## 5 Conclusions  
In this study, the spatiotemporal variation of WUE in IM from 2001 to 2018 was analyzed using a trend analysis. A quantitative analysis of the importance of vegetation and climatic factors on the WUE was carried out through grey relational analyses. Relatively important influencing factors of WUE were identified and chosen and their contributions to the WUE trend were quantified using ridge-regression analyses. Our results revealed that the multiyear average value of the WUE in IM was 1.39 g C m\({}^{-2}\) mm\({}^{-1}\) with an annual average increasing rate of 0.0071 g C m\({}^{-2}\) mm\({}^{-1}\) yr\({}^{-1}\) during 2001-2018. In terms of driving factors, LAI and precipitation were the most important factors affecting the WUE in IM, followed by relative humidity and wind speed. Air temperature, water vapor pressure, and sunshine duration were relative less important to the WUE. According to the independent contributions of important influencing factors to the WUE, changes in LAI showed the highest contribution (0.008 g C m\({}^{-2}\) mm\({}^{-1}\) yr\({}^{-1}\)) and a relative contribution rate of 44.57%, which were significantly higher than that of precipitation, relative humidity, and sunshine duration. It indicated that LAI was the primary contributor to, and vegetation change was the dominant factor of the regulation of the WUE variation in IM. The relative contribution rates of LAI were different in different vegetation types, with the value in swamp the smallest and meadow the largest. These results will deepen our understanding on the carbon-water coupling process of terrestrial ecosystems under environmental change.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/rs14184541/s1](https://www.mdpi.com/article/10.3390/rs14184541/s1)]([https://www.mdpi.com/article/10.3390/rs14184541/s1](https://www.mdpi.com/article/10.3390/rs14184541/s1)), Figure S1: Evaluation of GLASS eight-day composite 1 km resolution (a) GPP, (b) ET, and (c) WUE products against flux observations.  
Conceptualization, H.L.; Formal analysis, T.Z.; Funding acquisition, G.Y.; Methodology, G.Y.; Software, H.L.; Supervision, P.L.; Validation, X.B. and X.Z.; Visualization, J.L.; Writing--original draft, H.L.; Writing--review & editing, X.B., X.Z. and T.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (41801099), the second Tibetan Plateau Scientific Expedition and Research Program (2019 QZKK0307), and the Key Research and Development Program of Sichuan (2022 YFS0491).  
The GLASS data are available at [[http://www.glass.umd.edu/Download.html](http://www.glass.umd.edu/Download.html)]([http://www.glass.umd.edu/Download.html](http://www.glass.umd.edu/Download.html)), accessed on 12 March 2022. The meteorological data are available at [[http://data.cma.cn](http://data.cma.cn)]([http://data.cma.cn](http://data.cma.cn)), accessed on 12 March 2022. The vegetation types data are available at [[https://www.resdc.cn/](https://www.resdc.cn/)]([https://www.resdc.cn/](https://www.resdc.cn/)), accessed on 12 March 2022. The DEM are available at [[https://earthexplorer.usgs.gov/](https://earthexplorer.usgs.gov/)]([https://earthexplorer.usgs.gov/](https://earthexplorer.usgs.gov/)), accessed on 12 March 2022. The flux data are available at [[http://www.cmern.org.cn/](http://www.cmern.org.cn/)]([http://www.cmern.org.cn/](http://www.cmern.org.cn/)), accessed on 12 March 2022.  
We would like to thank the University of Maryland for supporting the data availability of the GPP, ET and LAI data. The author thanks anonymous reviewers for providing invaluable comments on the original manuscript.  
The authors declare no conflict of interest.