## 4 Discussion  
Varying Performances of the Eight Satellite-Based GPP Models between the Temperate Steppe and Alpine Meadow  
As expected, our results showed that all eight satellite-based GPP models performed better in the temperature-limited alpine meadow than in the water-limited temperate steppe. The large uncertainties in the GPP models in the dry climate were highly consistent with previous findings [3; 17; 94; 95], and can be mainly attributed to the poor representation of the effect of water stress on GPP in these models [17; 34; 96]. First, the environmental variables (e.g., EVI, LST, Ta, LSWI, and VPD) in these models cannot fully address the soil water content (SWC) dynamics that mainly determine water availability to plants [53; 59; 60]. For example, the decreases in the EVI usually lag behind the SWC when drought occurs [18; 53]. The VPD (LSWI) is more closely related to atmospheric water content [9; 97; 98] (leaf water content [25]) than to the SWC [27; 99]. The LST and Ta may even increase during extreme drought events [53]. Recent studies have indicated that solar-induced chlorophyll fluorescence (SIF) and other improved vegetation indices (i.e., normalized difference drought index and Normalized Multi-band Drought Index) are more sensitive to drought than EVI or LSWI [100; 101; 102; 103], which may serve as a potential direction for future model improvements. Second, the linear functions in these models may not fully capture the complex photosynthetic responses to water stress in arid regions [104]. These can be partially verified by the much weaker correlations between the observed GPP and fPAR/LUE of the LUE models or by multiplication of the environmental factors of the statistical models in the temperate steppe than in the alpine meadow (Figure 8). Furthermore, previous studies have typically used a single function of environmental indicators to reflect the moisture stress for all grassland types [3; 38; 39]. In fact, the responses of photosynthesis to moisture stress vary greatly by grassland type [34; 105]. For example, in the arid temperate steppe, vegetation with shallow roots would experience stomatal closure to avoid water loss if soil water was limited [53]. In contrast, humid alpine meadow vegetation would likely extract deep soil water by increasing the root length if the surface soil moisture was limited due to drought occurrences and/or a period of warming (one or more years) [106; 107; 108]. The function of environmental indicators was mostly generated by the best-fit relationship between the environmental variables and GPP in the limited tower sites covering humid or semihumid ecosystems [8; 25; 33], which would exaggerate the uncertainties of the GPP estimates in the water-limited areas [34].  
In addition, most GPP models performed better in the alpine shrub meadow than in the alpine swamp meadow. This phenomenon can be attributed to two factors. One is that these models cannot reflect the slight depression of surface water on GPP in the alpine swamp meadow. The surface water could decrease the amount of aerial biomass, which plays a key role in photosynthesis and thus decreases CO\({}_{2}\) uptake by the emergent plants [109]. However, all these GPP models assume a higher GPP with more water. The other factor is that these GPP models may aggregate the effects of the PAR on GPP, which was reported not to be a dominant factor in vegetation growth at an eight-day or seasonal scale in the alpine swamp meadow [109; 110]. For example, the observed GPP increased slightly, although the PAR decreased significantly from 2003-2004 (Figures 4d and 5d). Given that the PAR is an important parameter for most GPP models (Table 1), the calculated GPP from the VPM, MVPM, GR, and VI showed significant decreasing tendencies in 2004 relative to 2003 (Figure 7c). Such biases may also occur in the wetlands because their CO\({}_{2}\) flux is mainly controlled by the water depth instead of the PAR at an eight-day or seasonal scale [109].