California is confronting its largest drought in recorded history, which may signal the onset of a megadrought. An executive order mandates reduction in residential water consumption. At the same time, the state's population continues to grow. Reducing residential outdoor water use is a critical management objective to adapt to scarce water resources. This dissertation asks, to what degree can land planning contribute to outdoor water demand management? Can principles of compact development and strategic growth mitigate use? First, the dissertation reviews the literature of water demand models that incorporate landscape variables, thematically characterizing the variables and examining model spatiotemporal resolution. Next, the dissertation examines methods of quantitatively characterizing the urban environment using land cover, weather, and landform variables. It develops variables by parcel, and then defines microclimate zones of similar parcels by clustering on each parcel's microclimate signature. In the fourth chapter, it examines the contributions of landscape and microclimate to household water consumption in the East Bay Municipal Utility District of California. It evaluates 26 million observations of monthly water use data from 2005-2011 recorded at over 300,000 single family residences. It analyzes the data as a whole, and then subsets the full population by quantile of water user and by microclimate zone. Results reveal heterogeneous water demand profiles, but all models indicate that landscape type and the presence of a pool are important predictors of consumption. Less important are lot size and microclimate variables. With both high and low water users spatially distributed throughout the study area and across microclimate zones, there is evidence that many different development styles in many different locations can be water efficient.

This research makes a significant contribution to strategic planning by introducing an approach that integrates science into the planning practices and policy-making of Oman to enable more informed and resilient development. Oman, like many of its neighbors in the Arabian Sea region, is quite vulnerable to the potential impacts of climate change including: increased temperatures, erratic precipitation, desertification of much of its land mass, coastal flooding brought on by sea level rise (SLR), and increasing frequency of hazardous storms events. Throughout history, Oman has been subjected to various storms and cyclonic events that have resulted in both flash wadi flooding and high-tidal storm surge. Records show that major storms and cyclonic events have not only caused catastrophic damage to infrastructure and loss of agricultural lands and livestock, they have often resulted in loss of human life.

These events have been frequent and climate change projections indicate that they are on the increase. Northern Oman has documented evidence of major storms and cyclonic events as far back as 1890, and increasing in periodicity today (ie. 1971, 1977, 1987, 1995, 1997, 1999, 2003, 2007, 2010, 2015, 2016, and 2017). Dhofar, located in southern Oman and the geographic study region for this research, has experienced major storms and cyclonic events on a regular basis (ie. 1948, 1959, 1963, 1966, 1983, 1996, 2002, 2004, 2011, and 2015). In order to mitigate and reduce the damage caused by these events, Oman, at an early stage of economic and industrial development, must modify its approach to strategic planning and policy-making. In fact the motivation behind this research is to promote resilient planning within the strategic-development planning process already taking place in Oman by providing a better understanding of how integrating science into the process can result in a better vision for the future, and more resilient infrastructure and development in general.

This dissertation produces the first visualization of the inundation caused by the 2002 storm at Dhofar. It integrates a high-resolution earth surface model with a dynamic three-dimensional flood model based on the 2002 historic Dhofar extreme storm event that resulted in unprecedented damage. This novel modeling method allows researchers to analyze the realistic flow of water across the landscape during an extreme storm event. In addition, increments of sea level rise are modeled to predict future inundation levels based on predicted climate change. New GIS based technologies support the modeling and quantifying of impacts on infrastructure at a very high resolution at not only local scales but across vast regions.

This research demonstrates the application of a process that can help Oman move toward sustainable development and develop the creation of a resilient infrastructure to support the local population, in addition to the tourist industry that is quickly becoming a vital economic driver. It offers a glimpse into a future in which this knowledge and approach could be informative for the development of Oman and other Gulf countries. This is achieved by: 1) providing an overview of the principles of sustainable development and the implementation of those principles within the Omani context, 2) modeling the 2002 storm-surge event in association with various SLR scenarios along the coast of Dhofar, 3) quantifying coastal tourism projects that show vulnerabilities for the 2002 storm-surge event with different SLR projections, and 4) modeling the impacts of flash wadi flooding in 2002 and investigating the effects of this flooding on urbanization and infrastructure.

