Matching Items (178)
Description
This study performs numerical modeling for the climate of semi-arid regions by running a high-resolution atmospheric model constrained by large-scale climatic boundary conditions, a practice commonly called climate downscaling. These investigations focus especially on precipitation and temperature, quantities that are critical to life in semi-arid regions. Using the Weather Research and Forecast (WRF) model, a non-hydrostatic geophysical fluid dynamical model with a full suite of physical parameterization, a series of numerical sensitivity experiments are conducted to test how the intensity and spatial/temporal distribution of precipitation change with grid resolution, time step size, the resolution of lower boundary topography and surface characteristics. Two regions, Arizona in U.S. and Aral Sea region in Central Asia, are chosen as the test-beds for the numerical experiments: The former for its complex terrain and the latter for the dramatic man-made changes in its lower boundary conditions (the shrinkage of Aral Sea). Sensitivity tests show that the parameterization schemes for rainfall are not resolution-independent, thus a refinement of resolution is no guarantee of a better result. But, simulations (at all resolutions) do capture the inter-annual variability of rainfall over Arizona. Nevertheless, temperature is simulated more accurately with refinement in resolution. Results show that both seasonal mean rainfall and frequency of extreme rainfall events increase with resolution. For Aral Sea, sensitivity tests indicate that while the shrinkage of Aral Sea has a dramatic impact on the precipitation over the confine of (former) Aral Sea itself, its effect on the precipitation over greater Central Asia is not necessarily greater than the inter-annual variability induced by the lateral boundary conditions in the model and large scale warming in the region. The numerical simulations in the study are cross validated with observations to address the realism of the regional climate model. The findings of this sensitivity study are useful for water resource management in semi-arid regions. Such high spatio-temporal resolution gridded-data can be used as an input for hydrological models for regions such as Arizona with complex terrain and sparse observations. Results from simulations of Aral Sea region are expected to contribute to ecosystems management for Central Asia.
ContributorsSharma, Ashish (Author) / Huang, Huei-Ping (Thesis advisor) / Adrian, Ronald (Committee member) / Herrmann, Marcus (Committee member) / Phelan, Patrick E. (Committee member) / Vivoni, Enrique (Committee member) / Arizona State University (Publisher)
Created2012
Description
Growing concerns over climate change and the lack of a federal climate policy have prompted many sub-national organizations to undertake greenhouse gas (GHG) mitigation actions on their own. However, the interventions associated with these efforts are typically selected in a top-down and ad hoc manner, and have not created the desired GHG emissions reductions. Accordingly, new approaches are needed to identify, select, develop, and coordinate effective climate change mitigation interventions in local and regional contexts. This thesis develops a process to create a governance system for negotiating local and regional climate interventions. The process consists of four phases: 1) mapping the overall transition, 2) reconstructing the current intervention selection system, 3) assessing the system against principles identified in the literature, and 4) creating an improved system based on the assessment. This process gives users a detailed understanding of how the overall transition has progressed, how and why interventions are currently selected, what changes are needed to improve the selection system, and how to re-structure the system to create more desirable outcomes. The process results in an improved system that relies on participation, coordination, and accountability to proactively select evidence-based interventions that incorporate the interests of stakeholders and achieve system-level goals. The process was applied to climate change mitigation efforts underway in Sonoma County, California to explore the implications of real-world application, and demonstrate its utility for current climate change mitigation efforts. Note that results and analysis from interviews with Sonoma County climate actors are included as a supplementary file.
ContributorsCulotta, Daniel Scott (Author) / Wiek, Arnim (Thesis advisor) / Basile, George (Committee member) / Shrestha, Milan (Committee member) / Arizona State University (Publisher)
Created2012
Description
Perceptions of climate variability and change reflect local concerns and the actual impacts of climate phenomena on people's lives. Perceptions are the bases of people's decisions to act, and they determine what adaptive measures will be taken. But perceptions of climate may not always be aligned with scientific observations because they are influenced by socio-economic and ecological variables. To find sustainability solutions to climate-change challenges, researchers and policy makers need to understand people's perceptions so that they can account for likely responses. Being able to anticipate responses will increase decision-makers' capacities to create policies that support effective adaptation strategies. I analyzed Mexican maize farmers' perceptions of drought variability as a proxy for their perceptions of climate variability and change. I identified the factors that contribute to the perception of changing drought frequency among farmers in the states of Chiapas, Mexico, and Sinaloa. I conducted Chi-square tests and Logit regression analyses using data from a survey of 1092 maize-producing households in the three states. Results showed that indigenous identity, receipt of credits or loans, and maize-type planted were the variables that most strongly influenced perceptions of drought frequency. The results suggest that climate-adaptation policy will need to consider the social and institutional contexts of farmers' decision-making, as well as the agronomic options for smallholders in each state.
