## 4 Discussion
### Limitations and Uncertainty  
The results acquired in this study were comparable to previous studies, namely, the findings reported in this paper were robust and could be used in future studies. Published estimates of alpine grassland NEP were used to validate the NEP in this research (Table 5). We compared the results of the BEPS-NEP with those of other models. The NEP of alpine steppe and alpine meadow matched the simulation results of other models [68; 69; 71]; however, the simulation results of all these models were lower than the values at the flux tower observation [30], and this was because carbon fluxes measured by eddy correlation observations generally do not include the effects of disturbances such as grazing and fire and therefore may overestimate carbon sinks at the regional scale. For instance, based on the carbon fluxes from global eddy correlation observations, the global NEP was estimated by scaling to be 23 Pg C/yr, which is approximately eight times greater than the global terrestrial carbon sink [91]. In conclusion, due to the prevalence of anthropogenic influences at regional scales and their obvious impact on carbon sinks, eddy correlation is rarely used to directly estimate the size of the carbon sinks on regional scales, but is more often used to recognize the ecosystem-scale response of the carbon cycle to climate change [92]. The uncertainty in the results is can be explained by three main reasons. One reason is that the uncertainty in the BEPS model results arises from the differences in simulation results using different LAI products; this uncertainty was analogous to the uncertainty in the model parameters that affect the simulated time trends [41]. Another reason was the low number of observations in the TRSR, which affected the reliability of the meteorological data used as model inputs [71]. Finally, the response of NEP to climate change was mainly controlled by the sensitivity of autotrophic and heterotrophic respiration to temperature [68], and there was great heterogeneity in soil temperature in the TRSR due to the presence of permafrost, which undoubtedly posed a great challenge to the accuracy of the simulations. Apart from temperature, precipitation, and solar radiation, there were other external factors that had vital effects on carbon fluxes, such as grazing intensity, indiscriminate mining, and human activities [69]. This was particularly true for the restoration of previously destroyed vegetation through anthropogenic measures such as the establishment of protected areas, the implementation of fencing, the return of farmland to grassland, the ecological migration pioneered by the Chinese government in the TRSR via ecological compensation in 2005, and the official establishment of the TRSNP in 2020 [21; 93]. In Equation (15), \(\epsilon\) is the residual between the variation rates of carbon fluxes and climate factor contribution, representing the contribution of human activities and some uncertainty to carbon fluxes. We analyze the spatial distribution of the contribution of other factors to carbon flux trends (Figure 13). The positive contributions of other factors to the GPP trend were mainly concentrated in the YRSP and HRSP, and the positive contributions of other factors to the NEP were distributed in the southern regions of the TRSR and LRSP. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  
In future studies, the multiple coupled effects of natural and anthropogenic factors on the carbon dynamics of alpine grasslands in the TRSR should be considered. Although the EEMD method has certain applicability in the trend decomposition of carbon fluxes and meteorological elements, the processing of EEMD components as robust indicators of interannual variation has not been approached and has many challenges. First, the ability to attribute components to their potential physical processes (e.g., annual climatology, noise) was suggested to be temporary but has not yet been formalized in big data processing [94].