## 3 Results
However, GPP was more closely related to the temperature-related variables (Ta and LST) than the moisture-related indicators (Prec and SWC) in the alpine meadow sites. The opposite relationships were found in the temperate steppe. These results partially prove the dominant role of water stress and temperature stress on GPP in the temperate steppe and alpine meadow, respectively ([PERSON], 2005; [PERSON], 2006; [PERSON], 2006; [PERSON], 2006). Comparison of the Eight Satellite-Based GPP Models in Estimating GPP between the Temperate Steppe and Alpine Meadow  
All of the models performed much better in the alpine shrub meadow (\(R^{2}\geq 0.84\)) and alpine swamp meadow (\(R^{2}\geq 0.70\)) than in the temperate steppe (\(R^{2}\leq 0.68\)) as compared with the site-level observations (Table 3). The mean explanatory power of these eight models reached 89%, 82%, and 61% in the alpine shrub meadow, alpine swamp meadow, and temperate steppe, respectively. Additionally, the RMSEs of the alpine shrub meadow (25.8%) and alpine swamp meadow (35.9%) were clearly smaller than that of the temperate steppe (56.7%). The performance varied greatly among the models in the temperate steppe. The MVPM and VI models with higher \(R^{2}\) and lower AICc were the two better-performing models (\(R^{2}=68\%\) and 67%, respectively, and AICc = -40.4 and -54.7, respectively) of the LUE and statistical models, respectively. The AVM, VPM, and PCM performed relative poorly (\(R^{2}\leq 60\%\) and AICc \(\geq 5.9\)). In contrast, slight intermodel differences were observed in the alpine meadow. The MODIS-GPP and GR performed relatively worse (\(R^{2}\leq 0.84\), RMSE \(\geq 24.5\%\)) than the other LUE and statistical models (\(R^{2}=0.88\)-0.91, RMSE = 22.6%-27.4%), respectively. GPP estimates were close to the EC observations for most models regardless of grassland type (\(|\)IPBIAS\(|<16.5\%\)), except for a significant overestimation by the VPM in the temperate steppe (54.2%) and by the PCM in the alpine swamp meadow (31.0%), and an obvious underestimation by the PCM in the temperate steppe (40.6%). Overall, the performances of the LUE  
Figure 6: Correlations between the observed GPP (GPP_tower) and the climate variables or vegetation indices throughout the year. The observations at the two temperate steppe sites (NM and XL) were analyzed together since (1) they were observed in different years and (2) only two years of data were available for the NM site. \({}^{\text{a}}\), significant at the 0.01 level; \({}^{\text{b}}\), significant at the 0.05 level. models (VPM, MVPM, and MODIS-GPP) were slightly worse than those of the statistical models (TG, GR, VI, AVM, and PCM) (Table 3), with a mean \(R^{2}\) of 0.72 and 0.75, respectively. Nevertheless, the best-performing LUE model (MVPM) and statistical model (VI) had similar modeling performance, characterized by \(R^{2}\) values of 0.80 and 0.79, respectively, AICc values of -153.4 and -150.4, respectively, and RMSE values of 36.5% and 36.9%, respectively. In addition, the performances of all the models were much poorer in drought years than in non-drought years in the temperate steppe (Table 3). The mean \(R^{2}\) of all the models in non-drought years was 32.6% higher than that in severe drought years. The explanatory powers of the two best-performing models (MVPM and VI) were 33.3% and 36.1% lower in drought years than in non-drought years, respectively. The RMSEs were also much larger in drought years than in non-drought years for the MVPM (81.7% vs. 36.7%, respectively) and VI (69.6% vs. 38.8%, respectively) models. All the models except the PCM overestimated the GPP significantly (26.9%-108.7%) in the drought years. Furthermore, the relative qualities of these eight models in the drought years (as reflected by AICc) were different from that during non-drought years (Table 3). For example, though the VPM model had the highest \(R^{2}\), it was the worst because it had the largest RMSE. On an annual scale, the GPP estimates differed greatly by model in the temperate steppe.