winter. At the branch scale, simulated photosynthesis and transpiration of sun-leaves are close to the measurements. We show also, using model simulations, that the seasonal decrease of measured net photosynthesis at the branch level could be explained by a decrease in leaf nitrogen content during the leafy season. At stand scale, a good correlation was obtained between simulated and observed fluxes both on a half-hourly basis and on a daily basis. Except at the end of the leafy season, the model reproduces reasonably well the seasonal pattern of both CO 2 and H 2 O fluxes. Finally, even if there are some discrepancies between model estimations and fluxes measured at stand scale by eddy covariance, the model simulates properly both annual carbon and water balances when compared with the sum of the measured local fluxes. The remaining differences question the scaling up process when building such a model and the spatial footprint of eddy fluxes measurements.

Introduction

In the recent years, several research programs monitor CO 2 and H 2 O fluxes above temperate, boreal and also tropical forest canopies. A global network (FLUXNET) of over 250 long-term eddy flux stations exists [START_REF] Running | A global terrestrial monitoring network, scaling tower fluxes with ecosystem modelling and EOS satellite data[END_REF]Baldocchi et al., 2001a) and the number of stations is rapidly increasing. Many studies have shown a strong variability in the net ecosystem exchange of the carbon (NEE) according to water availability, species composition or environmental characteristics [START_REF] Law | Environmental controls over carbon dioxide and water vapor exchange of terrestrial vegetation[END_REF]. Process-based models provide a theoretical framework for analysis and interpretation of the scaling of physiological processes, enabling physiologists to extend their work to larger scales [START_REF] De Pury | Simple scaling of photosynthesis from leaves to canopies without the errors of big-leaf models[END_REF]. They then allow us to improve our understanding of measured NEE variability and to predict the global change effect. With the increase of longer data sets, there is a new challenge for testing coupled carbon-water flux models on multi-time and spatial scales. Some works have been made in this direction [START_REF] Aber | Extrapolating leaf CO 2 exchange to the canopy: a generalised model of forest photosynthesis compared with measurements by eddy correlation[END_REF][START_REF] Grant | Diurnal and annual exchanges of mass and energy between an aspen halzelnut forest and the atmosphere: testing the mathematical model ecosys with data fotm the BOREAS experiment[END_REF][START_REF] Kimball | Sensitivity of boreal forest regional water flux and net primary productivity simulations to sub grid scale land cover complexity[END_REF]Law et al., 2000a,b;Baldocchi and Wilson, 2001b) but often by using only integrative measurements by the eddy covariance method. But recently, some studies have shown that there were some discrepancies between integrative fluxes measurements and other independent measurements of NEE (Granier et al., 2000a;[START_REF] Ehman | An initial intercomparison of micrometeorological and ecological inventory etimates of carbon exchange in a mid-latitude deciduous forest[END_REF].

Moreover, if one intends to predict both short-term and long-term net ecosystem exchanges dynamics in forests, a canopy carbon-water flux model must be coupled with a biogeochemical model including carbohydrates allocation, growth and maintenance respiration, litter production and mineralisation, soil organic matter mineralisation and soil water balance sub-models. These models include a large number of parameters and variables linked together in different sub-models running at different time steps and different spatial scales.

Consequently, there is a necessity to test these assembling of sub-models using not only integrative data sets at the ecosystem scale (NEE, evapotranspiration (ETR)) but also data from different compartments of the system (soil respiration, soil water content, tree ring increment, organ growth, wood respiration, etc.). According to the difficulties of some measurements, these data sets are not common and rarely available for the same stand.

We then developed a new hybrid model, CAS-TANEA, described in [START_REF] Dufrêne | Modelling carbon and water cycles in a Beech forest. Part I: Model description and uncertainty analysis on modelled NEE[END_REF] aiming to capture net carbon and water fluxes of deciduous forests from half-hourly to multi-annual time scales and to accurately simulate ecosystem changes in biomass and soil organic matter from season to decades. The objectives of this research are to quantify how fluxes (CO 2 , H 2 O) and carbon pools (trunks and soil organic matter) in a temperate beech forest vary across these multiple scales and how climate, dynamic plant structure and physiological capacity act upon these variables.

In this paper, we focus on the validation of the different sub-models: light interception, branch photosynthesis, leaf and wood respiration, soil autotrophic and heterotrophic respiration, transpiration, rainfall interception, soil water content, tree growth, NEE. Because of the large number of variables and parameters included in this type of process-based models it is very difficult to validate the outputs using only the few synthetic variables generally available (NEE, ETR). Consequently, our approach was to test separately the most important sub-models included in CASTANEA using a large data set from a temperate beech forest located in Hesse (North-East of France) over one year. Then, we compare three independent annual ecosystem carbon balances: (1) estimated by the eddy covariance method, (2) estimated by the sum of the measurements of each individual process, and (3) simulated by the model. This work can both provide an independent evaluation of tower-based flux measurements and a reliable validation of a process-based model in order to use it with confidence to address other questions. For example, CASTANEA has already been used to evaluate the annual productivity and carbon fluxes over a large heterogeneous forest region (over 17,000 ha), in terms of species composition, canopy structure, age, soil type and water and mineral resources [START_REF] Le Maire | Modelling annual production and carbon dioxide fluxes of a large managed temperate forest using forest inventories, satellite data and field measurements[END_REF]. CASTANEA is also designed to quantify the relative effects of global changes (CO 2 , temperature, precipitation, radiation, etc.) on forest growth during last century.

Synthetic model description

CASTANEA is a physiologically multi-layer process-based model aiming to predict the carbon balance of an even-aged monospecific deciduous forest stand. The main output simulated variables are (i) the evolution of leaf area index, the standing biomass, the soil carbon and water content which are state variables, and (ii) the canopy assimilation, the maintenance and growth respirations, growth of organs, soil heterotrophic respiration, transpiration and evapotranspiration which are flux densities variables.

The canopy is assumed to be homogeneous horizontally and vertically subdivided into a variable number of layers (i.e. multi-layer canopy model), each of them enclosing the same amount of leaf area (typically less than 0.1 m 2 m -2 ). No variability between trees is assumed and then one "averaged" tree is considered to be representative of the stand. Tree structure is a combination of five functionally different parts: foliage, stems, branches, coarse and fine roots. A carbohydrate storage compartment is also considered but is not physically located in the model.

