## 3 Results and Discussion
### Calibration  
Table 2 shows the values of the calibrated parameters for the Pampa biome using INLAND model with AMALGAM calibration method. The parameters are associated with their respective hierarchical levels of calibration. The sensitive difference between the default values of the model for this vegetation type and the calibrated values can be clearly observed. This fact further highlights the importance of calibration, since, although the model individualizes the representation of different vegetation types, local environmental conditions significantly alter their input parameters, causing errors in the simulations. The first calibration level calibrates parameters that influence the determination of the surface net radiation. The daily average of solar radiation reflected by the surface presents good agreement with the observed data after calibration, the RMSE and PBIAS decrease significantly (Figure 4 and Table 3). The seasonality and the daily cycle of the \(Rr\) are well represented. The model satisfactorily reproduced the average diurnal cycle of the \(Rn\) (Figure 5), which is of critical importance to the correct representation of latent, sensible, and soil heat flux, as the balance of radiation determines the amount of energy available for such fluxes. In addition, the coefficient of determination close to one (Table 3) for the calibrated and observed daily data reinforces that calibration significantly improved the simulation of this variable. Figure 3: Cumulative monthly rainfall (mm) and average monthly temperature (\({}^{\circ}\)C) observed at the experimental site (2014 to 2018) and at the Conventional Weather Station (CWS) of National Institute of Meteorology (INMET) from 1961 to 2013. Analyzing monthly means of surface albedo (ratio between the solar radiation reflected by the surface and incoming shortwave radiation in the surface), we can observe that using default parameters, the mean albedo were 0.27 for 2014 and 2015, and were 0.28 for 2016 and 2017, while observed data were 0.18 for 2014 and 2017, and were 0.17 for 2015 and 2016. That is an approximate difference of 0.1 for the entire period. After calibration the simulated value was 0.18 (Figure 6) for the four years. It is important to note that there is almost no seasonal variation of this variable. Figure 4: Daily average of the solar radiation reflected by the surface observed (\(Rr.obs\)), simulated by the model before calibration (\(Rr.def\)) and simulated after the calibration (\(Rr.clb\)). Figure 5: (a) Average diurnal cycle of solar radiation reflected by the surface observed (\(Rr.obs\)), simulated by the model before calibration (\(Rr.def\)), and simulated after the calibration (\(Rr.clb\)); (b) same for \(Rn\). The second calibration level corresponds to turbulent fluxes. The calibrated model improved the \(LE\) representation (Figure 7). This improvement is evident for the months of January to March and of October to December, periods in which \(LE\) values are higher than those for the rest of the year. The seasonality of \(LE\) accompanies the seasonality of the \(Rn\), showing that this component strongly controls evaporation and transpiration processes of the Pampa biome. The average diurnal cycle and scatter plot of \(LE\) for 2014 to 2017 shown in Figure 8 shows that the calibrated simulation makes significant improvements relative to the default simulation, especially for