ContributorsRodríguez, Natalia (Author) / Eakin, Hallie (Thesis advisor) / Muneepeerakul, Rachata (Thesis advisor) / Manuel-Navarrete, David (Committee member) / Arizona State University (Publisher)
Created2015
Description
Sustainability depends in part on our capacity to resolve dilemmas of the commons in Coupled Infrastructure Systems (CIS). Thus, we need to know more about how to incentivize individuals to take collective action to manage shared resources. Moreover, given that we will experience new and more extreme weather events due to climate change, we need to learn how to increase the robustness of CIS to those shocks. This dissertation studies irrigation systems to contribute to the development of an empirically based theory of commons governance for robust systems. I first studied the eight institutional design principles (DPs) for long enduring systems of shared resources that the Nobel Prize winner Elinor Ostrom proposed in 1990. I performed a critical literature review of 64 studies that looked at the institutional configuration of CIS, and based on my findings I propose some modifications of their definitions and application in research and policy making. I then studied how the revisited design principles, when analyzed conjointly with biophysical and ethnographic characteristics of CISs, perform to avoid over-appropriation, poverty and critical conflicts among users of an irrigation system. After carrying out a meta-analysis of 28 cases around the world, I found that particular combinations of those variables related to population size, countries corruption, the condition of water storage, monitoring of users behavior, and involving users in the decision making process for the commons governance, were sufficient to obtain the desired outcomes. The two last studies were based on the Peruvian Piura Basin, a CIS that has been exposed to environmental shocks for decades. I used secondary and primary data to carry out a longitudinal study using as guidance the robustness framework, and different hypothesis from prominent collapse theories to draw potential explanations. I then developed a dynamic model that shows how at the current situation it is more effective to invest in rules enforcement than in the improvement of the physical infrastructure (e.g. reservoir). Finally, I explored different strategies to increase the robustness of the system, through enabling collective action in the Basin.
ContributorsRubinos, Cathy (Author) / Anderies, John M (Thesis advisor) / Abbott, Joshua K (Committee member) / Janssen, Marcus A (Committee member) / Arizona State University (Publisher)
Created2017
Description
The subliminal impact of framing of social, political and environmental issues such as climate change has been studied for decades in political science and communications research. Media framing offers an “interpretative package" for average citizens on how to make sense of climate change and its consequences to their livelihoods, how to deal with its negative impacts, and which mitigation or adaptation policies to support. A line of related work has used bag of words and word-level features to detect frames automatically in text. Such works face limitations since standard keyword based features may not generalize well to accommodate surface variations in text when different keywords are used for similar concepts.
This thesis develops a unique type of textual features that generalize triplets extracted from text, by clustering them into high-level concepts. These concepts are utilized as features to detect frames in text. Compared to uni-gram and bi-gram based models, classification and clustering using generalized concepts yield better discriminating features and a higher classification accuracy with a 12% boost (i.e. from 74% to 83% F-measure) and 0.91 clustering purity for Frame/Non-Frame detection.
The automatic discovery of complex causal chains among interlinked events and their participating actors has not yet been thoroughly studied. Previous studies related to extracting causal relationships from text were based on laborious and incomplete hand-developed lists of explicit causal verbs, such as “causes" and “results in." Such approaches result in limited recall because standard causal verbs may not generalize well to accommodate surface variations in texts when different keywords and phrases are used to express similar causal effects. Therefore, I present a system that utilizes generalized concepts to extract causal relationships. The proposed algorithms overcome surface variations in written expressions of causal relationships and discover the domino effects between climate events and human security. This semi-supervised approach alleviates the need for labor intensive keyword list development and annotated datasets. Experimental evaluations by domain experts achieve an average precision of 82%. Qualitative assessments of causal chains show that results are consistent with the 2014 IPCC report illuminating causal mechanisms underlying the linkages between climatic stresses and social instability.
