# Ecosystem Water Use Efficiency in the Three-North Region of China Based on Long-Term Satellite Data
## 3 Results
### Analysis of Influencing Climate Factors of Vegetation WUE Change  
The spatial distributions of the \(t\)-test analysis of the correlation results between WUE and climate factors (annual precipitation and annual average temperature) are shown in Figure 8. In the northern and eastern parts of the Three-North region, WUE mainly showed a negative correlation with temperature. A positive correlation with temperature mainly occurred in the western and southwestern parts. These results suggested that in eastern parts of Three-North region, the increase of temperature may both increase GPP and ET, but the net effects in WUE was negative, while in western parts, the increase of temperature (usually with increase of temperature) would slightly increase WUE. The statistics showed that 53.61% of the total area had positive correlations between WUE and temperature, with 3.19% and 0.91% of the total area showing significant (\(p<0.05\)) and extremely significant positive correlations (\(p<0.01\)). Only 1.66% and 0.45% of the total area showed significant (\(p<0.05\)) and extremely significant negative correlations (\(p<0.01\)). For precipitation, the western parts of the Three-North region mainly showed negative correlations between precipitation and WUE, while the eastern parts mainly showed positive correlations, as shown in Figure 8b. A total of 44.13% of the total area showed positive correlations between precipitation and WUE, with 2.29% and 0.63% of the total area showing significant (\(p<0.05\)) and extremely significant (\(p<0.05\)) positive correlations, respectively. A total of 4.36% and 1.5% of the total area showed significant (\(p<0.05\)) and extremely significant (\(p<0.05\)) negative correlations, respectively, between WUE and precipitation. The spatial distribution pattern of correlations between precipitation and WUE suggested that the increase of precipitation in eastern parts will significant increase WUE, while in western parts, the effects are complex.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Categories** & **Sen Standard** & **MK Test Standard** & **Hurst Exponent** & **GPP** & **ET** & **WUE** \\ \hline Sustainability and significant & & & & & & \\ degradation & \(\beta>0\) & \(\mid\)\(Z\)\(\mid\) 1.96 & \(H>0.5\) & 3.75\% & 7.79\% & 5.28\% \\ Sustainability and slight & & & & & & \\ degradation & \(\beta\leq 0\) & \(\mid\)\(Z\)\(\mid\)\(\leq\) 1.96 & \(H>0.5\) & 34.29\% & 22.41\% & 38.54\% \\ Sustainability and slight & & & & & & \\ improvement & \(\beta\leq 0\) & \(\mid\)\(Z\)\(\mid\)\(\leq\) 1.96 & \(H>0.5\) & 42.77\% & 49.92\% & 40.00\% \\ Sustainability and significant & & & & & & \\ improvement & \(\beta>0\) & \(\mid\)\(Z\)\(\mid\) 1.96 & \(H>0.5\) & 13.79\% & 18.10\% & 11.15\% \\ Undetermined future variation & - & - & \(<H\) 0.5 & 5.40\% & 1.78\% & 5.03\% \\ in trends & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Sustainability and variation trend analysis of GPP, ET, and WUE in the Three-North region of China.  
Figure 8: Spatial distributions of the correlations between the annual WUE and climate factors ((a): annual average temperature; (b): annual precipitation).