東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Understanding Large Wind Farm Impact...

Xia, Geng.

Linked to FindBook

Google Book

Amazon

博客來

Understanding Large Wind Farm Impacts on Regional Climate and Vegetation Growth from Observational and Modeling Perspectives.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Understanding Large Wind Farm Impacts on Regional Climate and Vegetation Growth from Observational and Modeling Perspectives./
Author:	Xia, Geng.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2018,
Description:	215 p.
Notes:	Source: Dissertations Abstracts International, Volume: 79-10, Section: B.
Contained By:	Dissertations Abstracts International79-10B.
Subject:	Alternative Energy. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10746181
ISBN:	9780355787573

Understanding Large Wind Farm Impacts on Regional Climate and Vegetation Growth from Observational and Modeling Perspectives.
Xia, Geng.

Understanding Large Wind Farm Impacts on Regional Climate and Vegetation Growth from Observational and Modeling Perspectives. - Ann Arbor : ProQuest Dissertations & Theses, 2018 - 215 p.

Source: Dissertations Abstracts International, Volume: 79-10, Section: B.

Thesis (Ph.D.)--State University of New York at Albany, 2018.

This item must not be sold to any third party vendors.

In the most recent decade, wind energy has experienced exponential growth worldwide and this rapid increase is expected to continue, particularly over farmlands in the United States. This poses an important question regarding whether the widespread deployment of wind turbines (WTs) will influence surface/near-surface microclimate and vegetation growth. In this dissertation, I investigate the potential wind farm (WF) impacts on regional climate and vegetation growth from both observational and modeling perspectives. High resolution satellite, radiosonde and field observations are used to determine the magnitude and variability of WF-induced changes on surface/near-surface temperatures while the Weather Research and Forecasting (WRF) model is used to simulate these changes in real-world WFs at regional scales and to uncover the physical processes behind the simulated temperature changes. First, the primary physical mechanisms controlling the seasonal and diurnal variations of WF impacts on land surface temperature (LST) are investigated by analyzing both satellite data and field observations. It is found that the turbine-induced turbulent kinetic energy (TKE) relative to the background TKE determines the magnitude and variability of such impacts. In addition, atmospheric stability also matters in determining the sign and strength of the net downward heat transport as well as the magnitude of the background TKE. Second, the WRF's ability in simulating the observed WF impacts on LST is examined by conducting real-world WF experiments driven by realistic initial and boundary conditions. Overall, the WRF model can moderately reproduce the observed spatiotemporal variations of the background LST but has difficulties in reproducing such variations for the turbine-induced LST change signals at pixel levels. However, the model is still able to reproduce the coherent and consistent responses of the observed WF-induced LST changes at regional scales. Third, the spatiotemporal characteristics of the simulated temperature changes as well as the relevant physical processes responsible for such changes are further investigated using the WRF model. It is found that (i) the WF-induced sensible heat flux change is the dominant surface forcing responsible for the simulated temperature changes; (ii) the WF-induced temperature changes are not only restricted at the surface but also can extend vertically to the hub-height level and horizontally spread 60 km in the downwind direction; (iii) the vertical divergence of heat flux from the planetary boundary layer scheme and the resolved temperature advection are the two most likely physical processes behind the simulated temperature changes. Finally, the possible WF impacts on vegetation growth are also investigated using high resolution (~250m) satellite derived vegetation indices (VI) over two well-studied large WF regions. Results indicate that the WFs have insignificant or no detectable 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 wind farm pixels relative to non-wind farm pixels. Most importantly, the majority of the VI changes are within the data uncertainty, suggesting that the WF impacts on vegetation, if any, cannot be separated confidently from the data noise.