This thesis develops a unique type of textual features that generalize triplets extracted from text, by clustering them into high-level concepts. These concepts are utilized as features to detect frames in text. Compared to uni-gram and bi-gram based models, classification and clustering using generalized concepts yield better discriminating features and a higher classification accuracy with a 12% boost (i.e. from 74% to 83% F-measure) and 0.91 clustering purity for Frame/Non-Frame detection.
The automatic discovery of complex causal chains among interlinked events and their participating actors has not yet been thoroughly studied. Previous studies related to extracting causal relationships from text were based on laborious and incomplete hand-developed lists of explicit causal verbs, such as “causes" and “results in." Such approaches result in limited recall because standard causal verbs may not generalize well to accommodate surface variations in texts when different keywords and phrases are used to express similar causal effects. Therefore, I present a system that utilizes generalized concepts to extract causal relationships. The proposed algorithms overcome surface variations in written expressions of causal relationships and discover the domino effects between climate events and human security. This semi-supervised approach alleviates the need for labor intensive keyword list development and annotated datasets. Experimental evaluations by domain experts achieve an average precision of 82%. Qualitative assessments of causal chains show that results are consistent with the 2014 IPCC report illuminating causal mechanisms underlying the linkages between climatic stresses and social instability.
ContributorsAlashri, Saud (Author) / Davulcu, Hasan (Thesis advisor) / Desouza, Kevin C. (Committee member) / Maciejewski, Ross (Committee member) / Hsiao, Sharon (Committee member) / Arizona State University (Publisher)
Created2018
Description
Climate change impacts are evident throughout the world, particularly in the low lying coastal areas. The multidimensional nature and cross-scale impacts of climate change require a concerted effort from different organizations operating at multiple levels of governance. The efficiency and effectiveness of the adaptation actions of these organizations rely on the problem framings, network structure, and power dynamics of the organizations and the challenges they encounter. Nevertheless, knowledge on how organizations within multi-level governance arrangements frame vulnerability, how the adaptation governance structure shapes their roles, how power dynamics affect the governance process, and how barriers emerge in adaptation governance as a result of multi-level interactions is limited. In this dissertation research, a multilevel governance perspective has been adopted to address these knowledge gaps through a case study of flood risk management in coastal Bangladesh. Key-informant interviews, systematic literature review, spatial multi-criteria decision analysis, social network analysis (SNA), and content analysis techniques have been used to collect and analyze data. This research finds that the organizations involved in adaptation governance generally have aligned framings of vulnerability, irrespective of the level at which they are operated, thus facilitating adaptation decision-making. However, this alignment raises concerns of a neglect of socio-economic aspects of vulnerability, potentially undermining adaptation initiatives. This study further finds that the adaptation governance process is elite-pluralistic in nature, but has a coexistence of top-down and bottom-up processes in different phases of adaptation actions. The analysis of power dynamics discloses the dominance of a few national level organizations in the adaptation governance process in Bangladesh. Lastly, four mechanisms have been found that can explain how organizational culture, practices, and preferences dictate the emergence of barriers in the adaptation governance process. This dissertation research overall advances our understanding on the significance of multilevel governance approach in climate change adaptation governance.
