Impacts of 319 wind farms on surface temperature and vegetation in the United States

Abstract

The development of wind energy is essential for decarbonizing energy supplies. However, the construction of wind farms changes land surface temperature (LST) and vegetation by modifying land surface properties and disturbing land-atmosphere interactions. In this study, we used MODIS satellite data to quantify the impacts of 319 wind farms on local climate and vegetation in the United States. Our results indicated insignificant impacts on LST during the daytime but significant warming of 0.10°C on annual mean nighttime LST averaged for all wind farms, and 0.36°C for those 61% wind farm samples with warming. The nighttime LST impacts exhibited seasonal variations, with stronger warming in winter and autumn up to 0.18°C but weaker effects in summer and spring. We observed a decrease in peak NDVI for 59% of wind farms due to infrastructure construction, with an average decrease of 0.0067 compared to non-wind-farm areas. The impacts of wind farms depended on wind farm size, with winter LST impacts for large and small wind farms ranging from 0.21°C to 0.14°C, and peak NDVI impacts ranging from -0.009 to -0.006. The LST impacts declined with the increasing distance from the wind farm, with detectable impacts up to 10 km. In contrast, the vegetation impacts on NVDI were only evident within the wind farm locations. Wind farms built in grassland and cropland showed larger warming effects but weaker vegetation impact compared to those built on forest land. Furthermore, spatial correlation analyses with environmental factors suggest limited geographical controls on the heterogeneous wind farm impacts and highlight the important role of local factors. Our analyses based on a large sample offer new observational evidence for the wind farm impacts with improved representativeness. This knowledge is important to fully understand the climatic and environmental implications of energy system decarbonization.

Localized Eco-Climatic Impacts of Onshore Wind Farms: A Review

“…In another study, the inhibition of the summer gross primary production by onshore wind farms in Hebei after their completion was 8.9%, and the annual net primary production suppression was 4% (Tang et al, 2017). Although onshore wind farms inhibit vegetation growth, studies have found that the effect of onshore wind farm impacts was mainly concentrated inside the wind farms, and the most degraded areas were inside the wind farms, which was verified by diagnostic model tests (Qin et al, 2022;Aksoy et al, 2023).…”

“…Although in situ data monitoring has the advantages of high data accuracy and distinctive features, it is limited in scale and has a major disadvantage, especially in assessing the ecological and climatic impacts of onshore wind farms in a large-scale context. In situ data monitoring can provide reliable information for the inversion of remotely sensed data, such as inversely assessing near-surface temperatures at different times (Walsh-Thomas et al, 2012; Zhou et al, 2012; Zhou et al, 2013; Harris et al, 2014; Slawsky et al, 2015; Chang et al, 2016; Liu et al, 2021b; Luo et al, 2021; Liu et al, 2022a; Ma et al, 2022; Qin et al, 2022), vegetation indexes (Li et al, 2016; Tang et al, 2017; Xia and Zhou, 2017; Wu et al, 2019; Liu et al, 2022b; Aksoy et al, 2023), landscape patterns (Zhang et al, 2016; Diffendorfer et al, 2019; Guo et al, 2020) and other indicators in order to assess the impacts of onshore wind farms on the localized eco-climate.…”

“…Studies on the timing of warming showed that the warming effect of onshore wind farms on the near-surface is concentrated at night, and is not significant during the day (Ma et al, 2022;Qin et al, 2022). A few studies have suggested that the nighttime temperature base value decreased compared to the pre-construction period (Luo et al, 2021;Liu et al, 2022a).…”

Heterogeneous warming impacts of desert wind farms on land surface temperature and their potential drivers in Northern China

“…The wake effect of wind turbines generates turbulence that spreads for kilometers downwind, the buffer should be outside the wake range. Meanwhile, the buffer should not be too far from the WF to share a similar climate background (Zhou et al 2012, Tang et al 2017, Qin et al 2022. The desert pixels are finally filtered by MCD12Q1 IGBP land cover data (figure S2), the filtered pixels are defined as wind farm pixels (WFPs) and buffer pixels (BUPs) in the following text.…”

“…However, when the ABL is unstable, with warmer air near the surface and cooler air in the upper layer, the WF impact on daytime LST is more complicated. Rotations of the wind turbine blades mix the cooler upper air and the warmer near-surface air and cool down the land surface (Miller and Keith 2018, Zhou et al 2020, Qin et al 2022, solar radiation heats the surface and creates upward convection. Besides, when the ABL is neutral, the temperatures of upper and lower air layers are approximate and the heat convection is near zero.…”