Producing accurate measurements regarding the depth and extent of sea level rise and wadi flooding inundation in areas where infrastructure and urban areas are threatened is critical to sound decision-making. Analyzing the impacts of flooding on current infrastructure, economic projects, and urban areas are first steps in implementing resilient and intelligent planning in Oman.

Urban areas are increasingly exposed to weather-related environmental hazards under climate change, and equity concerns exist in adaptation and mitigation programs for these exposures and their consequences. To address these issues above, we used data-driven approaches to investigate three unique cases. We first developed a novel, cost-effective, and spatially-explicit classification of urbanization trends over time, using time-series nighttime light remote sensing images and unsupervised classification, in mainland China between 1992 and 2013. We identified five temporal typologies of urbanization, namely stable urban activity, high-level steady growth, acceleration, low-level steady growth, and fluctuation. Our classification characterizes distinct urbanization patterns over time and can be applied to environmentally sensitive and hazard-prone areas where monitoring of urbanization is critical. Next, we perform a multitemporal and multi-scenario projection of exposure to flooding, caused by sea level rise and storm surge, in the highly urbanized San Francisco Bay Area. We found increased uncertainty in exposure over time and in scenarios with higher greenhouse gas concentrations. Such elevated uncertainty suggests that stakeholders should employ adaptation strategies that are no-regret, reversible, and flexible, and that regulators may explicitly require a long-term planning horizon for adaptation programs and new developments. Finally, we investigated the allocation of a widely-adopted adaptation and mitigation program, clean vehicle rebates, from two major policy programs in California. We evaluated rebate allocation rates with respect to community characteristics including socioeconomic and environmental disadvantages, household income, racial and ethnical composition, and ambient air pollution. We found that when rebate assignment and amount were only based on vehicle technology and did not consider the varied socioeconomic backgrounds of potential applicants, rebate allocation rates were higher in advantaged, wealthier communities, and communities with intermediate levels of nitrogen dioxide concentration, but the rates were lower in communities with higher percentages of Hispanics and Non-Hispanic Blacks. After introducing an income cap, expanded vehicle eligibility, and income- and geography-tiered rebate amounts, rebate allocation rates increased in communities with lower-household income, higher percentages of Hispanics, and slightly higher nitrogen dioxide concentration. The findings of this study implied the need for and effectiveness of equity-related policy designs to spread the benefits of adaptation and mitigation programs to more diverse populations. In all, these studies seek to engage a broader conversation about environmental and societal challenges in urban areas under climate change, as well as how data-driven approaches can reveal the underlying processes of these challenges and inform better policy and decision-making.

