## 5 Conclusions
Logistic curve model of \(NPP_{WBI}\) (Y) has an upper asymptote at \(NPP_{WBI\_annual}\) and the inflexion point and the maximum of \(Y\)’ at \(DOY_{max\_WBI}\). \(DOY_{max\_LF}\) is assumed to occur at the second inflexion point of \(Y\)’ i.e., the point where third derivative \(Y^{\prime\prime\prime}=0\) (**panel A**); annual dynamics of \(NPP_{WBI}\) and \(NPP_{LF}\) modelled using the same parameter \(k\) (see text), and their sum \(NPP_{BM}\) (**panel B**). Figure 6: Evolution, from the year 2008 (age 36) until 2017 (age 45), of tree’s \(DBH\) distribution, by tree species, for stands around eddy covariance tower. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Year** & **Data Coverage before Processing [\%]** & **Data Coverage after Quality Control [\%]** & **Data Coverage after \(u^{*}\) Filtering [\%]** \\ \hline
2008 & 95.5 & 46.6 & 39.4 \\
2009 & 88.2 & 46.8 & 42.1 \\
2010 & 91.9 & 47.0 & 45.4 \\
2011 & 94.3 & 56.5 & 47.7 \\
2012 & 83.3 & 49.8 & 42.7 \\
2013 & 92.8 & 53.0 & 48.4 \\
2014 & 95.7 & 51.4 & 47.3 \\
2015 & 96.0 & 52.9 & 43.2 \\
2016 & 83.8 & 45.1 & 41.6 \\
2017 & 79.0 & 45.0 & 38.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Percentage of half-hourly _NEE_ files before processing, after quality control filtering procedures and after \(u^{*}\) filtering procedure. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Spaceies *** & \multicolumn{4}{c}{**Parameter**} & \multicolumn{2}{c}{**Source**} \\ \hline  & \(b_{0}\) & \(b_{1}\) & \(b_{2}\) & \\ \hline _Alnus glutinosa_ & 4.23243-10\({}^{-5}\) & 2.002354 & 1.001300 & [40] \\ _Carpius betulas_ & 2.96400-10\({}^{-5}\) & 2.022705 & 1.102119 & [41] \\ _Fraximus angusifolia_ & 3.95282-10\({}^{-5}\) & 1.974875 & 1.001444 & [42] \\ _Quercus robur_ & 4.96820-10\({}^{-5}\) & 2.048384 & 0.892124 & [41] \\ \hline \end{tabular}
\end{table} 
Table 10: Parameters of the \(NPP_{WBI}\) fit with Equation (10). \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline  & **NEE** & _GPP_ & _RECO_ & _Rg_ & _T_\({}_{a}\) & _T_\({}_{s}\) & \(P\) & _SWC_ & \(\Delta\)_SWC_May-Sep_ & _SGS_ & _EGS_ \\ \hline _NEE_ & 1 & & & & & & & & & \\ _GPP_ & \(-0.578\) & 1 & & & & & & & & \\ _R\({}_{ECO}\)_ & \(0.327\) & \(0.582\) & 1 & & & & & & & \\ _R\({}_{g}\)_ & \(0.107\) & \(-0.184\) & \(-0.107\) & 1 & & & & & & \\ _T\({}_{a}\)_ & \(0.209\) & \(-0.213\) & \(-0.038\) & \(0.105\) & 1 & & & & & \\ _T\({}_{s}\)_ & \(-0.030\) & \(-0.108\) & \(-0.155\) & \(-0.176\) & \(0.902\) * & 1 & & & & \\ \(P\) & \(0.255\) & \(-0.004\) & \(0.250\) & \(-0.644\) * & \(0.328\) & \(0.398\) & 1 & & & \\ _SWC_ & \(0.421\) & \(-0.118\) & \(0.281\) & \(-0.778\) * & \(0.191\) & \(0.272\) & \(0.776\) * & 1 & & \\ _ASWC_May-Sep_ & \(-0.707\) * & \(0.403\) & \(-0.240\) & \(0.089\) & \(-0.219\) & \(-0.096\) & \(-0.645\) * & \(-0.422\) & 1 & \\ _SGS_ & \(-0.021\) & \(0.221\) & \(0.238\) & \(-0.075\) & \(0.054\) & \(0.019\) & \(0.160\) & \(0.025\) & \(-0.228\) & 1 \\ _EGS_ & \(-0.165\) & \(-0.189\) & \(-0.382\) & \(0.672\) * & \(-0.458\) & \(-0.550\) & \(-0.749\) * & \(-0.834\) * & \(0.247\) & 0.131 & 1 \\ \hline \end{tabular}
\end{table} 
Table 6: Correlation coefficients for the main annual fluxes (_NEE_, _GPP_, _RECO_) and the mean annual values of the main driver variables (_Rg_—global radiation, _T\({}_{a}\)_—air temperature, _T\({}_{s}\)_—soil temperature, _P_—precipitation, _SWC_—soil water content, \(\Delta\)_SWC_May-Sep_—difference of the mean monthly _SWC_ in May and _SWC_ in September, _SGS_ and _EGS_—day of year for the start and the end of the growing season, respectively). Significant (_p_ < \(0.05\)) coefficients are marked with asterisk.