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- Creators: Arizona State University
- Status: Published
Description
The southwestern United States is an ecologically, climatologically, and topographically diverse geographical region. As a result, it has been difficult to develop accurate assessments and instructional pedagogy for defining and demonstrating climate sensitivity and change at a more local level. To address this problem, this dissertation is divided into two distinct sections involving climate data collection/analysis and geography education using interactive geovisualization video games (iGEOs). The first two papers analyze new climate observations in Joshua Tree National Park. The first paper examines the variability in accuracy of climate reanalysis and interpolation methods compared to field observations in Joshua Tree National Park and the Tucson Metropolitan Area. This study found that other than PRISM interpolation data, reanalysis techniques performed better in a region with a more extensive climate network. The second paper developed a climate regionalization zone separating the Mojave and Sonoran Deserts within Joshua Tree National Park using principal component analysis. This study used monthly temperature and precipitation observations, as well as seasonal climate trends. The final two papers describe and analyze the implementation of virtual interactive geovisualization video games (iGEOs) used to instruct geographical concepts in an introductory physical geography course at Arizona State University. The first paper examines the preliminary implementation of an iGEO in the San Francisco Peaks of northern Arizona, identifying student support for the games, but with caveats related to the technical shortcomings of the game design, and noticeable differences based on academic major. The second paper examines the changing experiences and challenges encountered by both students and instructors in an iGEO centered introductory geography course during the COVID-19 pandemic. This study found that, while students were impacted by the pandemic, all student groups had sufficient extensive and intensive learning materials to ensure a positive and successful lab experience. Overall, the significance of these four papers demonstrates that new applications of climate observations and geography pedagogy can effectively describe local climate sensitivity and instruct geographic concepts in the mountainous Southwest.
ContributorsHeintzman, Ryan Joseph (Author) / Cerveny, Randall S (Thesis advisor) / Dorn, Ronald I (Thesis advisor) / Balling Jr, Robert C (Committee member) / Selover, Nancy (Committee member) / Arizona State University (Publisher)
Created2021
Description
Farmer-Managed Irrigation Systems (FMIS) are community managed and operated irrigation systems, celebrated for their successful governance of water resources for many decades and in several countries. Nevertheless, in the face of climatic, political, and social change, their future is uncertain. This dissertation utilizes household adaptive capacity, socio-ecological system (SES) robustness, and the Institutional Analysis and Development framework to structure a multi-scalar and multi-stressor analysis of changes experienced in Nepal’s FMIS. The dissertation documents irrigators’ perception of environmental change, impacts, and response; diagnoses the multiple disturbances impacting the robustness of the FMIS; and analyzes changes in the FMIS as an institution over time, in an effort to understand the major drivers of SES change. Fifteen FMIS from five districts of Nepal were selected for the study. Data were collected through field observations, household surveys, key informant interviews, and focus group discussions. The status of the existing rules was compared with the data collected three decades ago in the Nepal Irrigation Institutions and Systems database. This study finds that FMIS’s robustness is threatened by uncertain water supply, inefficient infrastructure, scarcity of farm labor, weakening collective action, and natural disasters. Farm households, whose actions are necessary to sustain the management of FMIS, perceive environmental change differently according to their ecological region and position along the irrigation canal, leading to different adaptation strategies. Despite livelihood diversification, irrigators rely primarily on irrigation infrastructure management to respond to the impacts of environmental change. Institutional analysis demonstrates the evolution of FMIS in terms of working rules in the face of multiple stressors. In this study, payoff, information, and position rules have exhibited the most substantive change. However, boundary, choice, aggregation, and scope rules are less likely to change. The findings of this work point to the need for geographically differentiated adaptation support policies, and a need for closer attention to the dynamics of labor, environmental change, and institutional persistence in agriculture and irrigation sectors. FMIS being an exemplary institutional arrangement for the study of a SES, the research findings benefit similar institutions globally facing challenges to the sustainable governance of common pool resources.
