## 3 Results and Discussion
The reason for these underestimations might be related to the fact that 2005 was categorized as \"extremely moist\" and \"moderately moist\" between April and December for the areas where those sites were located [44]. For USAUD and USVAR, the underestimation happened in both cases when there was more precipitation in comparison to the other two years. For the remaining years, GPP was overestimated in those two sites. In addition, the overestimation was larger in the site located in the dry area (Bsh) than for the site located in the temperate area (Csa). An inverse relationship was found between the magnitude of the overestimation of the GPP and the precipitation for these two sites (Table 2). The underestimation of annual GPP in USFPe in 2004 can be explained by the fact that, although there was not more annual precipitation than in the other years, most of it fell during the summer months (data not shown) and therefore the local VDP was not underestimated. These results agree with the previous explanations about the effect of VDP estimations in the MOD17 algorithm and corroborate the hypothesis that GPPm is overestimated in non-forested biomes where soil water is severely limiting during the growing season. Though water content or water potential of topsoil horizons are the most desirable predictors in GPP modelling [9], such data is hard to summarise on a yearly basis and it is also not available for all the towers, making precipitation a more adequate variable to be considered. The existence of a strong correlation between annual GPP (GPPec and GPPm) and precipitation in water-limited grasslands and in shrublands (Table 3 and Figure 3a,b) agrees with the results obtained by [9]. It shows the ability of GPPec and GPPm to capture the relationship between photosynthetic activity and one limiting factor like precipitation, and it makes possible the use of this variable to improve the estimation of GPPec in non-forested biomes on an annual basis. The general predictive model which included the forested site  
Figure 2: Relationship between EC-derived annual GPP with MODIS annual estimated GPP. The dashed line is the linear best fit for dataset 1 (n = 12) and the solid line is the 1:1 reference. was less accurate than the model developed for non-forested sites. Thus, we recommend the use of specific models based on data from those biomes and to use meteorological data at a finer spatial resolution than interpolated DAO data. Suitability of the MODIS GPP Product to Estimate Temporal Dynamics of the Carbon Fluxes at Eight-Day Intervals. The MODIS GPP algorithm is considered to be able to capture seasonal dynamics in photosynthetic production [13]. However, the eight-day interval plots showed that the temporal dynamics of C in the non-forested water-limited sites were not as well captured by MOD17 (Figure 4a-c) as for the shrubland located in a non-water deficient area (Figure 4d). The delay in capturing the end of the  
Figure 3: Relationship between precipitation and **(a)** EC-derived annual GPP, **(b)** MODIS estimated annual GPP. The dashed line is the linear best fit for dataset 1 (n = 12). \begin{table}
\begin{tabular}{c c c c c} \hline
**Variable Y** & **Variable X** & **Dataset** & **r\({}^{2}\)** & **SEE** \\ \hline \multirow{3}{*}{GPPec} & & 1 & 0.77 & 149.26 \\  & & 2 & 0.94 & 143.36 \\  & & 3 & 0.93 & 142.57 \\ \hline \multirow{3}{*}{GPPec} & & 1 & 0.68 & 175.11 \\  & & 2 & 0.83 & 233.86 \\  & & 3 & 0.75 & 267.41 \\ \hline \multirow{3}{*}{GPPm} & & 1 & 0.71 & 159.51 \\  & & 2 & 0.83 & 249.28 \\ \cline{1-1}  & & 3 & 0.77 & 272.02 \\ \hline \end{tabular}
\end{table} 
Table 3: Relationship between EC-derived annual GPP (GPPec) (g C\(\cdot\)m\({}^{-2}\cdot\)year\({}^{-1}\)), MODIS annual estimated GPP (GPPm) (g C\(\cdot\)m\({}^{-2}\cdot\)year\({}^{-1}\)) and precipitation (Precip) (mm).