# Spatio-Temporal Changes in Water Use Efficiency and Its Driving Factors in Central Asia (2001-2021)
## 3 Results
### Analysis of the Impact of Driving Factors
#### 3.3.2 Statistics of Significant Differences between Drivers and Interactions  
The results of the ecological detection and interactive detection are shown in Figure 6. The ecological detection shows that there is no significant difference in the spatial distribution of WUE between the TEM and RN, DEF, and VPD natural factors. There is a significant difference in the spatial distribution of WUE between all the remaining drivers affecting WUE. The interactions between the drivers were then explored to investigate their driving effect on WUE. The interaction between X1 and X8 (X1 \(\cap\) X8 (0.681) \(>\) Max (q(X1(0.496)), q(X8(0.419)))) is bilinearly enhanced, with X1 and X8 synergistically explaining each other's WUE changes most strongly; the interaction of the two explained 68.1% of the variation in WUE. Specifically, TEM \(\cap\) FVC(X1 \(\cap\) X8, q = 0.681), TEM \(\cap\) CRSI(X1 \(\cap\) X5, q = 0.615), TEM \(\cap\) LAI(X1 \(\cap\) X6, q = 0.661), TEM \(\cap\) EVI(X1 \(\cap\) X7, q = 0.668), PRE \(\cap\) LAI (X2 \(\cap\) X6, q = 0.601), PRE \(\cap\) EVI(X2 \(\cap\) X7, q = 0.606), PRE \(\cap\) FVC(X2 \(\cap\) X8, q = 0.627), TEM \(\cap\) FVC (X1 \(\cap\) X8, q = 0.681), RN \(\cap\) LAI(X3 \(\cap\) X6, q = 0.639), RN \(\cap\) EVI(X3 \(\cap\) X7, q = 0.636), RN \(\cap\) FVC(X3 \(\cap\) X8, q = 0.648), RN \(\cap\) LST(X3 \(\cap\) X9, q = 0.611), CRSI \(\cap\) LST(X5 \(\cap\) X9, q = 0.629), CRSI \(\cap\) DEF(X5 \(\cap\) X14, q = 0.601), CRSI \(\cap\) VPD(X5 \(\cap\) X19, q = 0.628), LAI \(\cap\) LAI (X6 \(\cap\) X9, q = 0.637), LAI \(\cap\) DEF(X6 \(\cap\) X14, q = 0.635), LAI \(\cap\) VPD(X6 \(\cap\) X19, q = 0.662), EVI \(\cap\) LST (X7 \(\cap\) X9, q = 0.644), EVI \(\cap\) DEF(X7 \(\cap\) X14, q = 0.645), EVI \(\cap\) VPD (X7 \(\cap\) X19, q = 0.668), FVC \(\cap\) LST(X8 \(\cap\) X9, q = 0.657), FVC \(\cap\) DEF(X8 \(\cap\) X14, q = 0.655), FVC \(\cap\) VPD (X8 \(\cap\) X19, q = 0.678), and LST \(\cap\) TSVEG(X9 \(\cap\) X20, q = 0.611). The interactions of the above 24 driver combinations all explained more than 60% of the variation in WUE, indicating that 11 drivers--TEM, PRE, RN, CRSI, LAI, EVI, FVC, LST, DEF, TSVEG, and VPD--dominated the explanation of WUE variation in the bilinear enhancement type. Of these, the vegetation factors LAI, EVI, FVC, and CRSI occupied 22 combinations with the six drivers TEM, PRE, RN, LST, DEF, and VPD, so the vegetation factors were the most important among the bilinear enhancement effects. Figure 6: Ecological detection and interactive detection. In addition, the relationship between X8 and X18 (X8 \(\cap\) X18(0.592) \(>\) 0.542 = X8(0.419) + X18(0.123)) was non-linearly strengthened. X8 and X18 strengthened each other's ability to explain changes in WUE; their interaction explains 59.2% of the change in WUE. There were also 29 other combinations of X18 and X20, X6 and X18, X7 and X18, X6 and X10, X7 and X10, X8 and X10, X6 and X11, X7 and X11, X8 and X11, and other drivers with the same type of effect. Specifically, \(\mathrm{FVC}\cap\mathrm{VAP}(\mathrm{X8}\cap\mathrm{X18},\mathrm{q}=0.592)\),\(\mathrm{VAP}\cap\mathrm{TSVEG}(\mathrm{X18}\cap\mathrm{X20},\mathrm{q}=0.463)\), \(\mathrm{LAI}\cap\mathrm{VAP}(\mathrm{X6}\cap\mathrm{X18},\mathrm{q}=0.534)\), \(\mathrm{EVI}\cap\mathrm{VAP}(\mathrm{X7}\cap\mathrm{X18},\mathrm{q}=0.529)\), \(\mathrm{LAI}\cap\mathrm{AOD}\) (X6 \(\cap\) X10, \(\mathrm{q}=0.479\)), \(\mathrm{EVI}\cap\mathrm{AOD}(\mathrm{X7}\cap\mathrm{X10},\mathrm{q}=0.469)\), \(\mathrm{FVC}\cap\mathrm{AOD}(\mathrm{X8}\cap\mathrm{X10},\mathrm{q}=0.512)\), \(\mathrm{LAI}\cap\mathrm{GWS}\) (X6 \(\cap\) X11, \(\mathrm{q}=0.404\)), \(\mathrm{EVI}\cap\mathrm{GWS}(\mathrm{X7}\cap\mathrm{X11},\mathrm{q}=0.390)\), and \(\mathrm{FVC}\cap\mathrm{GWS}(\mathrm{X8}\cap\mathrm{X11},\mathrm{q}=0.432)\). The interaction between vegetation factors LAI, EVI, and FVC with VAP, AOD, GWS, TSVEG, and VAP was shown to be the strongest in explaining WUE. For example, as shown in Figure 4, VAP and TSVEG were weaker in explaining WUE changes (q = 0.123 and q = 0.220).