Half-hourly rates of gross canopy photosynthesis and transpiration are calculated from incident radiation and photosynthetic characteristics of individual leaves. Leaf nitrogen per unit area (gN m -2 leaf) is calculated from measured leaf nitrogen concentration (gN g dm -1 leaf), which is assumed to be constant inside the canopy, multiplied by the leaf mass per area, which decreases exponentially inside the canopy. The photosynthetic capacity of leaves, in different positions inside the canopy, is derived from leaf nitrogen density.

After subtraction of maintenance respiration requirements, the remaining assimilates are allocated to the growth of various plant tissues using a priority system, which varies according to the season. Phenological stages (budburst, end of leaf growth, start of leaf yellowing, etc.) and leaf growth are based on day-degrees. Maintenance respiration depends on temperature and nitrogen content of various organs while growth respiration depends on the biochemical composition of the considered organs.

The soil water balance is assessed by a bucket-type sub-model including three layers. The soil organic carbon (SOC) model derived from CENTURY [START_REF] Parton | Analysis of factors controlling soil organic matters levels in great plains grasslands[END_REF], shares SOC out three major components, which include active (live and soil microbes plus microbial products), slow (resistant plant material) and passive (soil stabilized plant and microbial material) pools. Flows of carbon between pools are controlled by a decomposition rate and by a microbial respiration loss parameter, both depending on soil texture, soil moisture and temperature.

There are two main time steps in the model: halfhourly and daily. The simulation period typically ranges from days to years. Most of variables including fluxes (light penetration, photosynthesis, respiration, transpiration, rainfall interception) are simulated halfhourly; all the state variables (organ biomass, soil and carbon water content) and some other ones (growth and phenology) are simulated daily. Input meteorological driving variables, which can be either half-hourly or daily values, are global radiation, rainfall, wind speed, air humidity, air temperature and CO 2 air concentration.

A more comprehensive description of the model, including equations, is given in [START_REF] Dufrêne | Modelling carbon and water cycles in a Beech forest. Part I: Model description and uncertainty analysis on modelled NEE[END_REF].

In situ measurements

Site characteristics

The experimental plot (Euroflux site FR02) is located in the State forest of Hesse (East of France; 48 • 40 N, 7 • 05 E) in a stand composed mainly of naturally established 30 years old beech trees (Fagus sylvatica L.). Three towers were erected: one (18 m high) is used for eddy covariance and microclimate measurements, the two other ones (15 m high) for physiological measurements. A hut containing the data acquisition systems is located near the first tower. A complete description of the site is given in [START_REF] Dufrêne | Modelling carbon and water cycles in a Beech forest. Part I: Model description and uncertainty analysis on modelled NEE[END_REF] and Granier et al. (2000a).

Model parameterization data set

The majority of the input parameters (both climate and plant characteristics) are measured at the same experimental site (i.e. Hesse forest) as the validation data set. The parameter data set was measured independently of the validation data set (except rainfall interception parameters, which were calibrated). Finally, some parameters not available are taken from other beech sites and from literature. A complete description of the input parameters is given in [START_REF] Dufrêne | Modelling carbon and water cycles in a Beech forest. Part I: Model description and uncertainty analysis on modelled NEE[END_REF]. Measurements were made for different days and solar elevations. We also provide the correlation coefficient (r 2 ) and the data size (n) of the relationship between the measurements and the fitted exponential.

Model validation data set

We used several independent data sets described below to evaluate the main sub-models. Most measurements were achieved during year 1997.

PAR profiles

At three dates in August 1997 and for different hours during the day (see Table 1), six vertical profiles of PAR were measured for different orientations around the scaffolding, using Li191-SA (Li-Cor, Lincoln, NE, USA) sensor. Profile of LAI were measured for the same orientations, using PCA-LAI2000 (Li-Cor) with three rings. To compare the measured and simulated profiles of PAR in a synthetic way, we have calculated the extinction coefficient (k) per date and per hour using Beer-Lambert law [START_REF] Monsi | Über den lichtfaktor in den pflanzengesell schaften und seine bedeutung für die stoffproduktion[END_REF]:

PAR PAR ref = exp(-k × LAI) (1)
with PAR ref the measured PAR on the top of the canopy.

Net radiation

A net radiometer (REBS, Seattle, USA) was installed above the stand at 17.5 m height, measuring the net radiation at 10 s time intervals and averaged and stored half-hourly. Some data (23 days) in winter were removed when daily temperatures are below 0 • C due to the frost on the net radiometer sensors (Granier personal communication).

Branch photosynthesis and night respiration

Two transparent branch bags were used to monitor the CO 2 exchange of branches during the 1997 growing season (end of April to early November).

One bag was placed near the top of the canopy (13 m above ground level, 0.4 point of LAI above, LMA = 92.0 g dm m -2 ) and a second one was placed at 7 m (4.8 point of LAI above, LMA = 41.8 g dm m -2 ). During measurements, bags were closed for 3 min and used as cuvettes operating as a closed system. Bags were scanned sequentially every half hour and monitored by a data logger. Between measurements, bags were supplied with ambient air (45 l s -1 ). A similar system was described by [START_REF] Dufrêne | A branch bag technique for simultaneous enrichment and assimilation measurements on beech (Fagus sylvatica L.)[END_REF] and used in the BOREAS project [START_REF] Saugier | Transpiration of a boreal pine forest measured by branch bags, sapflow and micrometeorological methods[END_REF]. Net photosynthesis was calculated by subtracting woody branch respiration to net CO 2 exchange, and compared with simulated net photosynthesis for the considered layer. Branch respiration was calculated using a Q 10 approach; parameters were measured monthly on branches outside the bag (Damesin et al., 2002, see below). Night foliage respiration was calculated and compared with simulation using the same approach.

To allow the comparison between measurements and model results at the branch level, some modifications were been made in the model. Firstly, the simulated PAR was lowered (-10%) to take into account plastic frame and walls reflectance, which reduces the amount of PAR inside the bag during the measurements [START_REF] Dufrêne | A branch bag technique for simultaneous enrichment and assimilation measurements on beech (Fagus sylvatica L.)[END_REF]. Secondly, the simulated transpiration and growth respiration of leaves were, in this case, simulated per canopy layer.

Above ground wood respiration

CO 2 efflux were measured on stems at 1.30 m height, using temporary clamp-on chambers made of two half-cylinders of transparent, hard acrylic resin. During records, the air inside the chamber was homogenized by a fan. Between two consecutive measurements, the chamber air was flushed with ambient air by increasing the fan speed for several minutes. Carbon dioxide efflux was measured on the airtight closed system using a solid-state IRGA gas analizer. Stem respiration measurements were conducted each month from March 1997 to February 1998 on 15 different trees pooled into five diameter classes.