ContributorsIshtiaque, Asif (Author) / Chhetri, Netra (Thesis advisor) / Eakin, Hallie (Thesis advisor) / Myint, Soe W (Committee member) / Arizona State University (Publisher)
Created2019
Description
Electricity infrastructure vulnerabilities were assessed for future heat waves due to climate change. Critical processes and component relationships were identified and characterized with consideration for the terminal event of service outages, including cascading failures in transmission-level components that can result in blackouts. The most critical dependency identified was the increase in peak electricity demand with higher air temperatures. Historical and future air temperatures were characterized within and across Los Angeles County, California (LAC) and Maricopa County (Phoenix), Arizona. LAC was identified as more vulnerable to heat waves than Phoenix due to a wider distribution of historical temperatures. Two approaches were developed to estimate peak demand based on air temperatures, a top-down statistical model and bottom-up spatial building energy model. Both approaches yielded similar results, in that peak demand should increase sub-linearly at temperatures above 40°C (104 °F) due to saturation in the coincidence of air conditioning (AC) duty cycles. Spatial projections for peak demand were developed for LAC to 2060 considering potential changes in population, building type, building efficiency, AC penetration, appliance efficiency, and air temperatures due climate change. These projections were spatially allocated to delivery system components (generation, transmission lines, and substations) to consider their vulnerability in terms of thermal de-rated capacity and weather adjusted load factor (load divided by capacity). Peak hour electricity demand was projected to increase in residential and commercial sectors by 0.2–6.5 GW (2–51%) by 2060. All grid components, except those near Santa Monica Beach, were projected to experience 2–20% capacity loss due to air temperatures exceeding 40 °C (104 °F). Based on scenario projections, and substation load factors for Southern California Edison (SCE), SCE will require 848—6,724 MW (4-32%) of additional substation capacity or peak shaving in its LAC service territories by 2060 to meet additional demand associated with population growth projections.
ContributorsBurillo, Daniel (Author) / Chester, Mikhail V (Thesis advisor) / Ruddell, Benjamin (Committee member) / Johnson, Nathan (Committee member) / Arizona State University (Publisher)
Created2018
Description
The role of climate change, as measured in terms of changes in the climatology of geophysical variables (such as temperature and rainfall), on the global distribution and burden of vector-borne diseases (VBDs) remains a subject of considerable debate. This dissertation attempts to contribute to this debate via the use of mathematical (compartmental) modeling and statistical data analysis. In particular, the objective is to find suitable values and/or ranges of the climate variables considered (typically temperature and rainfall) for maximum vector abundance and consequently, maximum transmission intensity of the disease(s) they cause.
Motivated by the fact that understanding the dynamics of disease vector is crucial to understanding the transmission and control of the VBDs they cause, a novel weather-driven deterministic model for the population biology of the mosquito is formulated and rigorously analyzed. Numerical simulations, using relevant weather and entomological data for Anopheles mosquito (the vector for malaria), show that maximum mosquito abundance occurs when temperature and rainfall values lie in the range [20-25]C and [105-115] mm, respectively.
The Anopheles mosquito ecology model is extended to incorporate human dynamics. The resulting weather-driven malaria transmission model, which includes many of the key aspects of malaria (such as disease transmission by asymptomatically-infectious humans, and enhanced malaria immunity due to repeated exposure), was rigorously analyzed. The model which also incorporates the effect of diurnal temperature range (DTR) on malaria transmission dynamics shows that increasing DTR shifts the peak temperature value for malaria transmission from 29C (when DTR is 0C) to about 25C (when DTR is 15C).
Finally, the malaria model is adapted and used to study the transmission dynamics of chikungunya, dengue and Zika, three diseases co-circulating in the Americas caused by the same vector (Aedes aegypti). The resulting model, which is fitted using data from Mexico, is used to assess a few hypotheses (such as those associated with the possible impact the newly-released dengue vaccine will have on Zika) and the impact of variability in climate variables on the dynamics of the three diseases. Suitable temperature and rainfall ranges for the maximum transmission intensity of the three diseases are obtained.
Motivated by the fact that understanding the dynamics of disease vector is crucial to understanding the transmission and control of the VBDs they cause, a novel weather-driven deterministic model for the population biology of the mosquito is formulated and rigorously analyzed. Numerical simulations, using relevant weather and entomological data for Anopheles mosquito (the vector for malaria), show that maximum mosquito abundance occurs when temperature and rainfall values lie in the range [20-25]C and [105-115] mm, respectively.
The Anopheles mosquito ecology model is extended to incorporate human dynamics. The resulting weather-driven malaria transmission model, which includes many of the key aspects of malaria (such as disease transmission by asymptomatically-infectious humans, and enhanced malaria immunity due to repeated exposure), was rigorously analyzed. The model which also incorporates the effect of diurnal temperature range (DTR) on malaria transmission dynamics shows that increasing DTR shifts the peak temperature value for malaria transmission from 29C (when DTR is 0C) to about 25C (when DTR is 15C).
