## 3 Results and Discussion
Finally, the partitioning of the NEE into GPP and R\({}_{e}\) is another source of uncertainty which might have a larger impact on the accuracy of the seasonal dynamics than on the annual values, since it is usually modelled for a small temporal window (e.g., 10-day) [36,37], which might provide more accurate results for one part of the year and less for others. In addition, partitioning of the NEE is more complex in non-forested ecosystems located in dry areas where growth is limited by water [45]. Figure 4: _Cont._  
Figure 4: Temporal trace of the eight-day observed EC-derived GPP and the percentage difference between the observed and the GPP predicted by the MODIS product (MOD17 values and estimations by Model 3 for different sites: **(a)** USAUD (2004–2005), **(b)** USFPe (2004–2005), **(c)** USVAR (2005–2006), **(d)** USKS2 (2005–2006), **(e)** USSSO4 (2004–2005), **(f)** USME3 (2004, 2005). GPP measured as kg C\(\cdot\)m\({}^{-2}\cdot\)day\({}^{-1}\). Horizontal axis represents eight-day periods. The characteristics of Model 3 for each site are shown in Table 4.  
Conditions/Variables Which May Affect the Suitability of the MODIS GPP Product for Estimating C Temporal Dynamics in Shrublands and Grasslands in Water Deficient Areas  
The temporal dynamics of the C fluxes at eight-day intervals were improved for most sites by adding accurate information regarding soil temperature, radiation and/or water availability to the GPPm data, as Model 3 showed (Table 4, Figure 4a-f). Their inclusion in the models agrees with the fact that radiation, temperature and water have been reported by several authors as the most important factors influencing ecosystem productivity and respiration (e.g., [9, 46]). The use of global radiation and soil temperature in the model for the non-water-limited shrubland lowered the GPP overestimation for the season with very high productivity and realistically captured the onset of the season (Figure 4e). The absence of water availability in the model suggests that in this site GPP is not constrained by the amount of water available, since the critical minimum value is never reached, as found by [47] in grasslands in humid areas. The fact that soil temperature was better related to GPP than air temperature in this site agrees with the findings of [8] for a forested site, and it might have been related to a more developed radical system in comparison to a grassland area (where air \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Tower**} & \multicolumn{3}{c}{**Model 1**} & \multicolumn{3}{c}{**Model 2**} & \multicolumn{3}{c}{**Model 3**} \\  & **Year** & \(\mathbf{r^{2}/\#\rho^{2}}\) & **SEE** & \(\mathbf{r^{2}/\#\rho^{2}}\) (**n**) & **SEE** & \(\mathbf{r^{2}/\#\rho^{2}}\) & **SEE** & **Var** \\ \hline \multirow{3}{*}{USAUD} & 2003 & 0.58 \({}^{\#}\) & - & & & & \\  & 2004 & 0.29 \({}^{\#}\) & - & (96) & - & 0.18 \({}^{\#}\) & & Ts \\  & 2005 & 0.18 \({}^{\#}\) & - & & & & & \\ \hline \multirow{3}{*}{USFPe} & 2004 & 0.32 & 0.73 & & & & & \\  & 2005 & 0.50 & 0.81 & (62) & 0.89 & **0.52** & 0.83 & GPPm, SWC \\  & 2006 & 0.57 & 0.49 & & & & & \\ \hline \multirow{3}{*}{USVAR} & 2004 & 0.02 \({}^{\#}\) & - & & & & & \\  & 2005 & 0.37 \({}^{\#}\) & - & (136) & - & **0.60**\({}^{\#}\) & & SWC \\  & 2006 & 0.14 \({}^{\#}\) & - & & & & & \\ \hline \multirow{3}{*}{USKS2} & 2005 & 0.54 & 0.89 & 0.49 & & & & \\  & 2006 & 0.51 & 0.81 & (82) & 0.88 & **0.65** & 0.75 & Ts, Rg \\ \hline \multirow{3}{*}{USSO4} & 2004 & 0.39 & 0.38 & & & & & \\  & 2005 & 0.46 & 0.57 & 0.46 & & & & \\  & 2006 & 0.36 & 0.39 & & & & & \\ \hline \multirow{3}{*}{USME3} & 2004 & 0.78 & 0.85 & **0.69** & & & & \\  & 2005 & 0.65 & 0.84 & (89) & 0.92 & **0.75** & 0.83 & GPPm, Ta \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Relationship between MODIS estimated eight-day GPP (GPPm) with EC-derived 8-day GPP. Model 1 was adjusted per tower and per year. Model 2 was adjusted per tower.