Critical infrastructure networks provide socio-economic services across local and regional scales. However, population and economic growth, urbanization, increasing infrastructure interdependency, and natural hazards – not least due to the worsening effects of climate change – are putting these infrastructure systems under increasing pressure. A comprehensive understanding of the potential impact of climate-change-induced natural hazards on the intra- and interconnected critical infrastructure networks and network resilience options given future climatic variations is still absent. Through three types of infrastructure networks as case studies, this dissertation develops a holistic and proactive approach to gain a better understanding of climate change impacts on critical infrastructure networks. The first case study focuses on the multimodal fuel transportation network in the San Francisco Bay Area and examines the local and cascading impact of coastal flooding under 120 climate change scenarios (including four global climate models, two representative concentration pathways, three sea level rise percentile estimates, and five planning horizons). These scenarios cover a wide range of possible climate outcomes from the most conservative to the most radical. The known extremes in current climate change predictions are taken into consideration. A multimodal network model is built to capture the connectivity and flow between different infrastructure assets. We leveraged outputs from state-of-the-art hydrodynamic flood models and evaluated the direct exposure of physical infrastructure exposure and indirect cascading network impacts. Our results reveal increasing fragmentation, a decrease in network density, connectivity, and efficiency from the year 2000 to the end of the century. When taking cascading effects into consideration, the actual impact of flood hazard is greater than that estimated from exposure analysis. Building on the first, the second case study explores further into hazard impact patterns using innovative pattern recognition methods. Using the National Airspace System (NAS) infrastructure as an example, we develop a new approach to understanding the spatiotemporal impact patterns of lightning strike hazards, which is crucial for guiding the design of a resilient NAS. We discover a total of five unique hazard impact patterns leveraging 11 years of lightning occurrence data and outage data from the Federal Aviation Administration. Among them, four impact patterns reveal new spatiotemporal characteristics of the lightning hazard that are absent in the current aviation knowledge base and not yet incorporated in existing lightning protection policies and procedures. Finally, we explore beyond local, regional, and national cases and aim to find universal network resilience patterns in face of climate change impacts. In the last study, we provide the first global evaluation of urban road networks in terms of both direct exposure to flood hazards and indirect impacts due to city-wide travel disruptions and cascading failures. We create a dataset of topological road networks for 2,564 cities in 177 countries, covering over 14 million kilometers of roads, and considered ten probabilistic flood scenarios (1:5 year to 1:1000-year return periods). Our results show heterogeneous flood impacts on road infrastructure and mobility across different countries and regions. It enables comparisons of exposure and vulnerability of road networks to flood hazards across countries, allowing the identification and prioritization of urban transport resilience measures. Furthermore, it reveals patterns in network topology and flood exposure that influence the overall resilience of networks, providing a foundation for causal factor identification. The three studies create a cohesive narrative about critical infrastructure resilience in face of natural hazards under a changing climate and offer insights into a new paradigm for environmental planning. Collectively they develop new strategies for detecting exposure, vulnerability, and resilience in intra and interconnected infrastructures. This new paradigm encompasses the complexity of the interrelated human and natural environment and is necessary if we are to predict and deal with our future habitats under climate change.

This dissertation empirically evaluates the association between urban form and the energy performance of neighborhoods focusing on their energy demand and on-site green energy supply. Urban form, the spatial pattern and density of urban physical objects, such as: buildings, streets, vegetation and open space, have a considerable long-term influence on macro-scale environments. Besides their well-known impacts on vehicle trips, the effects of urban form on space-conditioning energy use and on-site energy generation has recently received attention most likely as a result of the energy crisis of the late 2000s.

However, these complex effects of urban form on their net energy savings, generation and potential trade-offs have not been rigorously and comprehensively evaluated due to computational limitations and the lack of data rich environments. In addition, we do not understand what kind of urban form is best for utilizing the benefits from new technologies (for example, efficient vehicles and solar photovoltaics) as they are rapidly being adopted in urban landscapes. Given the inertia of the built environment, it is imperative to alter urban form based on a thorough understanding and comprehensive assessment of urban systems. As our scholarship and practice of green initiatives has been somewhat piecemeal and short-sighted, it is critical that we construct comprehensive models to study the relationships between urban form and energy efficiency.

Given this challenge, this dissertation assesses the impact of urban form on energy use and on-site green energy generation by developing and applying an empirically based data rich model. This research also provides an approach for evaluating potential trade-offs between energy demand and supply given specific urban form. Given these goals, this dissertation focuses on answering three questions that each represent energy demand, supply and trade-offs that are affected by urban form:(1) does urban form have impact on residential space-conditioning energy use?; (2) does urban form have influence on on-site solar energy potential?; and (3) how does the trade-off between vehicle energy use and on-site solar energy potential vary over urban density?

To best account for a complex real-world environment in the model, this dissertation employs advanced three-dimensional urban models derived from Geographic Information Systems (GIS)and Light Detection and Ranging (LiDAR) data. This method successfully captures the physical conditions of real landscapes, including vegetation, which has been impossible to obtain until recent years. After extracting urban form and demographic variables through spatial analyses, it applies multivariate analysis to assess the impact of urban form on energy use and on-site energy generation controlling for other factors. The Cities of Sacramento and San Francisco, California are used in this research, however it is argued the approach is universal in nature.

