Matching Items (26)
Description
Droughts are a common phenomenon of the arid South-west USA climate. Despite water limitations, the region has been substantially transformed by agriculture and urbanization. The water requirements to support these human activities along with the projected increase in droughts intensity and frequency challenge long term sustainability and water security, thus the need to spatially and temporally characterize land use/land cover response to drought and quantify water consumption is crucial. This dissertation evaluates changes in `undisturbed' desert vegetation in response to water availability to characterize climate-driven variability. A new model coupling phenology and spectral unmixing was applied to Landsat time series (1987-2010) in order to derive fractional cover (FC) maps of annuals, perennials, and evergreen vegetation. Results show that annuals FC is controlled by short term water availability and antecedent soil moisture. Perennials FC follow wet-dry multi-year regime shifts, while evergreen is completely decoupled from short term changes in water availability. Trend analysis suggests that different processes operate at the local scale. Regionally, evergreen cover increased while perennials and annuals cover decreased. Subsequently, urban land cover was compared with its surrounding desert. A distinct signal of rain use efficiency and aridity index was documented from remote sensing and a soil-water-balance model. It was estimated that a total of 295 mm of water input is needed to sustain current greenness. Finally, an energy balance model was developed to spatio-temporally estimate evapotranspiration (ET) as a proxy for water consumption, and evaluate land use/land cover types in response to drought. Agricultural fields show an average ET of 9.3 mm/day with no significant difference between drought and wet conditions, implying similar level of water usage regardless of climatic conditions. Xeric neighborhoods show significant variability between dry and wet conditions, while mesic neighborhoods retain high ET of 400-500 mm during drought due to irrigation. Considering the potentially limited water availability, land use/land cover changes due to population increases, and the threat of a warming and drying climate, maintaining large water-consuming, irrigated landscapes challenges sustainable practices of water conservation and the need to provide amenities of this desert area for enhancing quality of life.
ContributorsKaplan, Shai (Author) / Myint, Soe Win (Thesis advisor) / Brazel, Anthony J. (Committee member) / Georgescu, Matei (Committee member) / Arizona State University (Publisher)
Created2014
Description
Gerrymandering is a central problem for many representative democracies. Formally, gerrymandering is the manipulation of spatial boundaries to provide political advantage to a particular group (Warf, 2006). The term often refers to political district design, where the boundaries of political districts are “unnaturally” manipulated by redistricting officials to generate durable advantages for one group or party. Since free and fair elections are possibly the critical part of representative democracy, it is important for this cresting tide to have scientifically validated tools. This dissertation supports a current wave of reform by developing a general inferential technique to “localize” inferential bias measures, generating a new type of district-level score. The new method relies on the statistical intuition behind jackknife methods to construct relative local indicators. I find that existing statewide indicators of partisan bias can be localized using this technique, providing an estimate of how strongly a district impacts statewide partisan bias over an entire decade. When compared to measures of shape compactness (a common gerrymandering detection statistic), I find that weirdly-shaped districts have no consistent relationship with impact in many states during the 2000 and 2010 redistricting plan. To ensure that this work is valid, I examine existing seats-votes modeling strategies and develop a novel method for constructing seats-votes curves. I find that, while the empirical structure of electoral swing shows significant spatial dependence (even in the face of spatial heterogeneity), existing seats-votes specifications are more robust than anticipated to spatial dependence. Centrally, this dissertation contributes to the much larger social aim to resist electoral manipulation: that individuals & organizations suffer no undue burden on political access from partisan gerrymandering.
