# The Contributions of Climate and Human Activities to Water Use Efficiency in China's Drylands
## 3 Result
### Analysis of Driving Factors on Temporal Dynamics of WUE  
The relationships between monthly WUE, GPP, ET, and climatic factors are illustrated in Figure 4. Figure 4a illustrates that, throughout the research period, WUE demonstrated a significantly strong positive relationship with Tem and VPD (\(p<0.01\)), but it did not display any notable correlation with Prec and Solra. Additionally, both gross primary productivity (GPP) and evapotranspiration (ET) were found to have a markedly positive association with precipitation (\(p<0.01\)). ET was significantly negatively correlated with Tem (\(p<0.01\)), whereas GPP was not significantly correlated with Tem. Both GPP and ET were extremely positively correlated with the VPD. In the spring and summer months (Figure 4b,c), WUE displayed a highly significant positive correlation with Prec (\(p<0.05\)). Moreover, in fall (Figure 4d), WUE exhibited an extremely significant positive correlation with Prec and VPD (\(p<0.01\)). Prec demonstrated highly significant positive correlations with GPP and ET during the summer, while exhibiting negative correlations during the spring and fall (\(p<0.01\)). In contrast, VPD showed no significant correlations with GPP and ET in summer but displayed extremely significant positive correlations in spring and fall (\(p<0.01\)) (Figure 4). The effect of Prec on GPP was substantial in all three seasons, with significant positive correlations present in each season, and stronger correlations observed in spring and fall (Figure 4). The partial correlation between GPP and Tem also revealed seasonal differences, with a significantly negative correlation in fall (\(p<0.05\)) and an extremely significant positive correlation in summer (\(p<0.01\)) (Figure 4). The relationship between GPP and VPD was positive in fall but not in other seasons. VPD displayed a positive correlation with ET in fall (\(p<0.01\)) but did not show a significant correlation with ET in other seasons (Figure 4). Figure 4: Degree of correlation between climate factors on WUE, GPP, and ET in different seasons. Figure 3: The pattern of WUE change (**a**), and the spatial distribution of the annual mean WUE (**b**). The geographic distribution of partial correlations among climatic elements and WUE, GPP, and ET at an annual scale is shown in Figure 5. Over half of the study area displayed a negative correlation between WUE and Prec, among which the eastern drylands of China, central Tibet, and Xinjiang exhibited a strong negative correlation, and the central part of Qinghai Province and the southeastern part of Gansu Province showed a strong negative correlation (Figure 5a). The partial correlation between GPP and ET and Prec was predominantly positive (Figure 5b,c). The strongest positive correlation was observed in the eastern and northeastern regions of the study area (Figure 5b), while the negative correlation between ET and Prec was mainly concentrated in Qinghai and southeastern Tibet. The region with a proportion of 60.53% exhibited a negative correlation between the WUE and Tem (Figure 5d). Additionally, the central part of the study area, as well as the northern parts of Inner Mongolia and Xinjiang, and southwestern Tibet, displayed a strong negative correlation. The partial correlation between GPP, ET, and Tem was primarily positive (Figure 5e,f), with weak positive correlations. The regions with a negative correlation between WUE and VPD were mainly concentrated in the northeastern part of Inner Mongolia and the southern part of Shanxi and Hebei provinces, and sporadic distribution was found in Shaanxi and Gansu regions in the middle of the study area, as well as in Tibet and Xinjiang (Figure 5g). The strongest correlation between GPP, ET, and VPD was observed in the eastern and northeastern parts of the study area and in the northern part of Xinjiang (Figure 5h,i).