Efflux of CO 2 from branches was also continuously measured at three levels in the crown, on one tree in 1997 and four trees in 1998. Branch respiration measurements were performed automatically in an open system, each cuvette being scanned sequentially during 5 min every 90 min and monitored by a data logger. Both systems were described and results were analyzed in [START_REF] Damesin | Stem and branch respiration of beech: from tree measurements to estimations at the stand level[END_REF] and [START_REF] Ceschia | Spatial and seasonal variations in stem respiration of beech trees (Fagus sylvatica)[END_REF]. In order to compare directly with the models predictions, these results were scaled up from local measurements to the ecosystem on a monthly basis separating maintenance and growth respirations (see [START_REF] Damesin | Stem and branch respiration of beech: from tree measurements to estimations at the stand level[END_REF] for more details about scaling up).

Soil autotrophic and heterotrophic respirations

Soil CO 2 efflux was measured using a portable closed respiration system (LiCor Inc., USA) as described in Epron et al. (1999a). Every two to four weeks, 12 measurements were recorded at randomly selected locations on each sub-plots during an 8-h period from 8a.m. to 4p.m. Six sub-plots of about 100 m 2 each were randomly chosen within the experimental plot for soil CO 2 efflux measurements. Two 3 m 2 subplots with no tree were created in June 1996 by digging around 1 m deep trench, lining the trench with a polyethylene film and filling it back with soil (see Epron et al., 1999b for a more comprehensive description). Daily means were calculated for both total (main plots) and heterotrophic (trenched plots) soil respirations allowing a comparison with model simulations.

Sap flow

The thermal dissipation technique [START_REF] Granier | Une nouvelle méthode pour la mesure du flux de sève brute dans le tronc des arbres[END_REF][START_REF] Granier | Evaluation of transpiration in a Douglas-fir stand by means of sap flow measurements[END_REF] was used to measure sap flow over the growing season on 10 trees from codominant and dominant crown classes. Besides this experiment, two other preliminary experiments were previously undertaken. The first one was aimed at determining the radial variation of sap flux density within trunks in order to extrapolate to the whole trunk the sap flow measured in a 2 cm wide ring. A correction coefficient was then derived. A second experiment allowed relating sap flux density to tree circumference in order to scale sap flow from trees to stand [START_REF] Granier | Modelling canopy conductance and stand transpiration of an oak forest from sap flow measurements[END_REF][START_REF] Granier | A lumped water balance model to evaluate duration and intensity of drought constraints in forest stands[END_REF]. Then, the transpiration rate established at the stand level is compared with model simulations.

Rainfall interception: throughfall and stem flow

Cumulative throughfall every seven days was measured using 42 raingauges disposed on a grid over the experimental plot. Throughfall was also measured automatically with two linear collectors, each one being collected with a tipping bucket raingauge and the data was recorded every 30 min (from day 176 to 241 in 1997). Stem flow was measured on seven trees from different circumference classes using spiral collecting rings disposed around the trunks and connected to tanks. Stemflow was measured weekly, hence cumulating one or several rain events, during the leafy period (i.e. six months) in 1997. A more complete description is given in Granier et al. (2000b).

Soil water content

During 1997, volumetric water content (θ) was measured weekly with a neutron probe (NEA, Ballerup, Denmark) in eight 160 cm deep aluminium access tubes. For comparison with model output, volumetric water content was summed for both top (0-30 cm) and deep (30-160 scm) soil layers.

The eddy covariance data set

The net exchange of both CO 2 and H 2 O between the soil-forest ecosystem and the atmosphere was measured on the meteorological tower from January to December 1999 using the eddy correlation method [START_REF] Leuning | Eddy-covariance CO 2 flux measurements using open-path and close-path CO 2 analysers. Corrections for analyser water vapour sensitivity and damping of fluctuations in air sampling tubes[END_REF]. A complete description of the system used was given by Granier et al. (2000a). Profile of CO 2 between soil and top of the canopy was measured half-hourly allowing the calculation of CO 2 storage used to correct the net CO fluxes (Fc). During 1997, the system works continuously. Measured half-hourly and daily data are directly comparable with total evapotranspiration (soil and canopy) and net ecosystem exchanges simulated by the model. For seasonal and annual comparison, missing data were gap filled using interpolation when the gap is short (half-hour) or using empirical relationships for daily gaps according to [START_REF] Falge | Gap-filling strategies for long-term energy flux data sets[END_REF].

Annual water balance

Concerning the water balance, the soil evaporation (EP ground ) was not measured in Hesse, but [START_REF] Wilson | A comparison of methods for determining forest evapotranspiration and its components: sap-flow, soil water budget, eddy covariance and catchment water balance[END_REF] have estimated a mean value of 8% for the ratio soil evaporation above total evapotranspiration during the leafy period in a deciduous forest (Walker Branch Watershed) with a similar leaf area index (L max ∼ 6 m 2 m -2 ). Using the fact that ETR = Tr + EP ground + IN, we have estimated the soil evaporation in Hesse using the ratio EP ground /ETR (0.08), the transpiration (Tr) and the net interception (IN) during the leafy period:

EP ground = 0.08 × Tr + IN 1 -0.08 (2)
The transpiration is calculated from the sum of sapflow measurements during the leafy period and the interception is taken from Granier et al. (2000b). An estimation of the drainage (DR) was carried out during the leafy period using the following equation:

DR = P i -IN -RW -EP ground -Tr (3)
RW corresponds to the difference of soil water content between the beginning (May 2) and the end (October 27) of the leafy period used in Granier et al. (2000b) and P i is the cumulated rainfall during the same period.

