Abstract
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.