## 3 Results  
### Xeric Strubland  
Figure 3a shows the temporal dynamics of the GPP in this ecosystem, obtained from the eddy covariance system and from the LUE models for the period from July 2019 to December 2022 at the daily scale. Cyclical behavior can be observed, with the maximum values occurring at the end of spring and the minimum at the beginning of fall, which is well reflected in the LUE models, although both show an advance in the maximum values. Regarding the interannual behavior, there is a general increase in GPP, ranging from a maximum of about 5 gC/m\({}^{2}\)days\({}^{-1}\) in 2020 to 7 gC/m\({}^{2}\)days\({}^{-1}\) in 2022.  
The values given by the LUE model using meteorological data from ERA5-Land (hereafter ME) showed a similar but noisier trend, while the values given by the LUE model using a water availability index (hereafter WA) showed a general underestimation in the first two years studied (2019-2021), but a better fit in 2022.  
Figure 3: (**a**) Temporal dynamics of the GPP for xeric shrublands from July 2019 to December 2022 at daily scale. Black line: GPP from eddy covariance data (EC) system, red line: GPP estimated with the LUE model using meteorological data from ERA5-Land (ME), blue line: GPP estimated with the LUE model using a water availability index (WA). Validation presented in 1:1 graphs of the data EC vs. LUE models in xeric shrublands in the study period: (**b**) ME; (**c**) WA.  
These trends are evident in Figure 3b,c, which show the 1:1 plots of both models with respect to the EC data throughout the study period, together with R\({}^{2}\), RMSE, NRMSE and MBE. ME showed more scattered values resulting in R\({}^{2}\) of 0.31, RMSE of 1.01 gC/m\({}^{2}\) and NRMSE of 0.13. On the other hand, the WA gave a better fit (R\({}^{2}\) = 0.51) and lower error (RMSE = 0.43 gC/m\({}^{2}\) and NRMSE = 0.07) despite the underestimation mentioned above. This underestimation of the WA resulted in a higher bias, with relatively high MBE values (\(-0.68\) gC/m\({}^{2}\)) compared to those of ME (\(-0.16\) gC/m\({}^{2}\)).  
When the results were aggregated on a monthly basis (Table 3), there was an improvement in both LUE models, showing better R\({}^{2}\) and RMSE, especially in WA, where the RMSE drops to only 0.21 gC/m\({}^{2}\) and the R\({}^{2}\) reaches 0.74, while the MBE and NRMSE remain constant.  
Finally, using the LUE model that gave the best results, WA, the annual GPP accumulated by hydrological year in the xeric shrubland zone of the DBR, is presented in Figure 4. Several differences can be observed, from the firebreak area (redder values) without assimilation to the denser shrub areas (blue). It should be noted that although the maximum values in GPP increase, especially in 2021/2022, in the EC validation zone, at the spatial level it does not show this trend.  
Figure 4: Hydrological year maps of accumulated GPP in xeric shrublands using the LUE-WA model.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**LUE Model** & **Metric** & **Value** \\ \hline ME & R\({}^{2}\) & 0.36 \\  & RMSE & 0.73 \\  & NRMSE & 0.19 \\  & MBE & \(-0.19\) \\ \hline WA & R\({}^{2}\) & 0.74 \\  & RMSE & 0.21 \\  & NRMSE & 0.06 \\  & MBE & \(-0.55\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results of the metrics in xeric shrubland using monthly aggregates for ME and WA. RMSE and MBE given in gC/m\({}^{2}\).