Annual carbon balance

Both CO 2 and H 2 O fluxes, measured at different time steps, were summed over the year. Additionally, water fluxes were summed when leaf area index is maximum, from day 132 (May 12) to 259 (September 16). We used biomass increments estimated in 1997 for the stand by Le [START_REF] Goff | Root biomass and biomass increment in a beech (Fagus sylvatica L.) stand in North-East France[END_REF] and data from [START_REF] Bauhus | Fine-root growth in beech (Fagus sylvatica) forest gaps[END_REF] for fine roots. Soil heterotrophic and autotrophic respirations were estimated by [START_REF] Epron | Seasonal dynamics of soil carbon efflux and simulated rhizosphere respiration in a beech forest[END_REF]. The maintenance leaf respiration was estimated using measured nocturnal respiration in branch bags. The woody branch respiration was subtracted from measurements [START_REF] Damesin | Stem and branch respiration of beech: from tree measurements to estimations at the stand level[END_REF] and monthly coefficients for leaves (Q 10 and basal respiration) were fitted across the data. Then, we applied these coefficients half-hourly over 24 h to calculate daily and finally, yearly leaf maintenance respiration. However, to allow a comparison with the model output, the level of respiration inhibition during daylight used in the model was also applied to daylight leaf measurements.

The gross primary production (GPP) is estimated by two methods: either by summing autotrophic respiration values (R autotrophic ) and measured biomass increments (Eq. ( 1)) or calculated from fluxes measured by eddy covariance (EC) as described in Granier et al. (2000a). GPP = dB aerial wood + dB coarse roots + dB leaves +dB fine roots + R autotrophic (4)

In the same way, the ecosystem respiration (R eco = R heterotrophic + R autotrophic ) is calculated either by sum of each components of measured respiration or by estimation from EC measurements at a stand scale. The root respiration (needed in the first approach) is calculated as the difference between total soil respiration and heterotrophic respiration. The total annual soil respiration is set to the value of 663 gC m -2 year -1 (Epron et al., 1999a,b) and a proportion of 52% of heterotrophic respiration in the total soil respiration is used [START_REF] Epron | Seasonal dynamics of soil carbon efflux and simulated rhizosphere respiration in a beech forest[END_REF]. Foliage maintenance respiration was estimated from the branch bag measurements: monthly Q 10 was derived from nocturnal measurements (only when no leaf growth occurs) and used to estimate a continuously foliage respiration assuming a daylight inhibition of 62% according to [START_REF] Villar | Dark leaf respiration in light and darkness of an evergreen and a deciduous plant species[END_REF] The growth respiration of leaves was not independently measured and we calculated it simply as the ratio of biomass increments estimated from LMA measurements and the construction cost for respiration (equal to 0.2, cf. [START_REF] Niinemets | Energy requirement for foliage formation is not constant along canopy light gradients in temperate deciduous trees[END_REF].

Finally, the annual net ecosystem exchange is calculated (i) as the difference between GPP and R eco estimated by sum of fluxes, or (ii) directly summed using half-hourly eddy covariance (EC) measurements.

To compare the modelled NEE and the EC measurements, we also computed the sum of fluxes during different periods. We separated diurnal and nocturnal fluxes, leafy and unleafy season, with or without rain and before or after August 1 (beginning of edaphic drought).

Model simulations

We simulated all the processes either with mean values of input parameters or by taking into account an uncertainty on 17 key input parameters. To determine the key input parameters, 150 sets of 1000 simulations were performed with a 10% Gaussian random noise on each of the 150 input parameters. An uncertainty analysis is then presented to evaluate the error on the annual outputs caused by uncertainties in these sensitive inputs parameters. Uncertainties on these parameters are estimated using in situ data. These uncertainties are used to create an ensemble of 17,000 simulations where the values of the 17 key parameters are randomly selected using Gaussian random distributions. The methodology of the uncertainty analysis and the key parameters are described in [START_REF] Dufrêne | Modelling carbon and water cycles in a Beech forest. Part I: Model description and uncertainty analysis on modelled NEE[END_REF].

Results and discussion

Profile of PAR

The extinction coefficients, calculated using the profile of PAR simulated by the model, appear very close to the measured values (Table 1). However, the correlation coefficient between the fitted exponential curves and the measurements, is not always high (r 2 ranging from 0.43 to 0.80) and the measured PAR extinction often decreases at a higher rate than the fitted exponential relationships (data not shown).

Global net radiation

As shown in Fig. 1, we observe a very close relationship between daily simulated and daily measured global net radiation values (Rn) (y = 1.05x -1.13, r 2 = 0.97). Nevertheless, during leafless period, the model sligthly underestimates the low values of global net radiation.

Branch photosynthesis, transpiration and night respiration

Considering the branch bag at the top of the canopy, the model captures well the seasonal pattern and the short term variation of daily net photosynthesis (Fig. 2a, y = 0.87x + 0.54, r 2 = 0.78, d.f.1 = 158) showing an underestimation during the leaf growing phase (until end of May) and an overestimation starting in July and sensitive mainly during September and October. We found a good relationship between simulated and measured tree transpiration (Fig. 2c, y = 1.36x -0.01, r 2 = 0.89, d.f. = 158) with overestimations, except an underestimation during the water stress period.

With the aim to test if a decrease in photosynthetic capacity during the year could explain the increasing overestimation in net photosynthesis by the model, we introduced a seasonal change in nitrogen content measured on sun leaves of mature beech trees in Orsay (data not published, [N] = 0.191D l -0.464, r 2 = 0.89, d.f. = 16, with D l the daylight duration in hour and [N] in %). Results presented in Fig. 2 (dotted lines) show a clear improvement of pattern in simulated net photosynthesis during the second part of the season (r 2 increases from 0.78 to 0.85) without significant change in transpiration rate. Moreover, simulated nocturnal foliage respiration is largely improved at the end of the season, when nitrogen dynamic is considered (Fig. 2b). Despite the fact that changes in nitrogen content were measured in a different place and a different year, introduction of this empirical relationship improves the model predictions. As the simultaneous decrease in nitrogen content and photosynthetic capacity during the year has been described by different authors for deciduous trees [START_REF] Wilson | Spatial and seasonal variability of photosynthetic parameters and their relationship to leaf nitrogen in a deciduous forest[END_REF][START_REF] Frak | Spatial distribution of leaf nitrogen and photosynthetic capacity within the foliage of an individual tree: disentangling the effects of local light quality, leaf irradiance, and transpiration rate[END_REF], we can assume that this phenomenon occurred in Hesse during 1997. Considering "the lower branch bag", both net photosynthesis and transpiration are largely underestimated by the model (Fig. 3a). Using measured PAR and LMA inside the bag rather than simulated PAR and LMA, reduces in a large extent the discrepancy between model and measure, but an underestimation persists. With the aim to understand this remaining difference, we tested the possible effects on net photosynthesis of an increase from top to bottom in the canopy of (i) measured leaf nitrogen content, and (ii) leaf absorbance (according to increase in amount of chlorophyll and leaf mass per leaf area). Both are generally observed in the deciduous canopies [START_REF] Niinemets | Distribution of foliar carbon land nitrogen across the canopy of Fagus sylvatica: adaptation to a vertical light gradient[END_REF][START_REF] Niinemets | A model separating leaf structural and physiological effects on carbon gain along light gradients for the shade-tolerant species Acer saccharum[END_REF] but not considered in the model CASTANEA. However, these effects cannot explain the low simulated photosynthesis.

