## 1 Introduction
Currently, there is considerable research on the response of ecosystem carbon-water cycles to temperature variations. For example, a study by [PERSON] et al. [29] using isotopic techniques demonstrated a positive correlation between temperature and WUE. Furthermore, [PERSON] et al. [30] revealed that from 1982 to 2008, global WUE exhibited a stronger response to temperature, particularly in high-latitude regions. Some studies focus on the response of WUE to climatic factors across different vegetation types. For example, [PERSON] et al. [2] found that from 2000 to 2018, alpine meadows and alpine grasslands in northwest China were more susceptible to temperature changes, while temperate desert grasslands were more influenced by variations in precipitation. Similarly, [PERSON] et al. [8] found that in the Qinghai-Tibet Plateau region, alpine steppes are more sensitive to changes in WUE in response to climatic factors, whereas alpine desert steppes show lower sensitivity. However, WUE is not solely driven by a single environmental factor, such as precipitation or temperature; it is influenced by the interplay of multiple environmental factors. Consequently, exploring the effects and underlying mechanisms of these factors on WUE is essential for clarifying the carbon-water coupling in ecosystems. Although the effects of climatic variables on WUE in alpine grasslands have been widely studied, most existing research has relied on simple linear correlations to evaluate the influence of individual factors [31, 32, 33]. However, WUE is inherently governed by the synergistic and often nonlinear interactions among multiple hydrothermal and radiative variables, which can lead to mutual reinforcement or attenuation. With the increasing availability of multivariate statistical tools, it has become essential to move beyond univariate analyses to quantify the combined and indirect influences of multiple climatic drivers on WUE dynamics. This need is particularly urgent in high-altitude regions such as the Qinghai-Tibet Plateau, where ecosystems are exceptionally sensitive to climate variability [34, 35]. Despite progress in this area, few studies have conducted integrated, elevation-stratified evaluations of WUE response mechanisms. Addressing this gap, the present study employs partial correlation analysis and structural equation modeling (SEM) to disentangle the direct and mediated pathways through which temperature, precipitation, radiation, and other environmental factors influence WUE across elevation gradients. The findings provide a comprehensive understanding of vegetation carbon-water coupling in alpine ecosystems under accelerating climate change. Building on this framework, the present study focuses on the alpine grasslands of the Qinghai-Tibet Plateau--an ecologically fragile and climate-sensitive region [36, 37, 38, 39]--where the drivers of vegetation productivity and water fluxes are particularly complex and elevation-dependent. Using multi-source datasets from 2001 to 2018, we analyzed spatial and temporal variations in net primary productivity (NPP), evapotranspiration (ET), and WUE across elevation gradients during the growing season. Specifically, partial correlation analysis and SEM are employed to quantify both the direct and indirect effects of six climatic variables--precipitation, temperature, soil moisture, soil temperature, vapor pressure deficit (VPD), and photosynthetically active radiation (PAR)--on WUE. Additionally, a contribution rate decomposition approach is applied to assess how NPP and ET differentially influence WUE across elevation zones. By integrating these methods, the study aims to uncover elevation-sensitive mechanisms of ecosystem carbon-water coupling, offering new insights into how alpine vegetation responds to multi-factor climate regulation under accelerating environmental change.