## 4 Discussion
### Variations in Yearly Cumulative Values and Determination of the Growing Season  
The comparison between models resulted in differences in the yearly cumulative value and the shape of GPP distribution, leading to oscillations in the growing season estimations. These oscillations in the yearly cumulative values have been observed in other studies, where the NRH model is frequently reported to overestimate GPP (2015). The RH model is often reported to underestimate or overestimate actual GPP values depending on the temperature and season ([PERSON], 2015; [PERSON], 2015), potentiality leading to compensation effects in GPP flux rates when annual cumulative GPP is calculated. However, in our case, there is a general overestimation of GPP from RH when compared to the multi-model results. The models with temperature factors (TRH and TRH_exp) improve the RH when the temperature plays a significant role. However, when used for the overall year, the TRH model presents a slight underestimation and TRH_exp overestimation compared to the multi-model. Ultimately, both models are RH-based, and as the results indicate, the RH fit does not always produce the best result. Adding temperature factors adjusts the fit by either reducing or augmenting the curve without altering its shape. The WTD-based model mainly presents a higher cumulative value than the multi-model, while it is the contrary for the exponential model. Hence, we see how the oscillations of the yearly cumulative GPP values are highly dependent on the fit of the light response curve.  
In addition to comparing cumulative GPP values--a widely used approach for model evaluation--we also analyzed the differences between models based on phenology oscillations. The interpretation of the growing season changes depending on the focus of the study ([PERSON], 2015). The thermal growing season approach defines \"the period during which plants could potentially grow based on meteorological criteria (meteorological season)\" ([PERSON], 2015), while with GPP, the goal is to define the actual period of vegetation growth based on the carbon uptake ([PERSON], 2015). Despite the differences, the period in which the plants can potentially grow based on temperature should be the actual period of growth if there is no other limitation. In \"normal\" waterlogged conditions (no drainage, drying, etc.), peatland areas do not suffer water availability limitations [39]. Therefore, in the study area, it was assumed that there is an equivalency between these types of growing seasons. The approach could not be applied to other environments with vegetation growth limitations where the temperature and GPP growing season would not be comparable. Each GPP model presented a different annual value and distribution shape, finally producing variances in the length and amplitude of the growing seasons. These oscillations prove the importance of the right choice of strategy and how a multi-model approach can accomplish results that are more similar to reality; that is why the multi-model GPP growing season is more similar to the results obtained from the thermal growing season in CL. However, CR presents a growing season that is more similar to the WTD-based approach. The reasons for the higher similarity of the determined length of the growing season are unknown. We can only hypothesize that the higher presence of mosses and the surface oscillations in the floating mat area following WTD change, maybe, are creating more convenient set of conditions for the WTD-based model to work in CR. Hence, due to the penalization applied in the AIC to the use of more predictor variables, the WTD model is not selected on enough occasions in CR. The AIC evaluates the efficiency of the model, balancing the best results and the number of predictors to have those results. The WTD has additional parameters for tolerated and optimal WTD, hence, if another model has close results that are good enough without these extra two parameters, it is ranked as better.