## 4 Discussion
### Effects of LAI and Climate Change on WUE Variation  
The contributions of changes in influencing factors to the WUE trend were determined by the variation degree of these factors and the sensitivity of the WUE to these changes. Previous studies have demonstrated that LAI, temperature, precipitation, wind speed, air pressure, relative humidity and sunshine duration can all influence the WUE trend [11; 45; 60]. In this study, grey relational analyses of WUE with LAI and six climate factors were carried out. According to the grey relational analysis results, LAI, precipitation, wind speed, and relative humidity were found to be the relatively important influencing factors to the WUE. They represented vegetation, water and evaporation conditions, respectively. LAI is closely related to photosynthesis of vegetation and plant transpiration, and it directly or indirectly influences the carbon-water exchange between vegetation and the atmosphere [62]. Studies based on eddy covariance flux sites demonstrated that LAI influenced the WUE strongly and it was the dominant factor that influenced seasonal and interannual changes in the WUE in meadow [3; 63]. The current study showed that the grey relational degree between the WUE and LAI was the highest (Figure 6a, Table 1). Regionally, LAI variation was the dominant cause of WUE variation (Figure 9). It showed the highest contribution and relative contribution rate to the WUE variation. In the past decades, ecological restoration projects in northern China have expanded forest and meadow areas, bringing about a significant growth of LAI [29; 64]. On one hand, increasing LAI is conducive to plant absorption of CO\({}_{2}\) and the production of more biomass. On the other hand, LAI controls the relative magnitude of plant transpiration and indirectly impacts soil evaporation. Increases in LAI lead to increases in T/ET, thus resulting in the increase in WUE. Nevertheless, [PERSON] et al. [65] and [PERSON] et al. [33] found a threshold in the positive effect of LAI on the WUE. When LAI was lower than this threshold, the WUE quickly increased with the increase in LAI; otherwise, the WUE slowly increased and even decreased. In this study, we divided LAI into increments of 0.2 (Figure 10). According to the statistics on the sensitivity coefficient of the WUE to LAI under different gradients as well as the contribution of LAI to the WUE variation, we found that with the increase in LAI, the sensitivity of the WUE to LAI decreased (Figure 10a), and the contribution curve of LAI showed double peaks occurring in ranges of 0.2\(\sim\)0.4 and 1.4\(\sim\)1.6, respectively (Figure 10b). As LAI increased from 0 to 1.6, the contribution of LAI changes to the WUE first increased and then decreased, and finally increased. The overall variation trend was not significant, but the contribution was relatively high. When LAI > 1.6, its contribution to WUE quickly decreased, leading to less of a contribution by LAI to the WUE trend of forest and swamp than other vegetation types (Figure 9a). By contrast, climate change had a smaller contribution to the WUE variation than LAI. The influences of climate factors on the WUE were related to the vegetation type or  
Figure 10: (**a**) Sensitivity of WUE to LAI and (**b**) contributions of LAI to WUE trends under different LAI gradients. 1: 0–0.2, 2: 0.2–0.4, 3: 0.4–0.6...... and so on. water conditions [15]. In semi-arid and arid regions, increasing precipitation is conducive to maintaining vegetation growth and the increase in WUE [32]. This study recognized a rising trend of precipitation in IM, while the areas that had positive and negative effects of precipitation variation on the WUE trend accounted for 62.77% and 36.94% of the study areas (Table 2), respectively. This agrees with the conclusions by [PERSON] et al. [60]. In arid regions, changes in precipitation can lead to changes in the community structure of vegetation, dominant species and distribution [12].