## 2 Methodology
### Models' Statistical Evaluation  
The GPP data were analyzed using the Lilliefors test to determine their normality. Once the normality and no homoscedasticity are assured (Supplementary Tables S1 and S2), the goodness of fit to select the best performance of each model is evaluated with the \(p\)-value, adjusted R\({}^{2}\), and root mean square error (RMSE). For each parameter, the results in which their values are outside the bounds are discarded. For instance, in case of the convexity parameter for [PERSON] et al. (2010) fit, the parametrisation cannot trespass below zero or above one. Moreover, all parameters should present \(p\)-values < 0.05, if possible. The \(p\)-value filter is modified to select the most significant results (not below 0.05) only when no possible combination retrieves all parameters with significance levels below 0.1. Lastly, the fit with the lowest RMSE, highest adjusted R\({}^{2}\), and the most significant result based on the \(p\)-value is selected.  
In order to compare the performance of the models, RMSE, \(p\)-value, Akaike information criterion (AIC), and the correlation (R\({}^{2}\)) from the best fit are considered. The AIC indicates the amount of information that the model has lost concerning the original dataset and the efficiency of the model. In large datasets when an extra predictor is not efficient, the input would be avoided and the calculations would be faster, this is the reason why AIC  
Figure 4: Algorithm workflow to choose the best fit for each model and the best model out of them. Net ecosystem exchange (NEE) and ecosystem respiration (Reco) fluxes are the fluxes calculated from manual chamber campaigns following the [PERSON] et al. approach [PERSON] et al. (2010).  
presents the highest weight. Simultaneously, RMSE evaluates the differences between the modelled and measured values, the R\({}^{2}\) measures the relationship, and the \(p\)-value evaluates the significance of this relationship and, consequently, the fit. High accuracies can mask low probabilities to estimate the predicted value, and the combination of statistics allow us to discard these cases. The best performance for each combined two campaigns is selected based on them, with the highest weight of AIC (indicating the likelihood and efficiency of the model), followed by \(p\)-value and RMSE (revealing the significance and error of the estimations), and finally, R\({}^{2}\) is considered. The R\({}^{2}\) has the lowest weight since, for a good correlation, just a similar trend between variables is needed; thus, R\({}^{2}\) is not always an indicator of a good or the best fit.  
The selection of the best performing model involves creating a multi-model output and identifying the best performance separately. Within each two-campaigns-moving-windows, the models are ranked based on the AIC, \(p\)-value, RMSE, and R\({}^{2}\) outputs rescaled from 0 to 1 and multiplied by the weights presented in Table 1. The model with the lowest weighted statistics output is then chosen. Once the computations are completed for all the campaign values, the parameters obtained from the models are interpolated daily using the best ones. The strategy is applied for all models as the predicted parameters are equivalent. Lastly, a moving average (span = 12) is applied to the model's last three and the first three days for the multi-model consistency. The output with the smoothed sections represents the final thirty-minute multi-model result, from which daily and yearly cumulative values are computed.