ISBN: 9780355787573Subjects--Topical Terms:

1035473
Alternative Energy.

Understanding Large Wind Farm Impacts on Regional Climate and Vegetation Growth from Observational and Modeling Perspectives.
LDR:04747nmm a2200349 4500 001 2210288
005 20191121124158.5
008 201008s2018 ||||||||||||||||| ||eng d
020 $a 9780355787573
035 $a (MiAaPQ)AAI10746181
035 $a (MiAaPQ)sunyalb:12310
035 $a AAI10746181
040 $a MiAaPQ $c MiAaPQ
100 1 $a Xia, Geng. $3 3437430
245 1 0 $a Understanding Large Wind Farm Impacts on Regional Climate and Vegetation Growth from Observational and Modeling Perspectives.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2018
300 $a 215 p.
500 $a Source: Dissertations Abstracts International, Volume: 79-10, Section: B.
500 $a Publisher info.: Dissertation/Thesis.
500 $a Advisor: Zhou, Liming.
502 $a Thesis (Ph.D.)--State University of New York at Albany, 2018.
506 $a This item must not be sold to any third party vendors.
520 $a In the most recent decade, wind energy has experienced exponential growth worldwide and this rapid increase is expected to continue, particularly over farmlands in the United States. This poses an important question regarding whether the widespread deployment of wind turbines (WTs) will influence surface/near-surface microclimate and vegetation growth. In this dissertation, I investigate the potential wind farm (WF) impacts on regional climate and vegetation growth from both observational and modeling perspectives. High resolution satellite, radiosonde and field observations are used to determine the magnitude and variability of WF-induced changes on surface/near-surface temperatures while the Weather Research and Forecasting (WRF) model is used to simulate these changes in real-world WFs at regional scales and to uncover the physical processes behind the simulated temperature changes. First, the primary physical mechanisms controlling the seasonal and diurnal variations of WF impacts on land surface temperature (LST) are investigated by analyzing both satellite data and field observations. It is found that the turbine-induced turbulent kinetic energy (TKE) relative to the background TKE determines the magnitude and variability of such impacts. In addition, atmospheric stability also matters in determining the sign and strength of the net downward heat transport as well as the magnitude of the background TKE. Second, the WRF's ability in simulating the observed WF impacts on LST is examined by conducting real-world WF experiments driven by realistic initial and boundary conditions. Overall, the WRF model can moderately reproduce the observed spatiotemporal variations of the background LST but has difficulties in reproducing such variations for the turbine-induced LST change signals at pixel levels. However, the model is still able to reproduce the coherent and consistent responses of the observed WF-induced LST changes at regional scales. Third, the spatiotemporal characteristics of the simulated temperature changes as well as the relevant physical processes responsible for such changes are further investigated using the WRF model. It is found that (i) the WF-induced sensible heat flux change is the dominant surface forcing responsible for the simulated temperature changes; (ii) the WF-induced temperature changes are not only restricted at the surface but also can extend vertically to the hub-height level and horizontally spread 60 km in the downwind direction; (iii) the vertical divergence of heat flux from the planetary boundary layer scheme and the resolved temperature advection are the two most likely physical processes behind the simulated temperature changes. Finally, the possible WF impacts on vegetation growth are also investigated using high resolution (~250m) satellite derived vegetation indices (VI) over two well-studied large WF regions. Results indicate that the WFs have insignificant or no detectable 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 wind farm pixels relative to non-wind farm pixels. Most importantly, the majority of the VI changes are within the data uncertainty, suggesting that the WF impacts on vegetation, if any, cannot be separated confidently from the data noise.
590 $a School code: 0668.
650 4 $a Alternative Energy. $3 1035473
650 4 $a Atmospheric sciences. $3 3168354
650 4 $a Environmental science. $3 677245
650 4 $a Energy. $3 876794
690 $a 0363
690 $a 0725
690 $a 0768
690 $a 0791
710 2 $a State University of New York at Albany. $b Atmospheric and Environmental Sciences. $3 3193090
773 0 $t Dissertations Abstracts International $g 79-10B.
790 $a 0668
791 $a Ph.D.
792 $a 2018
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10746181