ContributorsWolf, Levi (Author) / Rey, Sergio J (Thesis advisor) / Anselin, Luc (Committee member) / Fotheringham, A. Stewart (Committee member) / Tam Cho, Wendy K (Committee member) / Arizona State University (Publisher)
Created2017
Description
This work investigates the effects of non-random sampling on our understanding of species distributions and their niches. In its most general form, bias is systematic error that can obscure interpretation of analytical results by skewing samples away from the average condition of the system they represent. Here I use species distribution modelling (SDM), virtual species, and multiscale geographically weighted regression (MGWR) to explore how sampling bias can alter our perception of broad patterns of biodiversity by distorting spatial predictions of habitat, a key characteristic in biogeographic studies. I use three separate case studies to explore: 1) How methods to account for sampling bias in species distribution modeling may alter estimates of species distributions and species-environment relationships, 2) How accounting for sampling bias in fossil data may change our understanding of paleo-distributions and interpretation of niche stability through time (i.e. niche conservation), and 3) How a novel use of MGWR can account for environmental sampling bias to reveal landscape patterns of local niche differences among proximal, but non-overlapping sister taxa. Broadly, my work shows that sampling bias present in commonly used federated global biodiversity observations is more than enough to degrade model performance of spatial predictions and niche characteristics. Measures commonly used to account for this bias can negate much loss, but only in certain conditions, and did not improve the ability to correctly identify explanatory variables or recreate species-environment relationships. Paleo-distributions calibrated on biased fossil records were improved with the use of a novel method to directly estimate the biased sampling distribution, which can be generalized to finer time slices for further paleontological studies. Finally, I show how a novel coupling of SDM and MGWR can illuminate local differences in niche separation that more closely match landscape genotypic variability in the two North American desert tortoise species than does their current taxonomic delineation.
ContributorsInman, Richard (Author) / Franklin, Janet (Thesis advisor) / Fotheringham, A. Stewart (Committee member) / Dorn, Ronald (Committee member) / Arizona State University (Publisher)
Created2018
Description
In the study of regional economic growth and convergence, the distribution dynamics approach which interrogates the evolution of the cross-sectional distribution as a whole and is concerned with both the external and internal dynamics of the distribution has received wide usage. However, many methodological issues remain to be resolved before valid inferences and conclusions can be drawn from empirical research. Among them, spatial effects including spatial heterogeneity and spatial dependence invalidate the assumption of independent and identical distributions underlying the conventional maximum likelihood techniques while the availability of small samples in regional settings questions the usage of the asymptotic properties. This dissertation is comprised of three papers targeted at addressing these two issues. The first paper investigates whether the conventional regional income mobility estimators are still suitable in the presence of spatial dependence and/or a small sample. It is approached through a series of Monte Carlo experiments which require the proposal of a novel data generating process (DGP) capable of generating spatially dependent time series. The second paper moves to the statistical tests for detecting specific forms of spatial (spatiotemporal) effects in the discrete Markov chain model, investigating their robustness to the alternative spatial effect, sensitivity to discretization granularity, and properties in small sample settings. The third paper proposes discrete kernel estimators with cross-validated bandwidths as an alternative to maximum likelihood estimators in small sample settings. It is demonstrated that the performance of discrete kernel estimators offers improvement when the sample size is small. Taken together, the three papers constitute an endeavor to relax the restrictive assumptions of spatial independence and spatial homogeneity, as well as demonstrating the difference between the small sample and asymptotic properties for conventionally adopted maximum likelihood estimators towards a more valid inferential framework for the distribution dynamics approach to the study of regional economic growth and convergence.
ContributorsKang, Wei (Author) / Rey, Sergio (Thesis advisor) / Fotheringham, A. Stewart (Committee member) / Ye, Xinyue (Committee member) / Arizona State University (Publisher)
Created2018
Description
Large-scale cultivation of perennial bioenergy crops (e.g., miscanthus and switch-
grass) offers unique opportunities to mitigate climate change through avoided fossil fuel use and associated greenhouse gas reduction. Although conversion of existing agriculturally intensive lands (e.g., maize and soy) to perennial bioenergy cropping systems has been shown to reduce near-surface temperatures, unintended consequences on natural water resources via depletion of soil moisture may offset these benefits. In the effort of the cross-fertilization across the disciplines of physics-based modeling and spatio-temporal statistics, three topics are investigated in this dissertation aiming to provide a novel quantification and robust justifications of the hydroclimate impacts associated with bioenergy crop expansion. Topic 1 quantifies the hydroclimatic impacts associated with perennial bioenergy crop expansion over the contiguous United States using the Weather Research and Forecasting Model (WRF) dynamically coupled to a land surface model (LSM). A suite of continuous (2000–09) medium-range resolution (20-km grid spacing) ensemble-based simulations is conducted. Hovmöller and Taylor diagrams are utilized to evaluate simulated temperature and precipitation. In addition, Mann-Kendall modified trend tests and Sieve-bootstrap trend tests are performed to evaluate the statistical significance of trends in soil moisture differences. Finally, this research reveals potential hot spots of suitable deployment and regions to avoid. Topic 2 presents spatio-temporal Bayesian models which quantify the robustness of control simulation bias, as well as biofuel impacts, using three spatio-temporal correlation structures. A hierarchical model with spatially varying intercepts and slopes display satisfactory performance in capturing spatio-temporal associations. Simulated temperature impacts due to perennial bioenergy crop expansion are robust to physics parameterization schemes. Topic 3 further focuses on the accuracy and efficiency of spatial-temporal statistical modeling for large datasets. An ensemble of spatio-temporal eigenvector filtering algorithms (hereafter: STEF) is proposed to account for the spatio-temporal autocorrelation structure of the data while taking into account spatial confounding. Monte Carlo experiments are conducted. This method is then used to quantify the robustness of simulated hydroclimatic impacts associated with bioenergy crops to alternative physics parameterizations. Results are evaluated against those obtained from three alternative Bayesian spatio-temporal specifications.
