## 4 Discussion
### Strengths and Limitations  
The PML_V2 ET data used in this study comprise ET-GPP coupling data with 500 m resolution [37]. The GPP and ET in PML_V2 ET dataset are mutually restricted and limited, which greatly improves the accuracy of ET compared with the previous models [32; 52; 53]. Meanwhile, several scholars have investigated the applicability of PML_V2 ET data in the Chinese region. [PERSON] et al. compared and estimated ET and its components in 10 major river basins in China during 2001\(-\)2020 and found that PML_V2 ET products were superior to other typical products (MOD16A2, SEBAL, GLEAM, MOD17A2H, VPM and EC-LUE) in estimating ET and GPP [54]. [PERSON] et al. validated the PML_V2 ET using MOD16 ET in the Three Gorges Reservoir Area (TGRA), and the validation results showed that the performance in the TGRA was slightly better than that of the PML_V2 ET in Northern China, which also confirmed the reliability of the data [55]. In addition, the PML_V2 ET product has a  
Figure 19: Regional area proportion statistics of significant positive correlation between ET and T (**a**) on each elevation gradient in the YRB; regional area proportion statistics of significant positive correlation between ET and SR (**b**) on each elevation gradient in the YRB; regional area proportion statistics of significant negative correlation between ET and P (**c**) on each elevation gradient in the YRB; the area proportion statistics of the regions with significant positive correlation between ET and T (PC_ET&T), positive correlation between ET and SR (PC_ET&SR), and negative correlation between ET and P (NC_ET&P) in the YRB on the monthly scale (**d**). relatively high spatial and temporal resolution, which can extract more detailed information of the ET change at the spatial and temporal scales. Therefore, the PML_V2 ET data are promising in ET analysis over the YRB with strong spatial heterogeneity. This study using PML_V2 ET data found that the annual average ET in the YRB showed a significant upward trend from 2001 to 2020, which was consistent with the previous research results in the YRB. For example, [PERSON] et al. found that the annual ET increased significantly at a rate of 1.94 mm/a during 1998-2017 [56]. The ET during 1990-2019 estimated by [PERSON] et al. based on the generalized evaporation complementarity relationship showed an upward trend of 0.21 mm/a [57], and the ET estimated by [PERSON] et al. from 1982 to 2015 based on the machine learning method also showed an upward trend [58]. The difference in the magnitude of ET trends in these studies was mainly caused by the different research periods. There was spatial variability in the role of climatic factors on ET [56; 59], and this study found that the top three climatic factors dominating ET changes in the basin were SR, SH, and P. This has also been explored in other studies. [PERSON] et al. found that the ET in the Dongting Lake and Poyang Lake basins, located in the southeastern part of the YRB, was mainly influenced by relative humidity from 1998 to 2017 [56]. [PERSON] et al. found that the annual ET\({}_{0}\) in UYRB from 1951 to 2020 was most affected by relative humidity [19]. [PERSON] et al. found that the spatial pattern of ET in the YRB from 1980 to 2014 was determined by both T and P, while, in most areas, SR was the dominant climatic factor of actual ET [22]. Meanwhile, this study found that regions where T or U was the dominant factor for ET variability occupied a smaller area, which was consistent with the findings of [PERSON] et al., who found the sensitivity of ET\({}_{0}\) to four major climatic variables within the YRB from 1960-2000 and found that relative humidity was the most sensitive variable in the basin, followed by shortwave radiation and T, both with similar sensitivity, and the impact of U was the smallest [33]. In addition, the influence of climatic variables on ET varied from month to month [33].