## 2 Materials and Methods
### Description of SITE Model and Site Specific Biophysical Parameters
In the sequential calibration procedure, we varied parameters in each step individually while all other parameters remained unchanged, as previously carried out by [55; 56]. After exhaustive calibration tests of the SITE model for the Caatinga vegetation, we defined the confidence interval of the calibrated parameters, which is crucial to obtain reliable energy and carbon fluxes values. Afterwards, we ran 44 simulations varying the most relevant calibration parameters for the wet season and dry season, as follows: initial fraction of moisture in the soil (\(\mathrm{\theta g/\theta d}\)) from 0.05 to 0.36, coefficient of stomatal conductance (m) from 4 to 10, maximum capacity of the Rubisco enzyme (Vmax) from 40 to 120 \(\mathrm{\mu mol}\) CO\({}_{2}\) m\({}^{-2}\) s\({}^{-1}\), typical dimension of leaves (d\({}_{\mathrm{u}}\)) from 0.020 to 0.090, typical dimension of stems (d\({}_{\mathrm{s}}\)) from 0.010 to 0.15, leaf width (w) from 0.01 to 0.15, and specific leaf area (SLA) from 9 to 26 m\({}^{2}\) leaf kg\({}^{-1}\) C. The ranges of parameter values were chosen according to either in situ measurements or previously published results in the Caatinga biome as described in Table 2. Moreover, these parameters were selected not only because of their importance and sensitivity in driving the main components of the energy balance and CO\({}_{2}\) fluxes but also due to the high uncertainty related to their in situ measurements. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Used Value** & **Source** \\ \hline Height of data measurement (z) & 11 m & Measured on site \\ Height of the canopy (z\({}_{1}\)) & 8 m & Measured on site \\ Height of lower canopy (z\({}_{2}\)) & 5 m & Measured on site \\ Zero plane displacement (d) & 7.33 m & Estimated \\ Roughness above the canopy (zh) & 1.35 m & Estimated \\ Total soil porosity (\(\mathrm{\Phi}\)) & 0.41 m\({}^{3}\) m\({}^{-3}\) & [38; 39] \\ Humidity content at field capacity (\(\mathrm{\theta_{CC}}\)) & 0.225 m\({}^{3}\) m\({}^{-3}\) & [38; 39] \\ Moisture content of the permanent wilting point (\(\mathrm{\theta_{PM}}\)) & 0.151 m\({}^{3}\) m\({}^{-3}\) & [38; 39] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Site-specific parameters used in the simulation. The model performance was evaluated and analyzed by means of Taylor diagrams [57] using the Pearson correlation coefficient (\(r\)), standard deviations (SD) between observations (x-axis) and simulated data (y-axis) and root mean square error (RMSE). Thus, SD \(>\) 1 indicates that the model overestimated the variables, and SD \(<\) 1 indicates that it underestimated them. In a Taylor diagram, radial distance represents the ratio of simulated to observed standard deviations, the azimuthal angle represents simulated-observed data correlation, and the distance between observed and simulated data points corresponds to the RMSE. These diagrams are useful because they provide summarized information on the relative performance of an ensemble of simulations. Furthermore, the model performance was assessed through the following statistical measures: median absolute error (MAE) and [PERSON]'s index of agreement (\(d\)). For plotting Taylor diagrams, we used the _taylor.diagram_ function of the R package _plotrix_[58]. The daily means and totals of the fluxes variables were bootstrapped over seasonal intervals for the estimation of random variance (\(\pm\)95% of confidence interval--CI) about the mean according to the methodology presented in the literature [59]. Statistically significant differences (\(p<0.05\)) in the mean seasonal value for a given meteorological variable or CO\({}_{2}\) flux components were determined by the degree of overlap in the 95% bootstrapped CI [60].