The simulated nocturnal foliage respiration is largely overestimated all along the leafy period except very early and very late in the season (Fig. 3b). This overestimation could be due to errors in estimating branch respiration inside the bag or to an overestimation of shade leaves respiration by the model.

According to the large observed heterogeneity in the light level at the base of the canopy, we can conclude that the simulated PAR, which is an average value within the considered layer, is not representative of the local branch bag condition. This is probably the main cause for net photosynthesis differences between observed and simulated values. The simulated transpiration is also underestimated in "the lower branch bag" and this corroborates the conclusion founded with photosynthesis simulations: as for the PAR, the average global radiation budget is probably not representative of the local branch bag condition.

Aerial wood respiration

Daily maintenance branch (Rbr) and stem (Rst) simulated respirations are very similar to the those derived from measurements (Rbr simulated = 0.90Rbr measured + 0.01, r 2 = 0.997; Rst simulated = 0.96Rst measured + 0.02, r 2 = 0.991). The model used constant Q 10 and basal respiration parameters on an annual basis for each organ in opposition to the field data were monthly measured parameters were used. Consequently, we can conclude that seasonal (i.e. monthly) changes in those parameters have a negligible influence on both daily (data not shown) and yearly (see Table 2) maintenance respiration of aerial woody organs.

Soil autotrophic and heterotrophic respiration

Simulated heterotrophic soil respiration has already been tested by [START_REF] Epron | Seasonal dynamics of soil carbon efflux and simulated rhizosphere respiration in a beech forest[END_REF]. However, according to slight changes in model parameterization and also considering the interdependence of several processes in the model (e.g. when photosynthesis rate changes, growth of roots and consequently root growth respiration change without changing allocation coefficients), we examine both microbial heterotrophic respiration and total soil respiration (i.e. heterotrophic + autotrophic). Simulated heterotrophic soil respiration is very similar to the previous version of the model and compares well with measurements (see Fig. 1a in [START_REF] Epron | Seasonal dynamics of soil carbon efflux and simulated rhizosphere respiration in a beech forest[END_REF]. Total simulated soil respiration is presented in Fig. 4, showing a good agreement with measured values except from mid-June to mid-August when simulated values are largely underestimated. This underestimation is largely due to low simulated values of autotrophic root respiration. One hypothesis could be an underestimation of the fine root biomass or of the fine roots growth during the summer, both controlling the fine root respiration. The fine root biomass could be misestimated from Table 2 Comparison between annual simulated (with or without uncertainties on 17 key input parameters) and measured carbon fluxes and biomass increment (in gC m -2 year -1 ) with two different approaches: sum of separated fluxes and integrated flux by eddy covariance measurements (Granier et al., 2000a

Net ecosystem carbon exchange (NEE)

Half-hourly time scale

In the half-hourly time scale analysis, we voluntarily excluded winter and September dry periods (see daily time scale and seasonal pattern below); all the results correspond to a period comprised between May and end of August. During this period, the model captures well the daily pattern of NEE as shown on the few days during June in Fig. 5a. Simulations do not reproduce the high variability of nocturnal fluxes. From May to September simulations are closed to measurements (Fig. 5b). Nevertheless, we note a better fit when we consider data averaged over 3 h rather than half-hourly data (half-hourly: NEE sim = 0.76NEE mes + 1.65, r 2 = 0.72; 3-h: NEE sim = 0.90NEE mes -1.12, r 2 = 0.86). We can conclude that the model is not able to capture properly all the short-term (half-hourly) NEE variations, especially at night.

Daily time scale and seasonal pattern

During the winter period, the total "ecosystem respiration" (coarse and fine root + soil heterotrophic) was estimated accurately by the model including the autumnal respiration peak (Fig. 6a). The length of the growing season was well reproduced and despite the simple hypothesis assuming a constant leaf nitrogen content over the season, the seasonal pattern of NEE is properly captured (Fig. 6a). During all the leafy period, we note a trend to overestimate the lowest daily NEE values and to underestimate the highest ones.

From early September to the end of the leafy period (end of October) simulated NEE are systematically overestimated.

By introducing a seasonal decrease in leaf nitrogen content (same way as in a Branch Bag) we do not improve strongly the accuracy of the model prediction (r 2 = 0.81 instead of 0.79) and contrary to the Branch Bag, simulated NEE remains overestimated in September and October. During this period, the predicted soil water content was above measured values (see Fig. 8c) and this led to an overestimation of photosynthesis and consequently of NEE. By forcing the measured soil water content we tested that this underestimation of the water stress could explain all the differences between the simulated daily NEE and the measured one between the September 18 and the October 9 but this was not the case later in the season.

If we consider NEE only during the daylight period (Fig. 6b) then predictions are improved (r 2 = 0.87; less scatter) except during September dry period (large overestimation). This is due to a better prediction of daylight fluxes compared with night fluxes, except during the dry period when night respiration was overestimated (Fig. 6c), partly compensating for the assimilation overestimation. During the leafy period, the day to day variability of the nocturnal NEE is much higher than during the leafless period. Like in the case of the half-hourly time step, the model is not able to reproduce this large variability observed between nights.

Transpiration, evapotranspiration and soil water balance

Half-hourly time scale

As for the NEE, in the half-hourly time scale analysis, the results correspond to the period comprised between May and the end of August. Simulated transpiration rates compare well with sapflow measurements (TR sim = 0.92TR mes + 0.15, r 2 = 0.88). By introducing a time lag of one half-hour to take into account the stem water buffer, the relationship was slightly improved (r 2 increases from 0.88 to 0.90 without any significant change in slope and intercept). A time lag between sapflow and canopy transpiration (directly measured or estimated by a model) has often been reported (Granier et al., 2000b) and is observed here again (Fig. 7a). Furthermore, we note only a slight improvement, considering data averaged over 3 h rather than half-hourly data (r 2 increases from 0.88 to 0.93, Fig. 7b). We can conclude that the model is more able to capture properly the short-term (half-hourly) TR variation measured using sapflow than NEE variation measured using the eddy-covariance technique.

