## 2 Materials and Methods
### Data Sources and Processing  
The MODIS data GPP (MOD17A2H) and ET (MOD16A2) products at resolutions of 500 m and 8 days for the period of 2000-2020 were downloaded from the NASA data system ([[https://ladsweb.modaps.eosdis.nasa.gov/](https://ladsweb.modaps.eosdis.nasa.gov/)]([https://ladsweb.modaps.eosdis.nasa.gov/](https://ladsweb.modaps.eosdis.nasa.gov/)) (accessed on 7 May 2023)). Since MOD16A2 data are only labeled with special values (32,762-32,766) for areas such as cities, permafrost ice and snow, bare ground, and water bodies and do not compute ET values, these areas were excluded here. These products are remotely sensed terrestrial products inverted from the data collected with the MODIS sensor on the Terra satellite, and since the transit time is the same, the two sets of data are identical in time and space, and the WUE calculated based on the two products has high spatial and temporal consistency. The downloaded data were resampled to 1000 m by using MRT 4.1 and ArcGIS 10.8 software, and pre-processing operations such as format conversion, study area extraction, and data merging were performed.  
Temperature and precipitation data were obtained from the 1 km resolution monthly precipitation/average temperature dataset downloaded from the website of the National Tibetan Plateau Science Data Center, China ([[http://data.tpdc.ac.cn](http://data.tpdc.ac.cn)]([http://data.tpdc.ac.cn](http://data.tpdc.ac.cn)) (accessed on 28 June 2023)) [26; 27; 28; 29]. Vapor pressure deficit data were obtained from the TerraClimate dataset ([[https://www.climatologylab.org](https://www.climatologylab.org)]([https://www.climatologylab.org](https://www.climatologylab.org)) (accessed on 1 July 2023)), which, based on climate-assisted interpolation, combines high-spatial-resolution climate normals from the World-Clim dataset with coarser-spatial-resolution but time-varying data from CRU Ts4.0 and JRA55. Surface soil moisture was obtained from the 1 km annual-scale dataset of soil moisture for China based on station observations published by the National Tibetan Plateau Science Data Center ([[http://data.tpdc.ac.cn](http://data.tpdc.ac.cn)]([http://data.tpdc.ac.cn](http://data.tpdc.ac.cn)) (accessed on 15 July 2023)) [30]. The temporal scope encompassed the years 2000 to 2020, and the datasets were transformed into the corresponding WGS4 projected coordinate system through the application of a projective transformation by using ArcGIS software.  
Vegetation-type data were derived from the 1:1,000,000 spatial distribution of vegetation types in China provided by the National Earth System Science Data Center ([[http://www.geodata.cn](http://www.geodata.cn)]([http://www.geodata.cn](http://www.geodata.cn)) (accessed on 22 June 2023)), a national platform for scientific and technological infrastructure, and used to determine the distribution of ten vegetation types (coniferous forests, broad-leaved forests, meadows, grasslands, grasslands, alpine vegetation, cultivated vegetation, marshes, thickets, and deserts) and unvegetated land in the study area. For the calculation of the WUE statistics, coniferous forests and broad-leaved forests were combined into a single category, designated as \"forest types\". Similarly, meadows, grasslands, and alpine vegetation were combined into a single category designated as \"grasslands\". In contrast, scrub, cultivated vegetation, and swamps were not combined, and deserts and non-vegetated areas were excluded from the statistics, resulting in a total of five categories.  
Table 1 below shows the data sources for this study.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline