A Case Study of Land-Surface-Temperature Impact from Large-Scale Deployment of Wind Farms in China from Guazhou

Chang, Rui; Zhu, Rong; Guo, Peng

doi:10.3390/rs8100790

Cited by 25 publications

(23 citation statements)

References 24 publications

(60 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…At pixel levels, MODIS VIs exhibit spatial variability that is mostly related to the variations in topography and land cover types [8][9][10] Here, we use three methods to detect and attribute the WF impacts on vegetation activity. The first two methods, spatial coupling analysis and time series analysis, are proposed by Zhou et al [8][9][10] and followed by Harris et al [11], Slawsky et al [12], Xia et al [13], Chang et al [14], and Tang et al [27]. These methods have successfully detected WF impacts on the land surface temperature (LST) over different WF regions.…”

Section: Detection and Attribution Methodsmentioning

confidence: 99%

“…The above detection and attribution approaches have been used successfully in previous studies and proven to be very effective in identifying WFs impacts on LST using MODIS data [8][9][10][11][12][13][14]. It is difficult to believe that these approaches work well for LST but not for VIs using measurements from the same satellites.…”

Section: Uncertainties In Detection and Attributionmentioning

confidence: 99%

See 1 more Smart Citation

Detecting Wind Farm Impacts on Local Vegetation Growth in Texas and Illinois Using MODIS Vegetation Greenness Measurements

Xia

Zhou

2017

Remote Sensing

View full text Add to dashboard Cite

This study examines the possible impacts of real-world wind farms (WFs) on vegetation growth using two vegetation indices (VIs), the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI), at a~250 m resolution from the MODerate resolution Imaging Spectroradimeter (MODIS) for the period 2003-2014. We focus on two well-studied large WF regions, one in western Texas and the other in northern Illinois. These two regions differ distinctively in terms of land cover, topography, and background climate, allowing us to examine whether the WF impacts on vegetation, if any, vary due to the differences in atmospheric and boundary conditions. We use three methods (spatial coupling analysis, time series analysis, and seasonal cycle analysis) and consider two groups of pixels, wind farm pixels (WFPs) and non-wind-farm pixels (NWFPs), to quantify and attribute such impacts during the pre-and post-turbine periods. Our results indicate that the WFs have insignificant or no detectible impacts on local vegetation growth. At the pixel level, the VI changes demonstrate a random nature and have no spatial coupling with the WF layout. At the regional level, there is no systematic shift in vegetation greenness between the pre-and post-turbine periods. At interannual and seasonal time scales, there are no confident vegetation changes over WFPs relative to NWFPs. These results remain robust when the pre-and post-turbine periods and NWFPs are defined differently. Most importantly, the majority of the VI changes are within the MODIS data uncertainty, suggesting that the WF impacts on vegetation, if any, cannot be separated confidently from the data uncertainty and noise. Overall, there are some small decreases in vegetation greenness over WF regions, but no convincing observational evidence is found for the impacts of operating WFs on vegetation growth.

show abstract

Section: Detection and Attribution Methodsmentioning

confidence: 99%

Section: Uncertainties In Detection and Attributionmentioning

confidence: 99%

Detecting Wind Farm Impacts on Local Vegetation Growth in Texas and Illinois Using MODIS Vegetation Greenness Measurements

Xia

Zhou

2017

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…Previous studies [3,50,51] have documented that large wind farms on land impact the atmospheric conditions such that increased land surface temperatures (LST) are observed in stable stratification, typically during nighttime. This has been explained from downward mixing of warm air from aloft.…”

Section: Discussionmentioning

confidence: 99%

“…Thus from the perspective of the SST retrieval accuracy it would be feasible to quantify wake-induced warming effects offshore. In fact, only the relative difference within the wake influenced regions versus neighboring non-disturbed regions would need to be compared as in the land-based studies [3,50,51]. However, due to the higher heat capacity of water and the continuous mixing by wind and ocean currents, such downward mixing of warmer air would need to be persistent in order to have a significant effect, and be measurable.…”

Section: Discussionmentioning

confidence: 99%

Wind Farm Wake: The 2016 Horns Rev Photo Case

et al. 2017

View full text Add to dashboard Cite

Offshore wind farm wakes were observed and photographed in foggy conditions at Horns Rev 2 on 25 January 2016 at 12:45 UTC. These new images show highly contrasting conditions regarding the wind speed, turbulence intensity, atmospheric stability, weather conditions and wind farm wake development as compared to the Horns Rev 1 photographs from 12 February 2008. The paper examines the atmospheric conditions from satellite images, radiosondes, lidar and wind turbine data and compares the observations to results from atmospheric meso-scale modelling and large eddy simulation. Key findings are that a humid and warm air mass was advected from the southwest over cold sea and the dew-point temperature was such that cold-water advection fog formed in a shallow layer. The flow was stably stratified and the freestream wind speed was 13 m/s at hub height, which means that most turbines produced at or near rated power. The wind direction was southwesterly and long, narrow wakes persisted several rotor diameters downwind of the wind turbines. Eventually mixing of warm air from aloft dispersed the fog in the far wake region of the wind farm.

show abstract

“…The nocturnal atmospheric boundary layer is usually more stable and thin in the nighttime, produce a stronger warming effect; wind speed is much high in summer, thus the turbine generate more turbulence, and leading to a stronger warming effect. Chang et al [11] Large wind farms in Guazhou China…”

Section: Study On the Influence Of Wind Farm By Remote Sensing Technomentioning

confidence: 99%

Research Progress on the Impact of Wind Farms on Regional and Global Climate

Gong¹,

Bao²,

Li³

2017

Proceedings of the 2017 2nd International Conference on Civil, Transportation and Environmental Engineering (ICCTE 2017)

View full text Add to dashboard Cite

Abstract. The influence of wind energy on the energy cycle and atmospheric circulation in nature and the possible impact on the climate are the hotspots in recent years. The comprehensive analysis of domestic and foreign research shows that the wind farms have a certain impact on regional and global precipitation, temperature, humidity, evaporation, wind speed and other climatic factors, but the conclusions varies from different methods and theoretical. Because of the limitation of the existing observations, simulation results of climate models also have many uncertainties, researchers should continuously improve the observation method and the optimization of climate model, improve the relevant theory, so as to obtain more accurate conclusions.

show abstract

A Case Study of Land-Surface-Temperature Impact from Large-Scale Deployment of Wind Farms in China from Guazhou

Cited by 25 publications

References 24 publications

Detecting Wind Farm Impacts on Local Vegetation Growth in Texas and Illinois Using MODIS Vegetation Greenness Measurements

Detecting Wind Farm Impacts on Local Vegetation Growth in Texas and Illinois Using MODIS Vegetation Greenness Measurements

Wind Farm Wake: The 2016 Horns Rev Photo Case

Research Progress on the Impact of Wind Farms on Regional and Global Climate

Contact Info

Product

Resources

About