## Materials and Methods
### LandscapeDNDC Model Framework  
LandscapeDNDC is a model framework that assembles process-oriented biogeochemical models of C-, N- and water cycling in grassland, arable and forest ecosystems applied at site and regional scales [10]. Each site is considered as a one-dimensional, vertical grid cell, structured into above- and belowground layers as found appropriate to represent a specific site heterogeneity. A vegetation type or cohort is represented by the distribution of foliage and fine root biomass within these layers [32]. The framework is composed of modules that calculate microclimate variability, physiological processes, decomposition processes, water fluxes and dimensional vegetation changes. Modules of different purposes are combined and modules for the same task can be exchanged according to land use and process detail required. The modules used were canopy ECM for microclimate [14], Treedyn (modified) for tree structural changes [33] and either PnET [12; 34] or PSIM [15; 35; 36] for vegetation physiology. The remaining modules for water cycle and soil biogeochemistry were derived from the original DNDC model and have also been evaluated at various sites [16; 37]. This study used LandscapeDNDC version 0.36.1 (win64). We focus on the two physiology-oriented modules PnET and PSIM to estimate ecosystem fluxes and soil gas emissions. PnET calculates C uptake, or gross primary production (GPP), from a direct dependence of the maximum photosynthetic rate (AMAXB) on leaf N concentration [38; 39], and determines respiration first from actual photosynthesis and second from the temperature-related activity of roots, wood and foliage (BASEFOLRESPFRAC, ROOTM-RESFRAC). In contrast, PSIM uses the [PERSON] model [40; 41] for photosynthesis with the potential carboxylation rate at 25 \({}^{\circ}\)C as a key parameter (VC-MAX25), and the Thornley and Cannell [42] approach, which determines respiration rate from temperature and N-content, controlled by a Michaelis-Menten coefficient (KM20). Therefore, PSIM allows for a direct optimisation of stomatal conductance, while in PnET transpiration is empirically related to photosynthesis using a fixed water use efficiency. With PSIM, it is also possible to simulate more than one vegetation cohort [32], thus considering the impact of competition based on cohort-specific spatially and temporally distributed tissue. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Horizon**} & \multirow{2}{*}{\ \begin{tabular}{c} **Thickness** \\ **mm** \\ \end{tabular}  } & \multirow{2}{*}{**Organic C**} & \multirow{2}{*}{**Organic N**} & \multirow{2}{*}{**pH**} & **Bulk Density** & **Field Capacity** & **Wilting Point** & \multirow{2}{*}{ \begin{tabular}{c} **Clay** \\ **Paint** \\ **Fraction** \\ \end{tabular}  } \\  & & & & **g cm\({}^{-3}\)** & **mm m\({}^{-3}\)** & **mm m\({}^{-3}\)** & \\ \hline O & 20 & 0.2162 & 0.0114 & 5.1 & 0.0670 & - & - & 0.035 \\ A & 80 & 0.0560 & 0.0038 & 5.4 & 0.7043 & 530 & 240 & 0.520 \\ E & 80 & 0.0287 & 0.0023 & 5.2 & 0.9682 & 530 & 240 & 0.516 \\ B & 200 & 0.0159 & 0.0010 & 5.4 & 1.1334 & 480 & 240 & 0.510 \\ BC & 380 & 0.0108 & 0.0003 & 6.2 & 0.9350 & 530 & 240 & 0.601 \\ C & 260 & 0.0146 & 0.0005 &Since the canopy is very heterogeneous, making it impossible to derive all necessary parameters on a species basis from the literature, we parameterised the upper canopy assuming basic properties of oaks (_Quercus robur_). However, parameters that could be directly or indirectly derived from measurements at the site were specifically defined (Table 3). These were in particular the measured peak foliage biomass and foliage development. Additionally, gas exchange measurements from about half of the investigation period (for EC measurements, the period between 1999 and 2007; for the chambers that were installed in 2008, the period until 2012) were used to adjust selected photosynthesis and respiration parameters, using an incrementally best fit method.