## 2 Materials and Methods
### CO\({}_{2}\) Flux Calculation and Gap Filling  
CO\({}_{2}\) fluxes were calculated in \(\upmu\)mol\(\cdot\)m\({}^{-2}\cdot\)s\({}^{-1}\) based on the gas concentration changes in the chamber headspace over the closure time using linear regression, as described in [PERSON] et al. [35; 36]. Before the flux calculation, the measured CO\({}_{2}\) concentrations were corrected for water dilution by applying water vapor correction in accordance with [PERSON] et al. [38]. Fluxes were calculated based on a minimum of 40 s of data after the exclusion of the first 10-15 s of data to eliminate data noise originating from disturbances occurringafter deployment of the chamber. In order to avoid underestimation of the fluxes caused by possible gas saturation or changes in the chamber headspace microclimate (as described in [PERSON] et al. [39]), the steepest part of the regression was used to calculate fluxes after excluding the end part of the data series, wherein possible disturbances occur and disturb the linear slope of the regression.  
Gap filling of the CO\({}_{2}\) data series in the periods between campaigns was carried out by applying the empirical model described by [PERSON] [40] and further elaborated by [PERSON] et al. [41]. Due to a limited amount of flux data, the gap-filling procedure was applied for each of the four plots (HWL, HWL_Si, LWL, and LWL_Si), where all data from replicates were integrated into one data pool. At the first step, for each of the campaigns, the temperature-dependent respiration model of [PERSON] and [PERSON] [42] was fitted to the measured Reco fluxes and air temperatures to estimate the Reco modeling parameters (R\({}_{\text{ref}}\) and E\({}_{\text{o}}\)):  
\[\text{Reco}=\text{R}_{\text{ref}}\cdot\text{e}^{\text{E}_{\text{o}}(\frac{1}{ \text{1 ref}-\text{T}_{0}}-\frac{1}{\text{T}-\text{T}_{0}})} \tag{1}\]  
where Reco is the measured ecosystem respiration (\(\mu\text{mol}\cdot\text{m}^{-2}\cdot\text{s}^{-1}\)), R\({}_{\text{ref}}\) is the respiration at the reference temperature of 283.15 K (T\({}_{\text{ref}}\)), E\({}_{\text{o}}\) is activation energy (K), T\({}_{0}\) is the constant starting temperature (227.13 K), and T is the mean air temperature for the time of chamber closure. The Reco modeling parameters were then used to calculate Reco fluxes for the time and temperatures of the NEE measurements in order to calculate gross primary production (GPP) by subtracting the modeled Reco from the measured NEE fluxes.  
In the second step, the PAR-dependent campaign-specific GPP model was applied by fitting the rectangular, hyperbolic light response function of [PERSON] (1913) to the calculated GPP (\(\mu\text{mol}\cdot\text{m}^{-2}\cdot\text{s}^{-1}\)) and measured PAR (\(\mu\text{mol}\cdot\text{m}^{-2}\cdot\text{s}^{-1}\)) to estimate the GPP modeling parameters (GPmax and \(\alpha\)):  
\[\text{GPP}=\frac{\text{GP}_{\text{max}}\times\ \alpha\ \times\ \text{PAR}}{ \alpha\ \times\ \text{PAR}\ +\ \text{GP}_{\text{max}}} \tag{2}\]  
where GPmax is the maximum rate of CO\({}_{2}\) fixation at infinite PAR (\(\mu\text{mol}^{-1}\cdot\text{m}^{2}\cdot\text{s}^{-1}\)), while \(\alpha\) is the light use efficiency (mol CO\({}_{2}\) mol\({}^{-1}\) photons).  
In the next step, the calculated Reco and GPP model parameters were interpolated linearly in the periods between campaigns (as described in [PERSON] et al. [36]) in order to calculate Reco and GPP based on the measured air temperatures and PAR, respectively. Finally, NEE was calculated from the formula NEE = GPP + Reco. All fluxes were calculated with 30 min time steps and were recalculated into mass units in order to calculate daily and seasonal sums of Reco, GPP, and NEE.