## 5 Conclusions  
Studying the temporal and spatial variation characteristics of WUE in terrestrial ecosystems and their response mechanisms to environmental changes helps to understand the adaptability of different vegetation types to habitats, promotes insights into the carbon and water cycles of terrestrial ecosystems, and provides data support for the scientific regulation and management of local water resources. In this study, random forest was used to simulate the growing season GPP of the QTP from 1982 to 2018. The results indicated that the annual average GPP during the growing season is 508.35 gC\(\cdot\)m\({}^{-2}\cdot\)yr\({}^{-1}\), with a growth rate of 1.784 gC\(\cdot\)m\({}^{-2}\cdot\)yr\({}^{-2}\). The multiple regression models were used to integrate five ET product sets, from which we found that the annual average ET is 355.44 mm\(\cdot\)yr\({}^{-1}\), with a growth rate of 0.817 mm\(\cdot\)yr\({}^{-2}\). Both GPP and ET show a significant upward trend. The annual average of GPP and ET exhibit a spatial pattern of decline from southeast to northwest. The results of temporal and spatial evolution trends of WUE indicated that the annual average WUE is 1.43 gC\(\cdot\)m\({}^{-2}\cdot\)mm\({}^{-1}\cdot\)yr\({}^{-1}\), with an annual growth rate of 0.002 gC\(\cdot\)m\({}^{-2}\cdot\)mm\({}^{-1}\cdot\)yr\({}^{-2}\). The correlations between temperature, precipitation, GPP, and ET were analyzed, revealing significant positive relationships between GPP, ET, and both temperature and precipitation. With the rise in temperature and precipitation over the QTP, both GPP and ET have increased to varying extents, while WUE has also exhibited an upward trend. Moreover, pronounced spatial heterogeneity was observed between the eastern and western regions of the QTP. To evaluate the impact of climate change on WUE, the study employed a 10-year moving window to examine the IAV of temperature and precipitation. The findings demonstrate that the IAV of climate factors closely aligns with changes in GPP and ET, indicating that climate variability significantly influences GPP and ET, ultimately driving variations in WUE. The vegetation ecosystem in the QTP is influenced by large-scale climate oscillations, local climatic changes, and human activities. While this paper considers the effects of  
Figure 8: IAV of variables in 10-year time windows (**(a)** shows the IAV of temperature and precipitation, black plots show the IAV of temperature for every 10-year moving window, while red plots display the IAV of precipitation for the same 10-year moving window; (**b**) shows the IAV of GPP, ET and WUE, black square plots shows the IAV of GPP, the black square scatter points represent the IAV of GPP, the red circular scatter points represent the IAV of ET, and the green triangular scatter points represent the IAV of WUE). temperature and precipitation on GPP and ET, many other factors remain unexamined. For instance, increasing atmospheric CO\({}_{2}\) concentrations can significantly increase WUE. Factors such as nitrogen deposition, drought events, human grazing activities, land use changes, and ecological engineering projects also impact WUE. These factors will be considered in future research. In addition, the RF model was applied to simulate 37 years of GPP changes across the QTP. Although the RF model achieved higher accuracy than the multiple linear regression model, it still has notable limitations. For instance, the model's performance is highly sensitive to parameter selection; its extrapolation capability is limited, making it vulnerable to changes in new environmental variables or extreme climatic conditions. Additionally, the RF model cannot reveal the causal mechanisms underlying the relationships between environmental variables and GPP variations, and the interpretability of its results remains limited.