The relationship between simulated and measured evapotranspiration is much more scattered (data not shown) than the transpiration one (ETR sim = 0.74ETR mes + 0.99, r 2 = 0.61, d.f. = 5952). The relationship was improved when only considering periods without rain (ETR sim = 0.75ETR mes + 0.85, r 2 = 0.67, d.f. = 2689).

Daily time scale and seasonal pattern

If we consider only days without rainfall, CAS-TANEA reproduces properly the total daily evapotranspiration (Fig. 8a) during the winter and during the growing season as well (ETR sim = 1.04ETR mes + 0.25, r 2 = 0.87, d.f. = 324). However, we note a tendency to overestimate the lowest daily values even during periods without water stress (i.e. from mid May to end of July). For rainy days, model largely overestimates measured values (data not shown) all along the year (ETR sim = 0.95ETR mes + 1.09, r 2 = 0.43, d.f. = 174). We can assume that the model overestimates evaporation during these days, because the simulated transpiration is well correlated with measured sapflow (Fig. 8b, TR sim = 1.00TR mes -0.01, r 2 = 0.93, d.f. = 185), even during rainy days. Energy reaching the soil during the leafy period is generally less then 5% of the incident energy in the Hesse forest because of high LAI values and of planophile leaf angle distribution. Consequently, the soil evaporation is low and the model reproduces these low values (less then 0.6 mm day -1 in average). In consequence we can assume that most differences between observed and measured evapotranspiration come from the part of rainfall, which is intercepted and re-evaporated by the canopy. If we now compare measured and simulated net interception of rainfall during the leafy period, we find very similar values (Table 3), which is not surprising if we consider that rainfall interception parameters were partly calibrated using the same data set [START_REF] Dufrêne | Modelling carbon and water cycles in a Beech forest. Part I: Model description and uncertainty analysis on modelled NEE[END_REF]. Moreover, the model simulates properly or even overestimates the available soil water content, whereas the contrary would have occurred if net interception, canopy evaporation or soil evaporation were systematically overestimated. According to the observations mentioned above, we believe that the model reproduces properly wood and canopy interception and evaporation. On the other hand, we can conclude that (i) eddycovariance measurements underestimate water vapor fluxes during rainy events (Granier et al., 2000b[START_REF] Granier | Deciduous forests: carbon and water fluxes, balances and ecophysiological determinants. Ecological studies[END_REF], and (ii) the model overestimates the lowest values of daily evapotranspiration. As mentioned above, the model reproduces accurately the soil water content evolution until end of August, but when the soil water stress increases it provides overestimated values (Fig. 8c). The transpiration simulated during the discrepancy period (i.e. September 23 until October 9) is close to that measured by the sapflow technique (Fig. 8b) with a tendency to underestimate (cumulated deficit is 8 mm). During this period no rain occurred, thus there was no water evaporated from rainfall interception. In consequence this difference between predictions and measurements can only result from an underestimation of the simulated drainage.

Water use efficiency

While drought effects on assimilation have been extensively studied at the leaf level, few attempts have been made to test the assumption concerning the way to take into account these effects at the stand level [START_REF] Reichstein | Severe drought effects on ecosystem CO 2 and H 2 O fluxes in three Mediterranean evergreen ecosystems: revision of current hypotheses[END_REF]. In Quercus ilex ecosystems, [START_REF] Reichstein | Severe drought effects on ecosystem CO 2 and H 2 O fluxes in three Mediterranean evergreen ecosystems: revision of current hypotheses[END_REF], demonstrated that the approach of [START_REF] Sala | Simulations of canopy net photosynthesis and traspiration in Querens ilex L. under the influence of seasonal drought[END_REF] for including water stress effects lead to discrepancies in water use efficiency, in fluxes and in annual balances. The exact result also depends on the process of parameterization [START_REF] Reichstein | Inverse modelling of seasonal drought effects on canopy CO 2 /H 2 O exchange in three Mediterranean ecosystems[END_REF].

We have tested the way of including the effects of water stress on assimilation in the Hesse site for three contrasted years. For that aim, the water use efficiency (WUE) was estimated from the ratio between daily GPP (eddy covariance estimate) and daily transpiration (sapflow estimate) from 1997 to 1999. In 1997, the drought occured very late in the season, while in 1998 it occured in the middle of the season. There was no water stress in 1999. In 1998 and 1999 the seasonal evolution of WUE is well described by the model (Fig. 9). But in 1997, while the drought occurs very later in the season, the model simulates a too pronounced increase of WUE from end of August. Consequently, for this species when the water stress is moderate or occurs early, the model of [START_REF] Sala | Simulations of canopy net photosynthesis and traspiration in Querens ilex L. under the influence of seasonal drought[END_REF] allows an accurate simulation of the WUE dynamics. But when drought and leaf ageing occur simultaneously, which is not common in European oceanic climate, a strong decrease in photosynthetic capacities should probably also be taken into account.

Annual carbon and water balances

Water balance

The annual simulated transpiration (311 mm) is close to that derived from sapflow measurements (313 mm). By contrast, the total evapotranspiration is much higher in the model (596 mm) than that measured by EC (351 mm). As explained above (in the daily pattern of evapotranspiration) this difference mainly occurs during rainy days. There is probably an underestimation of measured evapotranspiration by the EC when canopy is wet. This hypothesis is confirmed by the analysis of the annual water balance during the leafy period (Table 3). The sum of measured interception, soil evaporation and transpiration (473 mm) is close to the value given by the model for total evapotranspiration (489 mm), and strongly differs from the value measured by EC (317 mm).

We also note that the model tends to underestimate the drainage (64 mm instead of 89 mm). [START_REF] Granier | A lumped water balance model to evaluate duration and intensity of drought constraints in forest stands[END_REF] have reported that several field experiments have shown that important drainage could occur in the lower soil layers even in dry soils. This could explain our underestimation of both drainage and soil water deficit at the end of the leafy season.

Carbon balance

Simulated and measured (or estimated from measurements) values are presented in Table 2. We calculated the annual net ecosystem exchange using two methods: Firstly, we estimated the annual carbon balance by summing all individual fluxes. Secondly, NEE was measured by the eddy covariance technique, and R eco and GPP were calculated using both eddy covariance measurements and Q 10 based relationship (Granier et al., 2000a).

