## 4 Results and Analysis
### Analysis of the Impact of Various Factors on WUE
#### 4.3.2 Partial Correlation Analysis of Climate Factors  
Figure 6a,b present the meteorological station data and raster data for precipitation and temperature, respectively, in the Li River Basin from 2001 to 2020. For precipitation, the change in meteorological station data is similar with the raster data's change basically. The precipitation data from meteorological stations exhibit an annual increase at a rate of 56.996 mm/a, while the raster data also show an upward trend at a rate of 19.547 mm/a. A notable difference is observed in 2015 and 2019, where the station data show substantial increases in precipitation, whereas the raster data's values remain relatively lower. Regard  
Figure 5: Partial correlation analysis and primary influencing factor analysis of WUE with GPP and ET in Li River Basin from 2001 to 2020.  
ing temperature, the station data for temperature have higher overall values than the raster data. From 2001 to 2018, both datasets exhibit similar trends; however, in 2019, the meteorological station data experience a sudden significant decrease, leading to a downward trend, whereas the raster temperature data maintain an overall upward trend at a rate of 0.0165 \({}^{\circ}\)C/a.  
The correlation between WUE and climate factors in the basin from 2001 to 2020 is shown in Figure 6. For precipitation, the correlation coefficients of WUE with it ranged from \(-\)0.7032 to 0.7234. The proportions of significant positive and insignificant positive correlation areas were 3.67% and 67.22%, respectively, totaling 66.90%; the significant positive correlation areas were predominantly concentrated in the northern part of Xing'an as well as the junction of Linggui and Yangshuo, and the remaining parts were distributed in the southern part of Pingle and the southeastern part of Lingchuan. The proportion of negative correlation area was about 33.10%; almost all of them had an insignificant negative correlation, as the significant negative correlation was only 0.34%. The significant negative correlation areas were small and scattered, and most of them were located in Yangshuo, while the insignificant negative correlation areas were relatively evenly distributed.  
Under the condition of controlling the effect of precipitation, Figure 7(b1,b2) show the spatial distribution of correlation coefficients between air temperature and WUE, which are more differentiated than precipitation, with values ranging from \(-\)0.809437 to 0.623538. The areas with positive correlation accounted for about 7.33% of the total area, while the percentage of negatively correlated areas was 92.67%, and overall, air temperature was negatively correlated with WUE. Further analysis showed that the percentages of areas with significant and insignificant negative correlations between temperature and WUE were 13.46% and 79.22%, respectively; the proportion of areas with significant negative correlations gradually increased from north to south; and the proportion of areas with insignificant negative correlations were higher and evenly distributed within the basin. Among the positive correlations, the area of significant positive correlation was 0.02%, which only appeared in Linggui and Pingle; the proportion of areas with insignificant positive correlation was relatively high (about 7.30%); and they were predominantly concentrated in Xing'an and Pingle. There was also a certain distribution at the junction of Linggui and Guilin urban areas.  
Figure 6: Annual average precipitation (**a**) and temperature (**b**) data for meteorological stations and raster types in the Li River Basin from 2001 to 2020.