grass) offers unique opportunities to mitigate climate change through avoided fossil fuel use and associated greenhouse gas reduction. Although conversion of existing agriculturally intensive lands (e.g., maize and soy) to perennial bioenergy cropping systems has been shown to reduce near-surface temperatures, unintended consequences on natural water resources via depletion of soil moisture may offset these benefits. In the effort of the cross-fertilization across the disciplines of physics-based modeling and spatio-temporal statistics, three topics are investigated in this dissertation aiming to provide a novel quantification and robust justifications of the hydroclimate impacts associated with bioenergy crop expansion. Topic 1 quantifies the hydroclimatic impacts associated with perennial bioenergy crop expansion over the contiguous United States using the Weather Research and Forecasting Model (WRF) dynamically coupled to a land surface model (LSM). A suite of continuous (2000–09) medium-range resolution (20-km grid spacing) ensemble-based simulations is conducted. Hovmöller and Taylor diagrams are utilized to evaluate simulated temperature and precipitation. In addition, Mann-Kendall modified trend tests and Sieve-bootstrap trend tests are performed to evaluate the statistical significance of trends in soil moisture differences. Finally, this research reveals potential hot spots of suitable deployment and regions to avoid. Topic 2 presents spatio-temporal Bayesian models which quantify the robustness of control simulation bias, as well as biofuel impacts, using three spatio-temporal correlation structures. A hierarchical model with spatially varying intercepts and slopes display satisfactory performance in capturing spatio-temporal associations. Simulated temperature impacts due to perennial bioenergy crop expansion are robust to physics parameterization schemes. Topic 3 further focuses on the accuracy and efficiency of spatial-temporal statistical modeling for large datasets. An ensemble of spatio-temporal eigenvector filtering algorithms (hereafter: STEF) is proposed to account for the spatio-temporal autocorrelation structure of the data while taking into account spatial confounding. Monte Carlo experiments are conducted. This method is then used to quantify the robustness of simulated hydroclimatic impacts associated with bioenergy crops to alternative physics parameterizations. Results are evaluated against those obtained from three alternative Bayesian spatio-temporal specifications.
ContributorsWang, Meng, Ph.D (Author) / Kamarianakis, Yiannis (Thesis advisor) / Georgescu, Matei (Thesis advisor) / Fotheringham, A. Stewart (Committee member) / Moustaoui, Mohamed (Committee member) / Reiser, Mark R. (Committee member) / Arizona State University (Publisher)
Created2018
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
This thesis examines the synoptic characteristics associated with ozone exceedance events in Arizona during the time period of 2011 to 2013. Finding explanations and sources to the ground level ozone in this state is crucial to maintaining the state’s adherence to federal air quality regulations. This analysis utilizes ambient ozone concentration data, surface meteorological conditions, upper air analyses, and HYSPLIT modeling to analyze the synoptic characteristics of ozone events. Based on these data and analyses, five categories were determined to be associated with these events. The five categories all exhibit distinct upper air patterns and surface conditions conducive to the formation of ozone, as well as distinct potential transport pathways of ozone from different nearby regions. These findings indicate that ozone events in Arizona can be linked to synoptic-scale patterns and potential regional transport of ozone. These results can be useful in the forecasting of high ozone pollution and influential on the legislative reduction of ozone pollution.
