## 2 Materials and Methods
### Eddy Covariance Data Processing  
Raw net ecosystem exchange (NEE) fluxes were calculated using commercial EddyPro software (version 7.0.1, Lincoln, NE, USA) with default settings. According to this mode, several processing steps were applied: despiking, amplitude resolution, drop-outs, absolute limits, skewness and kurtosis, discontinuities analysis. The following corrections were then made: angle of attack, since a Gill anemometer was used, double rotation correction, the time delay due to the separation of measuring instruments, block averaging and density correction (WPL). Basic filtering included stationarity test according to [PERSON] and [PERSON], (2008), and thus only data with \"0\" flag were used here for further computation.  
Figure 3: In situ allometric equation derived as the relationship between total dry biomass and diameter at breast height (DBH) of five model trees.  
Fluxes measured during low turbulent conditions were removed by using friction velocity threshold as a sufficient turbulence parameter [29; 30; 31]. For this purpose, the measurement period was arbitrarily divided into monthly sub-periods. Friction velocity threshold (u*th) was calculated for each month and the data was filtered in these periods according to the given u*th value. Outliers were then removed, assuming monthly integration periods and two standard deviations value as a criterion. The \"anomalize\" package for R was used for this purpose [32]. The rate of concentration change (i.e., the storage flux) of CO2 and H2O (\(\Delta\)SCO2 and \(\Delta\)SH2O, respectively) in the air column below the EC system was also calculated in the EddyPro software [33]. Briefly, storage fluxes are obtained as the difference between the average concentration in the beginning and in the end of a specific averaging period (most often 30 min, as also in our study). This yielded similar results to those obtained with the concentration profile approach [34]. Both \(\Delta\)SCO2 and \(\Delta\)SH2O values were accounted for in the final fluxes calculations [35]. Finally, NEE fluxes gap-filling and partitioning into GPP and ecosystem respiration (R\({}_{\text{eco}}\)) was performed using the \"REddyProc\" package [30] in the R environment [26], following the [PERSON] night-time approach of using the air temperature measured under a canopy at 2 m above ground for the R\({}_{\text{eco}}\) fluxes estimation [36; 37; 38; 31; 39]. In addition, water fluxes (given as evapotranspiration, ET) obtained by the EC method were calculated and gap-filled according to the same methodology as for the carbon fluxes, also using the REddyProc package [30].  
The assimilation of atmospheric CO2 is undoubtedly correlated with wood growth. However, due to the complexity of distinguishing irreversible seasonal wood growth from the increase in tree volume that results from reversible swelling and contraction of the stem, it was very difficult to determine this relationship, especially over a period of less than one year. The occurrence of the periodic reversible shrinkage or expansion of DBH mainly resulted from changes in the water content of plant tissue, and not from the actual increase in biomass. Nevertheless, there are various concepts regarding tree growth during periods of reversible contraction in response to water stress conditions. One such example is a concept of \"zero growth\" during periods of stem shrinkage, which utilizes a water-related growth threshold [40]. During a dry period, the stem may increase in size as a result of water-related swelling of stem tissues [41; 42]. For example, a slight increase after longer periods of water shortage was observed in two deciduous and two evergreen trees species in one dry and two wetter sites in Switzerland; this increase was interpreted as activity during periods of stem swelling and shrinkage [40]. Therefore, due to these difficulties, we have presented the annual stem increment and the total biomass growth for both years.