Note that, concerning leaf growth respiration, the model uses a construction cost for leaf growth respiration established from local measurements and so we cannot compare measurements and simulations, which are not independent. However, the model validation for total respiration is not concerned since leaf growth respiration corresponds only to 3% of the total annual respiration.

The model provides an accurate estimation of all respiration and biomass increments, independently measured except for root respiration, which is underestimated. Consequently, R eco calculated (from measurements) using the first method is above the modeled one (+7%). The annual measured GPP is also slightly higher (+2.5%) than the simulated one, and as a result the model overestimates the net ecosystem exchange of 44 gC (i.e. +10%). To conclude, we note that a precise estimation of NEE requires that the model accurately reproduces both GPP and R eco : in our case a small difference in root respiration causes a strong difference in NEE. For these reasons, the belowground allocation and respiration require more measurements and modeling works. The bias caused by assuming that the input model parameters are spatially constant (cf. Dufrêne et al., 2005), cannot be an alternative explanation of the difference observed between the modeled NEE and the NEE measured by the sum of fluxes approach (Table 2).

We can also observe that GPP, R eco and NEE estimated by summing CO 2 fluxes from the different compartments differ strongly from those estimated by EC measurements (respectively, 25%, 15% and 63%). Granier et al. (2000a) had the same conclusion, even if the difference was smaller (for GPP: 13%, instead of 25% in our calculation). According to the differences between the two approaches for estimating GPP, R eco and NEE from measurements and the good agreement between simulated values and the first approach estimation, the model prediction shows large differences with EC measurements (respectively, 22%, 4% and 80% for GPP, R eco and NEE).

It is not surprising that the model gives results closer to the first measurement method, even if the sub-models were not calibrated but parameterized using independent data sets because the way to scale-up from local measurements (or simulations) to stand estimation (or simulation) is very similar. Moreover, the model parameters were measured at similar scales as the measurements of local fluxes used for validation.

It is necessary to consider different potential sources of errors to understand the large discrepancies between the results from the two approaches using measurements (i.e. scaling local fluxes from each process and integrative EC data).

Firstly, the respiration of the dead woody organs (mainly branches remaining on the soil after thinning) is not taken into account in the model and in the "summed-fluxes" approaches, whereas this respiration is incorporated in the integrated fluxes (EC). This could explain, at least partly, the difference in NEE (Table 2).

Secondly, in order to estimate the R eco from EC measurements, we summed the night ecosystem respiration measurements and the daily ecosystem respiration. Daily ecosystem respiration is calculated from a Q 10 relationship established between night respiration measurements and air temperature and applied during the day using diurnal temperatures. The daily respiration calculated in this way could be underestimated, which could explain the difference in R eco (Table 2).

Nevertheless, these two hypotheses are conflicting. An increase of the R eco by taking into account dead wood in the sum of fluxes approach, leads to amplify the discrepancy with the EC estimations. On the other hand, during the leafless period the simulated fluxes are closer to the EC measurements (Table 4).

As the model gives results close to the sum of fluxes approach, it allows to test some hypotheses to explain the differences with the EC measurements. The diurnal fluxes during the leafy period correspond to the difference between GPP and diurnal R eco . A comparison of measured (EC) and modeled diurnal fluxes is presented in the Table 4 by separating the days with or without rain and the days before or after the drought period. The model overestimates the diurnal fluxes, essentially for rainy days and after the August 1. Before the drought the model slightly overestimates the diurnal fluxes of 10% for days without rain and 19% for the others. During and after the drought the overestimation becomes 53 and 139%. The rainy effect is maybe due to an underestimation of CO 2 fluxes by EC during rainy events (as for the evapotranspiration) and can partly explain the differences between the two approaches.

Concerning the seasonal effect, the overestimation of diurnal modeled NEE during the drought was already discussed for daily results. However, it is necessary to understand why, despite this overestimation, the modeled GPP is close to the estimation made by We separated diurnal and nocturnal, leafy and leafless, with and without rain, before and after the August 1 (drought period).

the sum of fluxes approach. It is thus possible that the drought had a greater effect on EC fluxes than on the local fluxes. To explain this, an interesting hypothesis concerns a possible scale effect with different forest areas generating different CO 2 fluxes. The area considered to measure model parameters and local fluxes (used to scale up) is different from the area concerned by EC measurements [START_REF] Goulden | Measurements of carbon sequestration by long-term eddy covariance: methods and a critical evaluation of accuracy[END_REF]. Moreover, the forest area concerned by EC measurements (i.e. footprint) changes according to wind direction and intensity. In particular, night flux came from a different area than daily fluxes and then estimating daily respiration using night measurements could lead to some bias.

To test this hypothesis, coupling footprint of EC-fluxes and carbon fluxes models parameterized spatially appears to be a promising approach.

Conclusion

CASTANEA, a biophysical model, was applied to estimate stocks and fluxes of both carbon and water over a deciduous forest from hourly to annual time scales. The simulations were compared to measurements made at different spatial scales. The model reproduced quite well all the measured individual processes, except the root respiration, which was underestimated during summer. However, discrepancies with EC measurements were observed. Concerning evapotranspiration, error on EC measurements during rainy days could explain the difference between modeled and measured values. On the contrary, regarding CO 2 , the reasons for discrepancies between the sum of local measurements and simulations, on one hand and EC measurements, on the other hand, remains to be clarified. However, the scale effects of measurements and simulations, appear to be a good explanation and should be further studied.

We have also underlined the need of a very accurate estimation of each of the individual processes to accurately assess the resultant (i.e. the net ecosystem exchange). Some important processes concerning carbohydrate allocation or fine roots turnover were not measured and consequently not directly tested (only indirectly using respiration). We can conclude that more effort should be made on studying and modeling both root respiration and root turnover.

The fact that the model was validated for most of the individual processes, which is generally not the case, lead us to have confidence in its general validity and applicability on other sites and species, where similar studies are currently undertaken. These new tests and validations should also include long-term fluxes and growth data.

Fig. 1 .

 1 Fig. 1. Simulated vs. measured daily net radiation (MJ m -2 ) during 1997. Results are separated between leafy period (᭹) and leafless period ( ).

Fig. 2 .

 2 Fig. 2. Comparison between daily measurements (᭹) and simulations in upper branch bag (with constant nitrogen in solid line and variable nitrogen in dotted line). (a) Daily net assimilation in gC m -2 day -1 ; (b) nocturnal respiration of leaves in gC m -2 day -1 ; (c) transpiration of leaves in mm day -1 .

Fig. 3 .

