## 3 Results and Discussion
Note: Dataset 1: grasslands and shrublands located in water limited sites (n = 12), Dataset 2: all sites located in grasslands/shrublands (n = 14), Dataset 3: all sites located in forested areas and grasslands/shrublands (n = 16), r\({}^{2}\): coefficient of determination, SEE: Standard Error of the Estimate (g C\(\cdot\)m\({}^{-2}\cdot\)year\({}^{-1}\)). All the regressions were significant at the 95% confidence level. growing season might have been due to the incapability of the algorithm to accurately reflect the influence of low soil water content, which led to an overestimation of GPP. In all cases, the results showed that capturing the magnitude of the changes and the peak of the photosynthetic activity in the non-forested sites (regardless of the soil water availability) is still an issue, as [13] and [32] have found. Along the same lines, the largest discrepancies between GPPm and GPPec in the water-limited sites were found during periods when the photosynthetic activity was the lowest, with an overestimation for all cases except for USFPe (2005). This overestimation for sites with low productivity during the fall has been reported previously by [13]. The exception of USFPe was probably related to the unusual precipitation events which happened during those periods [44] as discussed in the previous section. The results of the quantitative analysis of the agreement between GPPec and GPPm at eight-day intervals are showed in Table 4. All the variables in the annual dataset were normal (\(\alpha=0.05\)), except for the sites USAUD (all years) and USVAR (all years), where the GPPec was not normally distributed. Hence, for those sites the square of the Spearman rho coefficient of correlation (\(\rho\)) was included in the tables as a measure of agreement between GPPec and GPPm instead of the r\({}^{2}\) (Table 4). The values estimated by Model 3 were plotted for each tower and year (Figure 4), so that they could be compared to GPPec and GPPm. The highest agreement between GPPec and GPPm per year and tower (Model 1) was obtained for the forested site (USME3) (r\({}^{2}\)= 0.78, \(\mathrm{SEE}=0.85\), n = 45), while in the non-forested sites the agreement was lower than 65%. Analogous results were obtained for Model 2 (r\({}^{2}\)= 0.69, \(\mathrm{SEE}=0.92\), n = 89) and Model 3. Regardless of the temporal water restrictions at the forest site during the summer, the reduced seasonal variation made it easier to model the C cycle using GPPm. At the water-limited non-forested sites, the average agreement between GPPec and GPPm was lower than at the site without water restrictions. The performance of Model 1 also varied the most amongst the years in the water-limited non-forested sites. These differences between years had an impact on the accuracy of the model that took into account tower data over a number of years (Model 2), which were generally less accurate than the model that used tower data for each year separately (Model 1). None of the models for the sites located in grasslands or shrulands were able to explain more than 50% of the variance of the GPPec eight-day data using the GPPm eight-days as input. As for the annual GPP, one reason for the discrepancies between the eight-day GPPec and GPPm might be related to the large scale of the meteorological data used by MOD17 [2,13] and the underestimation of the local VPD in the water deficient areas. The use of more accurate meteorological data (e.g., tower data) can solve this problem in some cases, but in grasslands it can lead to an underestimation of GPPm [13]. Another source of disagreement between the eight-day GPPm and GPPec is that tower based GPP represents a small, unfixed footprint that changes in size and shape as function of wind speed, wind direction, surface roughness, and atmospheric stability [32] and this variability in GPPec has a larger impact when analysing temporal dynamics than when annual GPP is considered, since for the latter these differences can be balanced over the whole year.