Matching Items (4)
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- All Subjects: Climate
- Creators: Georgescu, Matei
- Creators: Albuquerque, Fabio Suzart
Description
The Great Plains region of the central United States and southern Canada is a promising location for wind energy resource development. Wind energy site assessments and forecasts can benefit from better understanding the variability that may result from several teleconnections affecting North America. This thesis investigates how the El Niño/Southern Oscillation (ENSO), the North Atlantic Oscillation (NAO), and the Pacific/North American Pattern (PNA) impact mean monthly wind speeds at 850 hPa over the Great Plains. Using wind speeds from the NCAR/NCEP Reanalysis 1, correlations were computed between the mean monthly wind speeds and average monthly teleconnection index values. A difference of means test was used to compute the change in wind speeds between the positive and negative phases of each index. ENSO was not found to have a significant impact on wind speeds, while the NAO and PNA patterns weakly affected wind speeds. The NAO index was positively (negatively) correlated with wind speeds over the northern (southern) plains, while the PNA index was negatively correlated with wind speeds over most of the plains. Even a small change in wind speed can have a large effect on the potential power output, so the effects of these teleconnections should be considered in wind resource assessments and climatologies.
ContributorsOrdonez, Ana Cristina (Author) / Cerveny, Randall (Thesis director) / Svoma, Bohumil (Committee member) / Balling, Robert (Committee member) / Barrett, The Honors College (Contributor) / School of Geographical Sciences and Urban Planning (Contributor)
Created2013-05
Description
Urban areas produce an urban heat island (UHI), which is manifest as warmer temperatures compared to the surrounding and less developed areas. While it is understood that UHI's are warmer than their surrounding areas, attributing the amount of heat added by the urban area is not easily determined. Current generation modeling systems require diurnal anthropogenic heating profiles. Development of diurnal cycle profiles of anthropogenic heating will help the modeling community as there is currently no database for anthropogenic heating profiles for cities across the United States. With more accurate anthropogenic heating profiles, climate models will be better able to show how humans directly impact the urban climate. This research attempts to create anthropogenic heating profiles for 61 cities in the United States. The method used climate, electricity, natural gas, and transportation data to develop anthropogenic heating profiles for each state. To develop anthropogenic heating profiles, profiles are developed for buildings, transportation, and human metabolism using the most recently available data. Since utilities are reluctant to release data, the building energy profile is developed using statewide electricity by creating a linear regression between the climate and electricity usage. A similar method is used to determine the contribution of natural gas consumption. These profiles are developed for each month of the year, so annual changes in anthropogenic heating can be seen. These profiles can then be put into climate models to enable more accurate urban climate modeling.
ContributorsMilne, Jeffrey (Author) / Georgescu, Matei (Thesis director) / Sailor, David (Committee member) / Brazel, Anthony (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / School of Geographical Sciences and Urban Planning (Contributor)
Created2014-05
Description
Urbanization, a direct consequence of land use and land cover change, is responsible for significant modification of local to regional scale climates. It is projected that the greatest urban growth of this century will occur in urban areas in the developing world. In addition, there is a significant research gap in emerging nations concerning this topic. Thus, this research focuses on the assessment of climate impacts related to urbanization on the largest metropolitan area in Latin America: Mexico City.
Numerical simulations using a state-of-the-science regional climate model are utilized to address a trio of scientifically relevant questions with wide global applicability. The importance of an accurate representation of land use and land cover is first demonstrated through comparison of numerical simulations against observations. Second, the simulated effect of anthropogenic heating is quantified. Lastly, numerical simulations are performed using pre-historic scenarios of land use and land cover to examine and quantify the impact of Mexico City's urban expansion and changes in surface water features on its regional climate.
Numerical simulations using a state-of-the-science regional climate model are utilized to address a trio of scientifically relevant questions with wide global applicability. The importance of an accurate representation of land use and land cover is first demonstrated through comparison of numerical simulations against observations. Second, the simulated effect of anthropogenic heating is quantified. Lastly, numerical simulations are performed using pre-historic scenarios of land use and land cover to examine and quantify the impact of Mexico City's urban expansion and changes in surface water features on its regional climate.
ContributorsBenson-Lira, Valeria (Author) / Georgescu, Matei (Thesis advisor) / Brazel, Anthony (Committee member) / Vivoni, Enrique (Committee member) / Arizona State University (Publisher)
Created2015
Description
Although many studies have identified environmental factors as primary drivers of bird richness and abundance, there is still uncertainty about the extent to which climate, topography and vegetation influence richness and abundance patterns seen in local extents of the northern Sonoran Desert. I investigated how bird richness and abundance differed between years and seasons and which environmental variables most influenced the patterns of richness and abundance in the Greater Phoenix Metropolitan Area.
I compiled a geodatabase of climate, bioclimatic (interactions between precipitation and temperature), vegetation, soil, and topographical variables that are known to influence both richness and abundance and used 15 years of bird point count survey data from urban and non-urban sites established by Central Arizona–Phoenix Long-Term Ecological Research project to test that relationship. I built generalized linear models (GLM) to elucidate the influence of each environmental variable on richness and abundance values taken from 47 sites. I used principal component analysis (PCA) to reduce 43 environmental variables to 9 synthetic factors influenced by measures of vegetation, climate, topography, and energy. I also used the PCA to identify uncorrelated raw variables and modeled bird richness and abundance with these uncorrelated environmental variables (EV) with GLM.
I found that bird richness and abundance were significantly different between seasons, but that richness and winter abundance were not significantly different across years. Bird richness was most influenced by soil characteristics and vegetation while abundance was most influenced by vegetation and climate. Models using EV as independent variables consistently outperformed those models using synthetically produced components from PCA. The results suggest that richness and abundance are both driven by climate and aspects of vegetation that may also be influenced by climate such as total annual precipitation and average temperature of the warmest quarter. Annual oscillations of bird richness and abundance throughout the urban Phoenix area seem to be strongly associated with climate and vegetation.
I compiled a geodatabase of climate, bioclimatic (interactions between precipitation and temperature), vegetation, soil, and topographical variables that are known to influence both richness and abundance and used 15 years of bird point count survey data from urban and non-urban sites established by Central Arizona–Phoenix Long-Term Ecological Research project to test that relationship. I built generalized linear models (GLM) to elucidate the influence of each environmental variable on richness and abundance values taken from 47 sites. I used principal component analysis (PCA) to reduce 43 environmental variables to 9 synthetic factors influenced by measures of vegetation, climate, topography, and energy. I also used the PCA to identify uncorrelated raw variables and modeled bird richness and abundance with these uncorrelated environmental variables (EV) with GLM.
I found that bird richness and abundance were significantly different between seasons, but that richness and winter abundance were not significantly different across years. Bird richness was most influenced by soil characteristics and vegetation while abundance was most influenced by vegetation and climate. Models using EV as independent variables consistently outperformed those models using synthetically produced components from PCA. The results suggest that richness and abundance are both driven by climate and aspects of vegetation that may also be influenced by climate such as total annual precipitation and average temperature of the warmest quarter. Annual oscillations of bird richness and abundance throughout the urban Phoenix area seem to be strongly associated with climate and vegetation.
ContributorsBoehme, Cameron (Author) / Albuquerque, Fabio Suzart (Thesis advisor) / Bateman, Heather L (Committee member) / Saul, Steven E (Committee member) / Arizona State University (Publisher)
Created2019