# The Observed Impacts of Wind Farms on Local Vegetation Growth in Northern China
## 4 Discussion  
In this paper, MODIS vegetation index and productivity data are used to analyze the effects of WFs on vegetation growth in the Bashang area in Northern China. The results show that the WFs have a significant inhibiting effect on vegetation growth and productivity both spatially and temporally. The potential processes, such as land-use change, differences in vegetation type, changes in climate, and wind speed and direction, may contribute to the inhibiting effect of vegetation growth. Construction of WFs and other human activities change the land cover type, thus affecting the vegetation growth in the study area. In this research, Finer Resolution Observation and Monitoring of Global Land Cover (FROM-GLC) data for two periods in 2000 and 2010 are used to analyze land use changes in WFPs and NNWFPs in the study area. The spatial resolution of the data is 30 m [(30)]. The results show that the proportion of non-vegetation pixels (water and urban areas) in WFFs changes from 0.72% to 1.25%, while from 2000 to 2010, that in the NNWFPs changes from 2.50% to 3.48% (Figure S1). The proportions of the non-vegetation pixels and their changes in WFPs and NNWFPs are very small. Although the construction of wind turbines may indeed damage the vegetation canopy, the annual plants (grassland and crop) in the study area are able to recover quickly after being damaged in the next year (Figure S4). With the increase of the effect of WFs on daytime LST, vegetation growth cannot be restored to the level before the construction, or even continue to decrease, indicating that WFs do affect vegetation growth by changing the vegetation growth environment (Figure S4). The difference in land cover types, such as different crop species and crop management, may also have potential influences on vegetation growth. For two main vegetation types in the WFPs and NNWFPs (crop and grass), the effects of the WFs are approximately the same (Figure S5). It is reasonable to assume that WFs have a similar effect on different crop species. The effect of crop species differences and crop managements would be much smaller than the strong and persistent signal of wind farm impacts. According to field campaigns, the support column of a wind turbine accounts for 4 m\({}^{2}\), and 1747 wind turbines account for approximately 7000 m\({}^{2}\), which represent a very small proportion (less than 0.1%) of the WFPs. Therefore, for the study area, land cover changes and differences in vegetation type for WFPs and NNWFPs have no significant effect on the underlying vegetation growth. The changes in temperature may play an important role in the effect of WFs on vegetation growth. There is a warming effect of 0.15-0.18 \({}^{\circ}\)C coupled with WFs at night, which agrees with most existing studies (Figure 6b) [(12; 13; 15)]. The possible reason for this phenomenon is that the diurnal and seasonal variations in wind speed and the changes in near-surface atmospheric boundary layer (ABL) conditions due to wind turbines operations [(9)]. Contrary to some existing studies, this research finds that there is also a warming effect of 0.45-0.65 \({}^{\circ}\)C coupled with WFs during the daytime (Figure 6a). [PERSON] et al. [(9)]  
Figure 5: Inter-annual variations of area mean (**a**) vegetation index differences and (**b**) GPP and NPP between WFPs and NNWFPs (WFPs minus NNWFPs) for the period 2003–2014. The insert numbers are the area mean VI, GPP, and NPP differences between the post- and pre-turbine construction periods (2012–2014 minus 2003–2005). WFPs and NNWFPs are defined as in Figure 2.  
speculated that the possible reason is the slow development of the daytime unstable and convective layer [31]. Changes in ET may also play a role in daytime warming [32]. The ET in the WFPs is lower than that in the NNWFPs, and reduced transpiration may contribute to a higher LST in WFPs (Figure S6). In the study area, the LAI is negatively related to the daytime LST, and the correlations are all significant at a confidence level of 95% (Figure S7).