# The Contributions of Climate and Human Activities to Water Use Efficiency in China's Drylands
## 3 Result  
### Temporal and Spatial Variations of WUE  
As shown in Figure 2a, the period from 2001 to 2020 demonstrated a general downward trend in WUE across China's drylands, with a decline rate of \(-\)0.004 gCmm\({}^{-1}\) H\({}_{2}\)O\(\cdot\)yr\({}^{-1}\). In contrast, both GPP and ET presented increasing trends (Figure 2b), with growth rates of 4.96 gCm\({}^{-2}\cdot\)yr\({}^{-1}\) and 4.25 mm\(\cdot\)yr\({}^{-1}\), respectively. In terms of spatial distribution, WUE displayed a minimal decreasing trend, covering 66.8% of China's drylands (Figure 2c). These areas were primarily located in the central, eastern, and northeastern drylands of China. Approximately 13.08% of the regions exhibited a significant decreasing trend (\(p\) < 0.05) and were primarily distributed in the eastern and northeastern drylands and Xinjiang. The study area exhibited a significant increase in WUE (\(p\) < 0.05), accounting for 1.95% of the total scattered sporadically in Qinghai and Tibet (Figure 2c). Approximately 95.37% of the regions displayed a significant increasing trend in GPP (\(p\) < 0.05), accounting for 60.71% of China's drylands. Significant growth was observed in the eastern drylands and parts of Qinghai (Figure 2d). Furthermore, approximately 91.34% of the regions  
\begin{table}
\begin{tabular}{c c} \hline
**Judgment Criteria** & **Type** \\ \hline \(q(X_{1}\cap X_{2})<\) Min(\(q(X_{1})\), \(q(X_{2})\)) & Weaken, nonlinear \\ Min(\(q(X_{1})\), \(q(X_{2})\))) \(<q(X_{1}\cap X_{2}))\) \(<\) Max(\(q(X_{1})\), \(q(X_{2})\)) & Weaken, uni- \\ \(q(X_{1}\cap X_{2})>\) Max(\(q(X_{1})\), \(q(X_{2})\)) & Enhance, bi- \\ \(q(X_{1}\cap X_{2})=q(X_{1})+q(X_{2})\) & Independent \\ \(q(X_{1}\cap X_{2})>q(X_{1})+q(X_{2})\) & Enhance, nonlinear \\ \hline \end{tabular} Notes: \(q\) (\(X_{1}\)) and \(q\) (\(X_{2}\)) respectively represent the explanatory power of two independent factors.  
\end{table}
Table 4: Interactive types between pairs of factors.  
displayed an increasing trend in ET, with 59.3% showing significant growth (\(p<0.05\)). Most of the significant growth in ET was concentrated in the central and eastern study areas (Figure 2e).  
Figure 3a illustrates the variability patterns in the WUE, GPP, and ET. The two models with the largest proportion of the study area were C and F (Table 3), accounting for 62.78% and 25.34% of the study area, respectively. More than half of the decrease in WUE was primarily due to the dominant growth of ET (model C), with its growth rate surpassing that of GPP. This pattern was mainly observed in the eastern and central parts of the study area as well as in the southwest edge of Tibet and Xinjiang. Conversely, the increase in WUE was mainly attributed to the growth of GPP (model F), which was concentrated in central Tibet, central Qinghai Province, the border of Inner Mongolia, Hebei, Shaanxi, and Xinjiang. The increase in WUE resulting from the increase in GPP and decrease in ET (model E) was mainly concentrated in northeast Tibet and southwest Qinghai.  
The spatial distribution of average WUE in the study area during 2001-2020 was categorized into five groups based on natural discontinuities. As illustrated in Figure 3b, the average WUE in China's drylands was identified to be 1.15 gCmm\({}^{-1}\) H\({}_{2}\)O, and the general spatial pattern exhibited an eastward progression from west to east, with the exception of Xinjiang. In Xinjiang, the WUE was higher overall, displaying a spatial trend of decline from the desert edge to the desert center, and was particularly high in the northern region.  
Figure 2: Temporal changes of WUE, GPP, and ET in drylands of China from 2001 to 2020. (**a**) is the time series change in WUE, (**b**) is the time series change in GPP and ET, and (**c**–**e**) are the changes in WUE, GPP, and ET for each raster.