Wind Farms Impacts on Land Surface Temperature and Its Driving Factors in an Arid Area of Xinjiang, China

Wind energy is vital for clean energy development in ecologically fragile arid regions. This study presents the first comprehensive analysis of wind farm impacts on land surface temperature (LST) in the extremely arid area of Xinjiang, China, using MODIS (2008–2022) and Landsat data. Key findings include (1) pronounced nighttime warming in winter (up to 1.548 °C/15a) in densely turbine-populated areas, contrasting with autumn cooling; (2) Random Forest regression identifying wind speed, precipitation, NDVI, and snow cover as key drivers of LST changes; (3) enhanced post-construction warming, especially in summer nights in the Southeast Wind Zone; (4) significant thermal effects confirmed against non-affected areas, showing diurnal asymmetry and downwind warming; and (5) a distance–decay pattern of LST anomalies, strongest within 2–5 km of turbines yet detectable up to 20 km. These results reveal a unique spatial–seasonal complexity in LST changes induced by wind farms in arid regions, emphasizing the critical roles of topography and turbine density. This study underscores the necessity of integrating microclimate feedbacks into sustainable wind energy planning in arid environments.

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Publication Details

Journal
Sustainability
Published
2025-10-24
DOI
https://doi.org/10.3390/su17219445
Primary Topic
Wind Energy Research and Development
Type
article
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article

Wind Farms Impacts on Land Surface Temperature and Its Driving Factors in an Arid Area of Xinjiang, China

Hongnan Jiang, Xu Li, Wei Cui, Mi Tian et al.
Sustainability
Wind Energy Research and Development
article

Wind Farms Impacts on Land Surface Temperature and Its Driving Factors in an Arid Area of Xinjiang, China

Hongnan Jiang, Xu Li, Wei Cui, Mi Tian, Mengyu Xie, Doudou Hao
article en

Abstract

Wind energy is vital for clean energy development in ecologically fragile arid regions. This study presents the first comprehensive analysis of wind farm impacts on land surface temperature (LST) in the extremely arid area of Xinjiang, China, using MODIS (2008–2022) and Landsat data. Key findings include (1) pronounced nighttime warming in winter (up to 1.548 °C/15a) in densely turbine-populated areas, contrasting with autumn cooling; (2) Random Forest regression identifying wind speed, precipitation, NDVI, and snow cover as key drivers of LST changes; (3) enhanced post-construction warming, especially in summer nights in the Southeast Wind Zone; (4) significant thermal effects confirmed against non-affected areas, showing diurnal asymmetry and downwind warming; and (5) a distance–decay pattern of LST anomalies, strongest within 2–5 km of turbines yet detectable up to 20 km. These results reveal a unique spatial–seasonal complexity in LST changes induced by wind farms in arid regions, emphasizing the critical roles of topography and turbine density. This study underscores the necessity of integrating microclimate feedbacks into sustainable wind energy planning in arid environments.

SustainabilityVol. 17(21)
Xinjiang University (CN)
Openalex Percentile: Top 58%
Wind Energy Research and Development
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