Remotely sensed evidence of the divergent climate impacts of wind farms on croplands and grasslands

Naijing Liu; Xiang Zhao; Xin Zhang; Jiacheng Zhao; Haoyu Wang; Donghai Wu

doi:10.1016/j.scitotenv.2023.167203

Remotely sensed evidence of the divergent climate impacts of wind farms on croplands and grasslands

Sci Total Environ. 2023 Dec 20:905:167203. doi: 10.1016/j.scitotenv.2023.167203. Epub 2023 Sep 18.

Authors

Naijing Liu¹, Xiang Zhao², Xin Zhang³, Jiacheng Zhao⁴, Haoyu Wang⁵, Donghai Wu⁶

Affiliations

¹ State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China; Beijing Engineering Research Center for Global Land Remote Sensing Products, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China; POWERCHINA Northwest Engineering Corporation Limited, Xi'an, Shaanxi 710065, China.
² State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China; Beijing Engineering Research Center for Global Land Remote Sensing Products, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China. Electronic address: zhaoxiang@bnu.edu.cn.
³ School of Computing, Mathematics and Digital Technology, Manchester Metropolitan University, Manchester M1 5GD, UK.
⁴ State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China; Beijing Engineering Research Center for Global Land Remote Sensing Products, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China.
⁵ School of Earth and Space Sciences, Peking University, Beijing 100871, China.
⁶ Key Laboratory of Vegetation Restoration and Management of Degraded Ecosystems, Guangdong Provincial Key Laboratory of Applied Botany, South China Botanical Garden, Chinese Academy of Sciences, Guangzhou 510650, China. Electronic address: donghaiwu@scbg.ac.cn.

PMID: 37730031
DOI: 10.1016/j.scitotenv.2023.167203

Abstract

To mitigate climate change, the utilization of wind energy has rapidly expanded over the last two decades. However, when producing clean electricity, wind farms (WFs) may in turn alter the local climate by interfering in land surface-atmosphere interactions. Currently, China and the United States have the highest wind energy capacities globally. Thus, quantitatively analyzing the impacts of WFs on land surface temperature (LST) between the two countries is valuable to deeply understand the climate impact of WF. In this study, we use the moderate-resolution imaging spectroradiometer (MODIS) time series from 2001 to 2018 to reveal the impacts of 186 WFs (76 in China and 110 in the US) on local LSTs. The remote sensing observations reveal that WFs generally lead to warming impacts in both countries, with stronger effects in the US compared to China. During the daytime, WFs in the US exhibit a significant warming effect of 0.08 °C (p < 0.05), while the impact in China is nonsignificant (0.06 °C, p = 0.15). At night, the warming impacts in the US are approximately 1.7 times greater than in China (0.19 °C vs. 0.11 °C). Differences in the LST impacts between the two countries are primarily driven by cropland WFs, which cause more significant cooling effects in China (-0.34 °C in the daytime and - 0.19 °C at night, p < 0.01) compared to the US. However, these differences are not significant for grassland WFs. Moreover, the impacts of WFs on croplands' LSTs are strongly correlated with their evapotranspiration impacts, likely influenced by irrigation practices. In addition to evapotranspiration, a machine learning model suggests that background climate and terrain factors can alter the LST impacts. Our observations in the two largest WF-deployment countries provide a new understanding of the climate impacts of WFs, which should be considered in the fields of wind and renewable energy deployment.

Keywords: Cropland; Grassland; Heterogeneity; Land surface temperature; Remote sensing; Wind farm.