## 3 Results
### Multi-Model Results and Performance  
In general, the distribution of GPP followed the same trends for every model; not surprisingly, the multi-model results show a Gaussian distribution with a peak between June and August (Figure 10). The use of multiple statistics to choose the best performance in each inter-campaign period, the exclusion of the WTD_15 model when non-reliable results were provided, and the smoothing during the first and last days of the model have generated a continuous trend where no apparent jump or significant trend change in between models is observed (Figure 10). Hence, it is assumed that the strategy proposed has provided an excellent synergy/combination of the models, capable of showing the seasonality of fluxes without any interference due to the multi-model approach.  
Figure 10: Modelled diurnal rates of gross primary productivity (GPP) for each plot of CL (1–3) and CR (1–3) sites for 2021 determined based on the multi-model approach; GPP is expressed in daily cumulative values (g CO\({}_{2}\) m\({}^{-2}\) d\({}^{-1}\)). Note that the GPP models abreviations stand for EXP—exponential model [21], NRH—non-rectangular hyperbolic model [20], RH—rectangular hyperbolic model, TRH and TRH_exp—linear and exponential temperature adjusted RH, and WTD_15—water table depth based model [22].  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Plots**} & \multicolumn{6}{c}{**Models**} \\ \cline{2-7}  & **RH** & **NRH** & **TRH** & **TRH\_exp** & **EXP** & **WTD\_15** \\ \hline CL\_1 & 1.96 & 2.14 & 1.86 & 1.89 & 1.91 & 3.18 \\ \hline CL\_2 & 1.67 & 1.71 & 1.61 & 1.67 & 1.65 & 2.22 \\ \hline CL\_3 & 1.81 & 1.99 & 1.69 & 1.71 & 1.73 & 3.60 \\ \hline CR\_1 & 1.31 & 1.56 & 1.12 & 1.17 & 1.26 & 3.24 \\ \hline CR\_2 & 1.32 & 1.40 & 1.15 & 1.20 & 1.23 & 2.26 \\ \hline CR\_3 & 1.19 & 1.27 & 1.13 & 1.13 & 1.13 & 2.33 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Values of the average performance per model, estimated via weighted statistics from Table 1, where eight is the weakest and zero is the most robust performance. Note that the GPP models abreviations stand for EXP—exponential model [21], NRH—non-rectangular hyperbolic model [20], RH—rectangular hyperbolic model, TRH and TRH_exp—linear and exponential temperature adjusted RH, and WTD_15—water table depth based model [22].  
Implementing the multi-model to obtain the best performance in each period generated a reliable model with R\({}^{2}\) over 0.85, \(p\)-value below 0.001, BIAS lower than \(\pm\)0.1 \(\upmu\)mol CO\({}_{2}\) m\({}^{-2}\) s\({}^{-1}\), and RMSE below 1.1 \(\upmu\)mol CO\({}_{2}\) m\({}^{-2}\) s\({}^{-1}\) except for CL_1 (Table 4). The highest average errors and lowest mean correlation are found in CL_1; however, for most of the year, CL_1's statistical performance is comparable to the rest of the plots. The drop in its average values is due to the 29 July campaign with RMSE values above five and R\({}^{2}\) values below 0.5 (Supplementary Table S9). In general, the 29 July campaign also shows the weakest performance for all CR plots, with the highest weighted statistic and, hence, with the worst combination of statistics. In the case of CL_2, the weakest performance takes place on 20 April, while for CL_3 occurs on 23 September; however, in both plots, these campaigns are closely followed by 29 July with also a weak performance, following the general trend (Supplementary Table S10). Once again, the performance in CR plots is more robust than in CL plots (Table 4) with higher correlations, lower errors, and more information preserved (AIC), and yet, it has to be considered that the absolute errors are lower also because the vegetation productivity is much lower at this site.