ContributorsParajuli, Jagadish (Author) / Eakin, Hallie (Thesis advisor) / Chhetri, Netra (Thesis advisor) / Anderies, Marty (Committee member) / Arizona State University (Publisher)
Created2021
Description
Crystalline silicon covers more than 85% of the global photovoltaics industry and has sustained a nearly 30% year-over-year growth rate. Continued cost and capital expenditure (CAPEX) reductions are needed to sustain this growth. Using thin silicon wafers well below the current industry standard of 160 µm can reduce manufacturing cost, CAPEX, and levelized cost of electricity. Additionally, thinner wafers enable more flexible and lighter module designs, making them more compelling in market segments like building-integrated photovoltaics, portable power, aerospace, and automotive industries. Advanced architectures and superior surface passivation schemes are needed to enable the use of very thin silicon wafers. Silicon heterojunction (SHJ) and SHJ with interdigitated back contact solar cells have demonstrated open-circuit voltages surpassing 720 mV and the potential to surpass 25% conversion efficiency. These factors have led to an increasing interest in exploring SHJ solar cells on thin wafers. In this work, the passivation capability of the thin intrinsic hydrogenated amorphous silicon layer is improved by controlling the deposition temperature and the silane-to-hydrogen dilution ratio. An effective way to parametrize surface recombination is by using surface saturation current density and a very low surface saturation density is achieved on textured wafers for wafer thicknesses ranging between 40 and 180 µm which is an order of magnitude lesser compared to the prevalent industry standards. Implied open-circuit voltages over 760 mV were accomplished on SHJ structures deposited on n-type silicon wafers with thicknesses below 50 µm. An analytical model is also described for a better understanding of the variation of the recombination fractions for varying substrate thicknesses. The potential of using very thin wafers is also established by manufacturing SHJ solar cells, using industrially pertinent processing steps, on 40 µm thin standalone wafers while achieving maximum efficiency of 20.7%. It is also demonstrated that 40 µm thin SHJ solar cells can be manufactured using these processes on large areas. An analysis of the percentage contribution of current, voltage, and resistive losses are also characterized for the SHJ devices fabricated in this work for varying substrate thicknesses.
ContributorsBalaji, Pradeep (Author) / Bowden, Stuart (Thesis advisor) / Alford, Terry (Thesis advisor) / Goryll, Michael (Committee member) / Augusto, Andre (Committee member) / Arizona State University (Publisher)
Created2021
Assessing the Response of Macroinvertebrate Communities to River Flow Dynamics in the Sonoran Desert
Description
Climate change is causing hydrologic intensification globally by increasing both the frequency and magnitude of floods and droughts. While environmental variation is a key regulator at all levels of ecological organization, such changes to the hydrological cycle that are beyond the normal range of variability can have strong impacts on stream and riparian ecosystems within sensitive landscapes, such as the American Southwest. The main objective of this study was to investigate how anomalous hydrologic variability influences macroinvertebrate communities in desert streams. I studied seasonal changes in aquatic macroinvertebrate abundances in eleven streams that encompass a hydrologic gradient across Arizona’s Sonoran Desert. This analysis was coupled with the quantification and assessment of stochastic hydrology to determine influences of flow regimes and discrete events on invertebrate community composition. I found high community variability within sites, illustrated by seasonal measures of beta diversity and nonmetric multidimensional scaling (NMDS) plots. I observed notable patterns of NMDS data points when invertebrate abundances were summarized by summer versus winter surveys. These results suggest that there is a difference within the communities between summer and winter seasons, irrespective of differences in site hydroclimate. Estimates of beta diversity were the best metric for summarizing and comparing diversity among sites, compared to richness difference and replacement. Seasonal measures of beta diversity either increased, decreased, or stayed constant across the study period, further demonstrating the high variation within and among study sites. Regime shifts, summarized by regime shift frequency (RSF) and mean net annual anomaly (NAA), and anomalous events, summarized by the power of blue noise (Maximum Blue Noise), were the best predictors of macroinvertebrate diversity, and thus should be more widely applied to ecological data. These results suggest that future studies of community composition in freshwater systems should focus on understanding the cause of variation in biodiversity gradients. This study highlights the importance of considering both flow regimes and discrete anomalous events when studying spatial and temporal variation in stream communities.