Finally, the malaria model is adapted and used to study the transmission dynamics of chikungunya, dengue and Zika, three diseases co-circulating in the Americas caused by the same vector (Aedes aegypti). The resulting model, which is fitted using data from Mexico, is used to assess a few hypotheses (such as those associated with the possible impact the newly-released dengue vaccine will have on Zika) and the impact of variability in climate variables on the dynamics of the three diseases. Suitable temperature and rainfall ranges for the maximum transmission intensity of the three diseases are obtained.
ContributorsOkuneye, Kamaldeen O (Author) / Gumel, Abba B (Thesis advisor) / Kuang, Yang (Committee member) / Smith, Hal (Committee member) / Thieme, Horst (Committee member) / Nagy, John (Committee member) / Arizona State University (Publisher)
Created2018
Description
Motivated by the need for cities to prepare and be resilient to unpredictable future weather conditions, this dissertation advances a novel infrastructure development theory of “safe-to-fail” to increase the adaptive capacity of cities to climate change. Current infrastructure development is primarily reliant on identifying probable risks to engineered systems and making infrastructure reliable to maintain its function up to a designed system capacity. However, alterations happening in the earth system (e.g., atmosphere, oceans, land, and ice) and in human systems (e.g., greenhouse gas emission, population, land-use, technology, and natural resource use) are increasing the uncertainties in weather predictions and risk calculations and making it difficult for engineered infrastructure to maintain intended design thresholds in non-stationary future. This dissertation presents a new way to develop safe-to-fail infrastructure that departs from the current practice of risk calculation and is able to manage failure consequences when unpredicted risks overwhelm engineered systems.
This dissertation 1) defines infrastructure failure, refines existing safe-to-fail theory, and compares decision considerations for safe-to-fail vs. fail-safe infrastructure development under non-stationary climate; 2) suggests an approach to integrate the estimation of infrastructure failure impacts with extreme weather risks; 3) provides a decision tool to implement resilience strategies into safe-to-fail infrastructure development; and, 4) recognizes diverse perspectives for adopting safe-to-fail theory into practice in various decision contexts.
Overall, this dissertation advances safe-to-fail theory to help guide climate adaptation decisions that consider infrastructure failure and their consequences. The results of this dissertation demonstrate an emerging need for stakeholders, including policy makers, planners, engineers, and community members, to understand an impending “infrastructure trolley problem”, where the adaptive capacity of some regions is improved at the expense of others. Safe-to-fail further engages stakeholders to bring their knowledge into the prioritization of various failure costs based on their institutional, regional, financial, and social capacity to withstand failures. This approach connects to sustainability, where city practitioners deliberately think of and include the future cost of social, environmental and economic attributes in planning and decision-making.
ContributorsKim, Yeowon (Author) / Chester, Mikhail (Thesis advisor) / Eakin, Hallie (Committee member) / Redman, Charles (Committee member) / Miller, Thaddeus R. (Committee member) / Arizona State University (Publisher)
Created2018
Description
Infrastructure are increasingly being recognized as too rigid to quickly adapt to a changing climate and a non-stationary future. This rigidness poses risks to and impacts on infrastructure service delivery and public welfare. Adaptivity in infrastructure is critical for managing uncertainties to continue providing services, yet little is known about how infrastructure can be made more agile and flexible towards improved adaptive capacity. A literature review identified approximately fifty examples of novel infrastructure and technologies which support adaptivity through one or more of ten theoretical competencies of adaptive infrastructure. From these examples emerged several infrastructure forms and possible strategies for adaptivity, including smart technologies, combined centralized/decentralized organizational structures, and renewable electricity generation. With institutional and cultural support, such novel structures and systems have the potential to transform infrastructure provision and management.
ContributorsGilrein, Erica (Author) / Chester, Mikhail (Thesis advisor) / Garcia, Margaret (Committee member) / Allenby, Braden (Committee member) / Arizona State University (Publisher)
Created2018