## 3 Results
Only three models (MVPM, TG, and VI) could capture the interannual variations in GPP (\(R^{2}\geq 0.53\)  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Model** & **Model Performance** & \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Temperature Steppe**} & **Alpine Shrub** & **Alpine Swamp** & \multirow{2}{*}{**All Site Years**} \\
**Type** & & & & & & **(N = 90)** & **(N = 90)** \\ \cline{5-8}  & & & **Drought (N = 69)** & **Non-Drought** & **All** & & \\  & & & **(N = 122)** & **(N = 181)** & & & \\ \hline \multirow{10}{*}{LUE} & \multirow{6}{*}{RMSE (\%)} & VPM & 0.54 & 0.61 & 0.59 & 0.91 & 0.86 & 0.64 \\  & & MVPM & 0.50 & 0.75 & 0.68 & 0.91 & 0.81 & 0.80 \\  & & MODIS-GPP & 0.37 & 0.66 & 0.60 & 0.84 & 0.81 & 0.73 \\  & & Average & 0.47 & 0.67 & 0.62 & 0.89 & 0.83 & 0.72 \\  & & VPM & 140.2 & 65.9 & 83.5 & 27.4 & 30.4 & 54.9 \\  & & MVPM & 81.7 & 36.6 & 47.4 & 22.6 & 34.5 & 36.5 \\  & & MODIS-GPP & 88.4 & 41.9 & 52.9 & 31.6 & 37.6 & 42.2 \\  & & Average & 103.4 & 48.2 & 61.3 & 27.2 & 34.2 & 44.5 \\  & & VPM & 108.7 & 37.4 & 54.2 & 16.5 & 6.2 & 16.0 \\  & & MVPM & 53.6 & \(-\)1.5 & 11.5 & 5.7 & 4.3 & 4.2 \\  & & MODIS-GPP & 50.5 & \(-\)2.5 & 10.0 & \(-\)12.2 & 14.2 & 4.2 \\  & & Average & 70.9 & 11.1 & 25.2 & 3.3 & 8.2 & 8.1 \\  & & VPM & 74.2 & 99.0 & 162.3 & \(-\)42.5 & \(-\)49.4 & 138.6 \\  & & MC\({}_{\text{C}}\) & MVPM & 2.0 & \(-\)29.8 & \(-\)40.4 & \(-\)75.1 & \(-\)24.7 & \(-\)153.4 \\  & & MODIS-GPP & 10.5 & \(-\)2.6 & \(-\)3.0 & \(-\)16.8 & \(-\)13.6 & \(-\)52.9 \\  & & TG & 0.47 & 0.66 & 0.61 & 0.89 & 0.84 & 0.78 \\  & & GR & 0.50 & 0.65 & 0.61 & 0.89 & 0.70 & 0.74 \\  & & VI & 0.46 & 0.72 & 0.67 & 0.89 & 0.78 & 0.79 \\  & & AVM & 0.43 & 0.57 & 0.54 & 0.88 & 0.88 & 0.76 \\  & & TCM & 0.2\(p<0.05\), \(N=8\)) (Figure 7a). Overall, the percent biases of the VPM and PCM (\(|\)PBIAS\(|>40.6\%\)) were much larger than those of the other models (\(|\)PBIAS\(|<11.5\%\)). The VPM and PCM overestimated (18.8%-175.6%) and underestimated (28.1%-55.0%) the GPP substantially at the two temperate stepe sites (i.e., NM and XL) during most of the study years. In addition, all the models overestimated the annual GPP by 36.7%-215% in the two drought years (2005 and 2006), except for an underestimation by the PCM in 2006. Notably, the estimates from the MODIS-GPP were as much as 2.1 times that of the GPP\({}_{\text{tower}}\) tower in 2005. As a result, the drought-induced reductions in the GPP were significantly underestimated by all the GPP models. For example, the observed GPP decreased by 73.1% from 2004 to 2005, which was clearly larger than the decrease in the simulated GPP (24.6%-55.3%). Comparatively, the annual GPP estimates from all the models varied slightly and were close to in situ observations (\(|\)PBIAS\(|<26.8\%\)) at the two alpine meadow sites (Figure 7b,c). Notably, the observed GPP in the alpine swamp meadow increased slightly by 6.2%, but the GPP estimated by the VPM, MVPM, GR, and VI models decreased significantly by more than 14.5% in 2004 relative to 2003. Table 4 shows the comparison between the explanation rates of a single variable (to the GPP's fluctuations) derived from linear regressions and the independent explanation rate of each variable, as well as the total derived from stepwise multiple linear regression. Results showed that the combination of multiple variables explained the much higher variability of GPP than any single factor over the temperate steppe and alpine meadow. For example, the total explanation power of EVI, Ta, LSWI, PAR, LST, and VPD was 18.4% higher than that of the best predictor (i.e., EVI) (76% vs. 62%, respectively).