## 3 Results
### Models' General Performance  
In general, the models have shown reliable values; however, when using the WTD-based approach [22] with short-term measurements (30 min time steps), some unusual drops of GPP values were observed, e.g., during June and July 2021 for CL_2 and CR_2 plots. The same model applied with the fifteen-day average WTD provided more continuous and reliable results along the time series, although some unusual drops remain (WTD_15). The reason behind this can be the response time from vegetation; the effect of WTD is not immediate, and a continuous rise or drop is needed to modify vegetation productivity without a limited effect [26]. Thus, thirty-minute WTD measurements cannot correctly predict the effect of WTD on GPP and the optimal WTD estimated during a campaign is not necessarily reliable for the rest of that period if the oscillations are significant. The exponential model from [PERSON] and [PERSON] [21], the non-rectangular hyperbolic function proposed by [PERSON] et al. [20], together with the Michaelis-Menten rectangular hyperbolic model with and without the addition of the created temperature factors, have displayed a consistent trend of outputs through the time series as it can be observed in Figure 8 for the year 2021. In the case of the WTD-based approach [22], some punctual periods during low WTD levels resulted in drops in the GPP trend not observed for the  
Figure 7: Percentage of 2020/21 campaigns in which including one of the temperature factors benefited the statistical results or resulted in an equally precise output. In the case of BIAS, RMSE, \(p\)-value, and AIC, a benefit is considered when the addition of temperature factor decreases these values and, in the case of R\({}^{2}\), when it increases them. Statistics are shown for each plot of the CL (1–3) and CR (1–3) sites. rest of the models (e.g., July-August CL_3, October CR_3). For this reason, during those periods, this model was not considered for the multi-model choice. As expected, a Gaussian distribution of GPP values with a peak between June and July marks the middle of the vegetation season. Each plot presents a different maximum and length of the GPP peak. Unquestionably, CL and CR sites report considerable differences in the daily cumulative GPP due to the differences in vegetation coverage and WTD regimes (Figure 8). These differences affect the performance of the models and the final output accuracy. The biggest residuals, or differences between the models and the measured data, happened in the June and July campaigns for the CL and CR sites. Although considering the average GPP measured during the winter campaign in February (Table 2), the values remain below \(\pm\)0.2 times the average GPP (Figure 9). Hence, considering the relative difference between modelled and measured data, the winter measurements, including February and March or October campaigns, present the highest errors. It is also important to mention how, in CL sites, generally, all models overestimate the measured values (negative relative BIAS), with particular attention to WTD_15, which has the highest general overestimation. Additionally, half of them underestimated the measurements during the first campaigns of the year (WTD, NRH, and EXP--18 April 2021, and RH, NRH, and EXP--14 March 2021, Figure 9). In the case of CR sites, the patterns are not that clear, though generally, the measured GPP values are overestimated except in the exponential model. The exponential model shows the underestimation of GPP during the first half of the measurement season and their overestimation during the next half. In this case, the first campaigns of the year are only underestimated by the NRH and EXP models in CR_1 and CR_2 and only by the EXP model in CR_3. Overall, modelled values in CR and CL sites show errors below \(\pm\)10% (\(\pm\)0.1, Figure 9).