## 1 Introduction  
Evapotranspirationigration is a key component of the water cycle and energy balance in terrestrial ecosystems and can be classified according to its components as ecosystem transpiration related to plant stomata and ecosystem evaporation of nonstomatal components, and the latter specifically includes surface evaporation components such as foliar evaporation and soil evaporation [1]. Transpiration and evaporation can effectively reduce the surface temperature, relieving the greenhouse effect. However, the fertilization effect of rising CO\({}_{2}\) concentrations [2] accelerates plant growth, leading to higher water use efficiency and relatively lower transpiration rates [3; 4], which in turn lead to global warming and drying [5] and an increase in surface temperature. The feedback relationship between evapotranspiration components and the environment is complex, and the precise partitioning of evapotranspiration components is important for thoroughly elucidating the feedback processes and roles between components and the environment. Considering that the water coupled carbon relationship model based on flux observation is a better starting point from which to investigate the partitioning of ecosystem evapotranspiration components, some new separation methods have been developed in recent years based on this assumption: [PERSON] et al. proposed potential water use efficiency and apparent water use efficiency using ET and GPP from flux observation sites under the premise of satisfying three assumptions of evapotranspiration partitioning [6]. [PERSON] and [PERSON] used a statistically significant positive intercept (soil evaporation term) in the model to achieve T/ET estimation [7]. Only continuous flux observations over relatively long periods (at least three years) can guarantee the physical significance of the intercept term. [PERSON] et al. estimated the temporal pattern of WUE using total primary productivity and evapotranspiration by separating the period in which transpiration dominates and using a random forest approach to train the WUE within the period in each short time interval to achieve partitioning of evapotranspiration [8]. Although the TEA algorithm output is robust to dataset noise, it exhibits some sensitivity to sites and model structures where evapotranspiration is relatively constant, leading to ecosystem evapotranspiration overestimation. In terms of practical observations that can evade the disadvantages of these models with a lack of independent universal validation of evapotranspiration components and their assumptions, the direct separation of ecosystem-scale evapotranspiration into transpiration and soil evaporation is still extremely challenging [9]. Methods to separate evapotranspiration based on observational perspectives include (1) the sap flow method (SF) or the micro-lysimeter (ML) method combined with flux observations; (2) isotope observations; and (3) high-frequency eddy covariance observation [10]. The sap flow method has major advantages for single-tree transpiration measurements but still suffers from coefficient correction, thermal damage, and scale expansion [11; 12], which introduce some errors in the estimation of transpiration. The micro-lysimeter method allows direct measurement of soil evaporation at the site [13], and is used for validation of soil evaporation models [14]. Eddy covariance observation is a well-recognized method for studying water-carbon fluxes in ecosystems and has been used worldwide. The direct partitioning of evapotranspiration from eddy covariance observations consists of three main types of methods: the modified relaxed eddy accumulation (MREA) method [15; 16; 17], the conditional eddy covariance (CEC) method [17], and the flux variance similarity (FVS) method [18; 19]. The relaxed eddy accumulation method considered only the nonstomatal process fluxes and solved for the stomatal component fluxes as a residual term, which resulted in an overestimation of transpiration [16; 17].