ContributorsSainz, Ruby (Author) / Sabo, John L (Thesis advisor) / Grimm, Nancy (Committee member) / Stampoulis, Dimitrios (Committee member) / Arizona State University (Publisher)
Created2021
Description
Communicating climate risks is crucial when engaging the public to support climate action planning and addressing climate justice. How does evidence-based communication influence local residents’ risk perception and potential behavior change in support of climate planning? Built upon our previous study of Climate Justice maps illustrating high scores of both social and ecological vulnerability in Michigan’s Huron River watershed, USA, a quasi-experiment was conducted to examine the effects of Climate Justice mapping intervention on residents’ perceptions and preparedness for climate change associated hazards in Michigan. Two groups were compared: residents in Climate Justice areas with high social and ecological vulnerability scores in the watershed (n=76) and residents in comparison areas in Michigan (n=69). Measurements for risk perception include perceived exposure, sensitivity, and adaptability to hazards. Results indicate that risk information has a significant effect on perceived sensitivity and level of preparedness for future climate extremes among participants living in Climate Justice areas. Findings highlight the value of integrating scientific risk assessment information in risk communication to align calculated and perceived risks. This study suggests effective risk communication can influence local support of climate action plans and implementation of strategies that address climate justice and achieve social sustainability in local communities.
ContributorsCheng, Chingwen (Author) / Tsai, Jiun-Yi (Author) / Yang, Y. C. Ethan (Author) / Esselman, Rebecca (Author) / Kalcic, Margaret (Author) / Xu, Xin (Author) / Mohai, Paul (Author) / Herberger Institute for Design and the Arts (Contributor)
Created2017-10-12
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
Urban growth, from regional sprawl to global urbanization, is the most rapid, drastic, and irreversible form of human modification to the natural environment. Extensive land cover modifications during urban growth have altered the local energy balance, causing the city warmer than its surrounding rural environment, a phenomenon known as an urban heat island (UHI). How are the seasonal and diurnal surface temperatures related to the land surface characteristics, and what land cover types and/or patterns are desirable for ameliorating climate in a fast growing desert city? This dissertation scrutinizes these questions and seeks to address them using a combination of satellite remote sensing, geographical information science, and spatial statistical modeling techniques.
This dissertation includes two main parts. The first part proposes to employ the continuous, pixel-based landscape gradient models in comparison to the discrete, patch-based mosaic models and evaluates model efficiency in two empirical contexts: urban landscape pattern mapping and land cover dynamics monitoring. The second part formalizes a novel statistical model called spatially filtered ridge regression (SFRR) that ensures accurate and stable statistical estimation despite the existence of multicollinearity and the inherent spatial effect.
Results highlight the strong potential of local indicators of spatial dependence in landscape pattern mapping across various geographical scales. This is based on evidence from a sequence of exploratory comparative analyses and a time series study of land cover dynamics over Phoenix, AZ. The newly proposed SFRR method is capable of producing reliable estimates when analyzing statistical relationships involving geographic data and highly correlated predictor variables. An empirical application of the SFRR over Phoenix suggests that urban cooling can be achieved not only by altering the land cover abundance, but also by optimizing the spatial arrangements of urban land cover features. Considering the limited water supply, rapid urban expansion, and the continuously warming climate, judicious design and planning of urban land cover features is of increasing importance for conserving resources and enhancing quality of life.
This dissertation includes two main parts. The first part proposes to employ the continuous, pixel-based landscape gradient models in comparison to the discrete, patch-based mosaic models and evaluates model efficiency in two empirical contexts: urban landscape pattern mapping and land cover dynamics monitoring. The second part formalizes a novel statistical model called spatially filtered ridge regression (SFRR) that ensures accurate and stable statistical estimation despite the existence of multicollinearity and the inherent spatial effect.
