## 3 Results and Discussion
### Ecosystem Water Use Efficiency Model Development  
The Pearson correlation coefficient between variables for the complete dataset (Table 3) has been used to understand the correlation between NDVI, eWUE and meteorological variables (VPD, P, ET\({}_{o}\) and Tair). Subsequently, linear models were fitted and assessed using the AIC to gauge their quality in terms of fit and complexity with the calibration dataset. The findings using 75% of the dataset revealed that the model featuring the combination of variables NDVI, ET\({}_{o}\), and Tair (Equation (1)) displayed the lowest AIC value (\(-2.4\)), making it the model striking the optimal balance between data explanation capacity and model simplicity. Furthermore, determination coefficients (R\({}^{2}\) and R\({}^{2}\) adj) were calculated for each model, with the selected model reaching an R\({}^{2}\) of 0.88 and R\({}^{2}\) adj of 0.86 (Table 4). The coefficients estimated for each variable in the chosen model provide insights into the relationship and magnitude of influence of each predictor on the eWUE variable. It is worth highlighting that, due to the limited sample size, these outcomes should be interpreted with caution and may require further validation with larger datasets to extract firm conclusions. \[\text{eWUE}=2.709+2.202\cdot\text{NDVI}+0.005\cdot\text{ETo}-0.134\text{-Tair} \tag{1}\]  
Figure 8: Mean daily values of GPP (green), NEE (orange) and eWUE (blue) across the six-year of experiments. Values depicted are mean values \(\pm\) standard deviation obtained after filtering and gap filling. The AIC is a criterion that aims to strike a balance between the model's fit to the data and its complexity. In other words, the AIC penalizes more complex models to avoid overfitting. A model that fits the data well but is overly complex might have a higher AIC than a simpler model with a slightly worse fit. The absolute value of AIC alone is not very informative; its usefulness lies in comparing AIC between different models. On the other hand, R\({}^{2}\) measures the proportion of variability in the dependent variable that is explained by the model. However, R\({}^{2}\) does not penalize the complexity of the model, meaning that a more complex model could have a higher R\({}^{2}\) simply because it is \"overfitting\" the data. As mentioned above, the remaining 25% of the dataset was reserved for a preliminary validation of this approach. Figure 9 shows the linear regression between WUE values modeled through Equation (1) and estimated WUE values from the EC tower observations. A regression coefficient of 0.83 and an RMSE value of 0.38 g C kg\({}^{-1}\) H\({}_{2}\)O were obtained. The results indicate an overestimation of eWUE; however, it is important to note that the values are monthly, and the variables considered exhibit a common variability within each month. It is worth noting that these validation results are representative for the location and specific conditions of the present study. In the absence of climate and human-induced changes, the interannual factors influencing eWUE remain constant [114]. However, several studies have observed that eWUE exhibits interannual and spatial variability due to various environmental conditions [46; 115]. It is additionally important to consider climate change projections, as they can also have an impact on the eWUE parametrization [34].