 3 Fig. 3. Comparison between daily measurements (᭹) and simulations (with simulated PAR in solid line and forcing with measured PAR in dotted line) in lower branch bag. (a) Daily net assimilation in gC m -2 day -1 ; (b) leaf nocturnal respiration in gC m -2 day -1 ; (c) leaf transpiration in mm day -1 .

Fig. 4 .

 4 Fig. 4. Simulated and measured daily soil respiration (heterotrophic + root autotrophic) in Hesse during 1997.

Fig. 5 .

 5 Fig. 5. Measured and simulated half hourly NEE (mol CO 2 m -2 s -1 ) in Hesse in 1997. (a) Daily pattern of simulated and measured NEE, from May 23 to May 27; (b) comparison of measured and simulated half hourly NEE averaged over 3 h during 1997. Linear regression and the straight line y = x are also plotted.

Fig. 6 .

 6 Fig. 6. Temporal dynamic of daily NEE in gC m -2 day -1 (measured in black circle and simulated in solid line) for constant nitrogen (N cst ) and grey line for variable nitrogen (N var ). (a) Sum over the entire day (Daily NEE); (b) Sum over the diurnal period (Diurnal NEE); (c) Sum over the nocturnal period (nocturnal NEE). Results for N cst are also plotted as measured vs. simulated.

Fig. 7 .

 7 Fig. 7. Half-hourly transpiration (TR mmol H 2 O m -2 s -1 ) measured and simulated in Hesse during 1997. (a) Daily pattern of simulated and measured TR, from May 23 to May 27; (b) comparison of measured and simulated half hourly TR during 1997 without time lag. Linear regression and the straight-line y = x are also plotted.

Fig. 8 .

 8 Fig. 8. Temporal dynamic of daily water fluxes measured and simulated. (a) Evapotranspiration (ETR) in mm day -1 only for days without rain; (b) transpiration (TR) in mm day -1 ; (c) relative extractable water (REW) in the soil. For (a) and (b), results are also plotted as measured vs. simulated.

Fig. 9 .

 9 Fig. 9. Temporal dynamic of daily water use efficiency (GPP/TR) derived from eddy covariance and sapflow measurements (grey dashed line) and simulated by CASTANEA (in solid black line). Results are sliding average over 14 days. The water stress period is represented in grey and defined as the days when the soil relative extratable water (REW) is below 0.4.

Table 1

 1 Light extinction coefficients (k) calculated using the simulated and measured profiles of PAR in the canopy

	Julian day (1997)	Solar elevation (GMT) (h)	r 2	n	Measured k	Simulated k
	217	14:56	0.74	45	0.63	0.66
	223	8:41	0.55	73	0.69	0.72
	223	10:07	0.43	73	0.65	0.61
	223	11:46	0.66	73	0.64	0.61
	232	12:47	0.80	75	0.67	0.66
	232	14:45	0.68	78	0.59	0.66
	Mean				0.65	0.65

  )

	Variable	Simulated	Simulated with	Integrated	Separated	Methods	Sources
		without	uncertainties	measured	measured	concerning the
		uncertainties		flux	fluxes	measurements
						of separated
						fluxes
	Sum of separated fluxes
	Biomass increment	363	347 ± 48		379	Dendrometric measurements Le Goff and Ottorini
	(aerial wood)						(2001)
	Biomass increment	84	80 ± 10		76	Root system excavations	Le Goff and Ottorini
	(coarse roots)						(2001)
	Biomass increment	175	175 ± 19		131	Litter collection	Granier	et	al.
	(leaves)						(2000a)
	Biomass increment	172	166 ± 27		171		Bauhus and Bartsch
	(fine roots)						(1996)
	Increment of carbo-	-8	-29 ± 37		-	Included above (see text)
	hydrate storage
	Rroots	220	215 ± 17		325	By difference (see text)	Epron et al. (2001)
	Rm stems	89	89 ± 17		77	Chambers	Damesin	et	al.
							(2002)
	Rm branches	82	83 ± 13		75	Chambers	Damesin	et	al.
							(2002)
	Rc wood	138	131 ± 24		130	Chambers	Damesin	et	al.
							(2002)
	Rm leaves	171	170 ± 31		168	Branch bags	This study
	Rc leaves	35	35 ± 20		26	Calculated
	GPP	1518	1456 ± 151	1245 1558	Sum of fluxes
	Rheterotrophic	321	321 ± 5		338	Trenched plots	Epron et al. (2001)
	Reco	1057	1044 ± 75	988 1139	Sum of fluxes
	NEE	463	413 ± 119	257	419	Sum of fluxes

mid-June to mid-August, if carbon allocation to roots or fine roots turnover vary during the season while the used version of the model assumes constant allocation coefficients and turnover from budburst to leaf fall.

Table 3

 3 Comparison between simulated and measured water fluxes (in mm year -1 ) during the leafy period (from May 2 to October 27)

	Variable	Simulated	Measured	Method	Sources
	Transpiration	316	310	Sapflow	This study
	Net interception	111	125	Raingauges under canopy	Granier et al. (2000b)
	Soil evaporation	59	38	Calculation (see text)
	Evapotranspiration	486	317	Eddy covariance	Granier et al. (2000b)
	Drainage	64	89	Calculation (see text)
	Rainfall	438	438	Raingauge above canopy

Table 4

 4 Comparison between annual NEE fluxes (gC m -2 per year), simulated and measured by EC techniques

	NEE	Simulated	Measured	Variation in %
	Diurnal leafy without rain before the August 1	253	230	+10
	Diurnal leafy with rain before the August 1	311	261	+19
	Diurnal leafy without rain after the August 1	282	184	+53
	Diurnal leafy with rain after the August 1	149	62	+139
	Nocturnal leafy	-340	-296	+15
	Diurnal leafy	994	737	+35
	Leafy	644	441	+46
	Leafless	-181	-178	-2

d.f.: degree of freedom, i.e. number of points considered in the regression calculation.

Acknowledgments

We are grateful to the Office National des Forêts (ONF, the French National Office for Forestry) for giving us facilities for in situ measurements. Financial support was provided by the European community through the Euroflux program (contract ENV4-CT95-0078). The author acknowledge Bernard Saugier and Franz Badeck for valuable discussions. We also thank J.Y. Pontailler for the branch bags measurements and Claire Damesin for respiration measurements. The authors thanks the anonymous reviewers for their constructive remarks that significantly helped strengthen the paper.