Results highlight the strong potential of local indicators of spatial dependence in landscape pattern mapping across various geographical scales. This is based on evidence from a sequence of exploratory comparative analyses and a time series study of land cover dynamics over Phoenix, AZ. The newly proposed SFRR method is capable of producing reliable estimates when analyzing statistical relationships involving geographic data and highly correlated predictor variables. An empirical application of the SFRR over Phoenix suggests that urban cooling can be achieved not only by altering the land cover abundance, but also by optimizing the spatial arrangements of urban land cover features. Considering the limited water supply, rapid urban expansion, and the continuously warming climate, judicious design and planning of urban land cover features is of increasing importance for conserving resources and enhancing quality of life.
ContributorsFan, Chao (Author) / Myint, Soe W (Thesis advisor) / Li, Wenwen (Committee member) / Rey, Sergio J (Committee member) / Arizona State University (Publisher)
Created2016
Description
Nonlinear responses in the dynamics of climate system could be triggered by small change of forcing. Interactions between different components of Earth’s climate system are believed to cause abrupt and catastrophic transitions, of which anthropogenic forcing is a major and the most irreversible driver. Meantime, in the face of global climate change, extreme climatic events, such as extreme precipitations, heatwaves, droughts, etc., are projected to be more frequent, more intense, and longer in duration. These nonlinear responses in climate dynamics from tipping points to extreme events pose serious threats to human society on a large scale. Understanding the physical mechanisms behind them has potential to reduce related risks through different ways. The overarching objective of this dissertation is to quantify complex interactions, detect tipping points, and explore propagations of extreme events in the hydroclimate system from a new structure-based perspective, by integrating climate dynamics, causal inference, network theory, spectral analysis, and machine learning. More specifically, a network-based framework is developed to find responses of hydroclimate system to potential critical transitions in climate. Results show that system-based early warning signals towards tipping points can be located successfully, demonstrated by enhanced connections in the network topology. To further evaluate the long-term nonlinear interactions among the U.S. climate regions, causality inference is introduced and directed complex networks are constructed from climatology records over one century. Causality networks reveal that the Ohio valley region acts as a regional gateway and mediator to the moisture transport and thermal transfer in the U.S. Furthermore, it is found that cross-regional causality variability manifests intrinsic frequency ranging from interannual to interdecadal scales, and those frequencies are associated with the physics of climate oscillations. Besides the long-term climatology, this dissertation also aims to explore extreme events from the system-dynamic perspective, especially the contributions of human-induced activities to propagation of extreme heatwaves in the U.S. cities. Results suggest that there are long-range teleconnections among the U.S. cities and supernodes in heatwave spreading. Findings also confirm that anthropogenic activities contribute to extreme heatwaves by the fact that causality during heatwaves is positively associated with population in megacities.
ContributorsYang, Xueli (Author) / Yang, Zhihua (Thesis advisor) / Lai, Ying-Cheng (Committee member) / Li, Qi (Committee member) / Xu, Tianfang (Committee member) / Zeng, Ruijie (Committee member) / Arizona State University (Publisher)
Created2023
Description
A lack of public trust in governance institutions such as governments, federal agencies, and public health organizations limits national capacities to mitigate climate-related risks. Trustworthy sources of risk information are theorized to be more persuasive and more likely to motivate adaptive behaviors. Accordingly, this dissertation addresses relational and translational challenges of risk communication to support public health and safety by making climate science more accessible to communities at risk from extreme heat. This project developed and applied a theory-driven model of trust determination to understand how United States residents evaluate the trustworthiness of state governments and emergency management agencies. Using confirmatory factor analysis, a two-factor model of trustworthiness as self-reliability and source reliability was strongly empirically supported. This measurement model of trustworthiness was translated into experimental trustworthiness manipulations capable of creating statistically significant differences in perceptions of source reliability. The dissertation then tested the persuasive efficacy of trust-building risk messaging using randomized controlled trials, finding that when controlling for political ideology, message trust condition had a significant effect on attitudes toward extreme heat risk and preparedness as well as intentions to prepare for extreme heat events. Practical and theoretical implications are discussed.
ContributorsMattson, LD (Author) / Adame, Bradley (Thesis advisor) / Corman, Steven R (Committee member) / Eakin, Hallie (Committee member) / Arizona State University (Publisher)
Created2024