## 2 Methodology
### Parametrisation and Models' Implementation
Hence, the \(T_{f2}\) reduces \(\alpha\) exponentially when temperatures decrease from the optima to 0 or increase from the optima until 45 \({}^{\circ}\)C, and in the same way as the \(T_{f1}\), \(T_{f2}\) calculation is split (Equation (3)). In both cases, the optimum temperature range is between \(T_{opt}-x_{2}\) and \(T_{opt}+x_{1}\), where no penalization of the light-use efficiency or GPP reduction is applied. Lastly, the parameters \(x_{1}\) and \(x_{2}\) are calculated for each plot, choosing the temperatures in which the model performs the best, ranging from \(\pm\)6.5 \({}^{\circ}\)C to \(\pm\)11.5 \({}^{\circ}\)C.  
\[\begin{split} GPP_{Tf2}=\frac{GP_{max}+PAR-T_{f2}}{GP_{max}+x+PAR -T_{f2}}\left\{\begin{array}{l}T_{f2}>(T_{opt}+x_{1}),\;T_{f2  
The non-rectangular hyperbola proposed by [PERSON] et al. (2018) and the exponential model of [PERSON] (2018) are also tested (Equations (4) and (5), respectively) to compare the performance of the rectangular hyperbolic models with some alternatives.  
\[GPP_{NRH}=\alpha\cdot PAR\cdot GP_{max}-\sqrt{(\alpha\cdot PAR\cdot GP_{max})^{ 2}\cdot-4\cdot\alpha\cdot PAR\cdot GP_{max}\cdot\theta} \tag{4}\]  
\[GPP_{EXP}=GP_{max}\cdot\left[1-exp\;exp\;\left(\frac{-\alpha\cdot PAR}{GP_{ max}}\right)\;\right] \tag{5}\]  
For the non-rectangular hyperbolic and exponential models, \(GP_{max}\), \(\alpha\) and \(PAR\) are defined as in the previous equations, and \(\theta\) determines the convexity of the curve for the non-rectangular hyperbola with the value ranging between 0-1. Finally, a WTD-based GPP model developed by [PERSON] et al. (2018) is included in the set of models compared (Equation (6)), where \(uP\) is the optimum _WTD_, \(tP\) the tolerated range of the oscillation of _WTD_ from the optimum (\(uP\)), and \(GP_{max}\) also considers a _WTD_ optimal for photosynthetic activity. In this case, the model is applied two times, using the measurement of _WTD_ in 30 min time steps and the last 15 days' _WTD_ average values. In both cases, the same equation is applied (Equation (6)). \[GPP_{WTD}=GP_{max}\cdot\frac{PAR}{\frac{GP_{max}}{\alpha}+PAR}\cdot exp\;exp\; \left(-0.5\cdot\frac{(WTD-uP)^{2}}{tP}\right) \tag{6}\]  
Before the parametrisation of the final fixed variables for the WTD-based model, a Gaussian relationship between GPP and _WTD_ oscillations from the optimum (\(uP\)) and tolerated positions (\(tP\)) is assumed, applying Equation (7) to obtain the fixed parameters of optimum and tolerated _WTD_ as [PERSON] et al. (2018) developed and based on the _Sphagnum_ spp. response to _WTD_ changes. \[GPP_{WTD}=GP_{max}\cdot exp\;exp\;\left(-0.5\cdot\frac{(WTD-uP)^{2}}{tP}\right) \tag{7}\]  
The models are used with the chamber flux measurements of GPP (NEE-Reco) for parametrising the campaign's fixed variables: \(GP_{max}\), \(\alpha\), and \(\theta\). The sole exception occurs with the _WTD_-based model where a previous step is required (Figure 3, Equation (7)), as earlier described. A general workflow is followed, and the best fit for every single model is selected for the two campaign moving windows (Figure 4). Figure 3: Optimum and tolerated water table depth (WTD) calculation scheme. The general workflow is the reference for Figure 4 scheme, and WTD parameters are secondary variables introduced at the start. The resulting parameters are interpreted as the output for the median date between the campaigns analyzed for every two campaign moving windows when the number of measurements in a campaign is lower than eight. Once all the campaigns are processed, the parameters are compiled and interpolated daily to model the inter-campaign GPP values using these parameters, tower-based _PAR_, and air temperature or _WTD_, if applied. Subsequently, as the final output, the whole time-series GPP with 30 min time steps, daily cumulative values, and yearly cumulative values are generated together with all statistical results from each model.