ContributorsWood, Jessica (Author) / Cerveny, Randall (Thesis advisor) / Georgescu, Matei (Committee member) / Brazel, Anthony (Committee member) / Arizona State University (Publisher)
Created2016
Description
Energy consumption in buildings, accounting for 41% of 2010 primary energy consumption in the United States (US), is particularly vulnerable to climate change due to the direct relationship between space heating/cooling and temperature. Past studies have assessed the impact of climate change on long-term mean and/or peak energy demands. However, these studies usually neglected spatial variations in the “balance point” temperature, population distribution effects, air-conditioner (AC) saturation, and the extremes at smaller spatiotemporal scales, making the implications of local-scale vulnerability incomplete. Here I develop empirical relationships between building energy consumption and temperature to explore the impact of climate change on long-term mean and extremes of energy demand, and test the sensitivity of these impacts to various factors. I find increases in summertime electricity demand exceeding 50% and decreases in wintertime non-electric energy demand of more than 40% in some states by the end of the century. The occurrence of the most extreme (appearing once-per-56-years) electricity demand increases more than 2600 fold, while the occurrence of the once per year extreme events increases more than 70 fold by the end of this century. If the changes in population and AC saturation are also accounted for, the impact of climate change on building energy demand will be exacerbated.
Using the individual building energy simulation approach, I also estimate the impact of climate change to different building types at over 900 US locations. Large increases in building energy consumption are found in the summer, especially during the daytime (e.g., >100% increase for warehouses, 5-6 pm). Large variation of impact is also found within climate zones, suggesting a potential bias when estimating climate-zone scale changes with a small number of representative locations.
As a result of climate change, the building energy expenditures increase in some states (as much as $3 billion/year) while in others, costs decline (as much as $1.4 billion/year). Integrated across the contiguous US, these variations result in a net savings of roughly $4.7 billion/year. However, this must be weighed against the cost (exceeding $19 billion) of adding electricity generation capacity in order to maintain the electricity grid’s reliability in summer.
Using the individual building energy simulation approach, I also estimate the impact of climate change to different building types at over 900 US locations. Large increases in building energy consumption are found in the summer, especially during the daytime (e.g., >100% increase for warehouses, 5-6 pm). Large variation of impact is also found within climate zones, suggesting a potential bias when estimating climate-zone scale changes with a small number of representative locations.
As a result of climate change, the building energy expenditures increase in some states (as much as $3 billion/year) while in others, costs decline (as much as $1.4 billion/year). Integrated across the contiguous US, these variations result in a net savings of roughly $4.7 billion/year. However, this must be weighed against the cost (exceeding $19 billion) of adding electricity generation capacity in order to maintain the electricity grid’s reliability in summer.
ContributorsHuang, Jianhua (Author) / Gurney, Kevin Robert (Thesis advisor) / Miller, Clark Anson (Committee member) / Rey, Sergio J (Committee member) / Georgescu, Matei (Committee member) / Arizona State University (Publisher)
Created2016
Description
Given increasing utility of numerical models to examine urban impacts on meteorology and climate, there exists an urgent need for accurate representation of seasonally and diurnally varying anthropogenic heating data, an important component of the urban energy budget for cities across the world. Incorporation of anthropogenic heating data as inputs to existing climate modeling systems has direct societal implications ranging from improved prediction of energy demand to health assessment, but such data are lacking for most cities. To address this deficiency we have applied a standardized procedure to develop a national database of seasonally and diurnally varying anthropogenic heating profiles for 61 of the largest cities in the United Stated (U.S.). Recognizing the importance of spatial scale, the anthropogenic heating database developed includes the city scale and the accompanying greater metropolitan area.
Our analysis reveals that a single profile function can adequately represent anthropogenic heating during summer but two profile functions are required in winter, one for warm climate cities and another for cold climate cities. On average, although anthropogenic heating is 40% larger in winter than summer, the electricity sector contribution peaks during summer and is smallest in winter. Because such data are similarly required for international cities where urban climate assessments are also ongoing, we have made a simple adjustment accounting for different international energy consumption rates relative to the U.S. to generate seasonally and diurnally varying anthropogenic heating profiles for a range of global cities. The methodological approach presented here is flexible and straightforwardly applicable to cities not modeled because of presently unavailable data. Because of the anticipated increase in global urban populations for many decades to come, characterizing this fundamental aspect of the urban environment – anthropogenic heating – is an essential element toward continued progress in urban climate assessment.
ContributorsSailor, David (Author) / Georgescu, Matei (Author) / Milne, Jeffrey M. (Author) / Hart, Melissa A. (Author)
Created2015-07-17