## 4 Discussion  
### Assessment of Data Precision  
The MODIS ET and GPP products were used to calculate the WUE in FPENC. Though the spatial resolution of 500 m and use of 8-day composites may obscure fine-scale variations in WUE, especially for heterogeneous land covers, the method has been used previously [6], and proved to be highly accurate. The MODIS GPP have proved to be underestimated in arid areas [52], and thus may lead to the underestimation of WUE; therefore, we tested the MODIS-based WUE with other data sources for further verification. We compared the annual average WUE values from the MODIS data and the EC dataset, with one shrubland  
Figure 14: **(a)** The WUE in drought years (WUEd), **(b)** the ecosystem resilience (Rd), and **(c)** the classification of the Rd; **(d)** the WUE in drought years (WUEd), the annual average WUE (WUEm), and the ecosystem resilience (Rd) for the ten vegetation types during the study period (from 2003 to 2022): woody savannas (WSAs), savannas (SAVs), mixed forest (MF), open shrubland (OSH), grassland (GRA), evergreen needle forest (ENF), deciduous broadleaf forest (DBF), closed shrubland (CSH), and cropland (CRO). Resilient: Rd \(\geq\) 1; slightly non-resilient: 0.9 \(<\) Rd \(<\) 1; moderately non-resilient: 0.8 \(\leq\) Rd \(\leq\) 0.9; and severely non-resilient: Rd \(<\) 0.8.  
and six grasslands selected [23]. In addition, we added another four EC sites located in the FPENC [53; 54; 55]. The EC measuring the WUE was higher than the MODIS based on the WUE with the root mean square error (RMSE) being 0.27 gC kg\({}^{-1}\)H\({}_{2}\)O\({}^{-1}\), the R-square (R2) being 0.83, and the relative error (bias) being 0.20 gC kg\({}^{-1}\)H\({}_{2}\)O\({}^{-1}\) (Figure S6). Though the results showed the MODIS WUE was lower than the EC-observed values, the verification showed that our research can capture the long-term trend and spatio-temporal variation of WUE; thus, it would be valuable to guide the vegetation afforestation in FPENC. With the fast development of a remote sensing product, a higher spatio-temporal resolution of WUE may be used to carry out further analysis in FPENC.  
As for the meteorological data source, Temp, VSWC, and Rn were derived from ERA5-land dataset, which has been proven to have high precision [56]. Several studies have highlighted that the ERA5-land dataset has a relatively large deviation from the mainstream precipitation products in mainland China [42; 43], so we used the CHIRPS precipitation dataset. Previous study in the semi-arid grasslands in Inner Mongolia has proved that the CHIRPS precipitation dataset reveals better accuracy than the EAR5-land with regard to both RMSE and bias [56]. As for our study, we compared the monthly PPT data from CHIRPS with observed data selected from 33 meteorological stations located in the FPENC for the year 2022, and the results show that the R2 was 0.84 and RMSE was 24.24 mm (Figure S8), indicating a higher precision of the CHIRPS PPT. The TerraClimate VPD used in our research has been identified as having good precision with station-based VPD [5]. Therefore, high-precision data sources ensure the accuracy of analysis on the spatio-temporal characteristics of WUE and its driving forces in the FPENC.