This dissertation reveals that urban form matters in reducing cooling energy demand and increasing on-site solar energy supply in cities. The results show higher population density, east-west street orientation, higher green space density within a 100ft radius and a higher sum of tree heights on the east, south and west sides of houses have statistically significant effects on reducing summer cooling energy use after controlling for other variables. With regard to impacts of trees on on-site solar energy generation, this research also discovers higher tree density, higher average tree heights and a higher variance of tree heights have significant impacts on reduction in the average rooftop insolation. Examining the trade-off between on-site solar energy potential and vehicle energy use, the results show that the density threshold that allows personal vehicle energy use becomes smaller than rooftop solar potential, changes as vehicle and solar technologies improve and different combinations of them become available.

This dissertation is the first comprehensive validation of many of the early theoretical works on climate responsive urban design and on-site solar energy guidelines in the 1960s through the early 1980s. It supports the argument that more energy related incentives and regulations are imperative not only on a single building scale but also for its neighboring environment on a community wide scale. Finally, this research provides a new approach to how city planners can respond to technological advances and policy shifts in energy related areas.

Abstract

Renewable Energy Landscapes: Approaches to Modeling Change in the Electrical System and Predicting the Influence on Urban Development and Environmental Resources

by

Tessa Eve Beach

Doctor of Philosophy in Landscape Architecture and Environmental Planning

University of California, Berkeley

Professor John D. Radke, Chair

Of the major infrastructure systems upon which society relies, energy systems have been some of the most influential yet under recognized drivers of urban development and environmental degradation. Both energy consumption and the proportion of the population living in urban areas are projected to increase substantially over the next half-century. At the same time, in the context of the serious climate change challenges facing society, the energy sector is entering a period of significant transition in primary energy sources, turning from fossil fuels to a variety of renewable and low-carbon resources. Such a transformation will require dramatic changes in the technologies and networks via which electrical power is produced and delivered. These changes can in turn be expected to propagate new patterns of environmental impact across the landscape—both through direct effects to natural resources as well as through influence on the location and form of future urban growth. These environmental effects have the potential to be as revolutionary as those that accompanied the transition to electrical power itself.

Despite these implications, the environmental effects of infrastructure transitions remain largely unexamined ex ante, and thus, the direct and indirect environmental implications of a low-carbon energy paradigm are still poorly defined. While decarbonizing the electric power sector necessitates a transition from the existing high-carbon system, a spatial spectrum of divergent potential low-carbon energy solutions is inherently feasible given that renewable resources such as solar and wind are at once both ubiquitous across the landscape and highly concentrated in specific areas. At one end of the spectrum, it is possible to construct large (utility-scale) renewable energy generators following the existing centralized electric power paradigm. At the other end, the potential exists for a fundamentally different structure involving small-scale, distributed renewable energy generators situated at or close to points of consumption. It is currently unclear what potential changes in the morphology of the electrical power system may occur as society increasingly transitions to generation from renewable resources, and what environmental and urban development consequences may result. The research presented in this dissertation collectively contributes innovative approaches to reducing this uncertainty before widespread energy infrastructure transitions occur. These approaches involve looking backwards to conceptualize the relationships between energy, environment, and urban development through time—then projecting forward with novel geospatial methods to forecast potential direct environmental resource impacts and indirect urban development effects of various future low-carbon energy system scenarios.

Throughout history, shifting spatial opportunities and constraints associated with past primary energy source and infrastructure system transitions have resulted in significant and varying patterns of direct environmental effects, as well as changing spatial locations and configurations of urban development. The impending transition to low-carbon energy resources can also be expected to induce such changes. In this work, the potential direct air pollution, water consumption, and land use conversion effects of six future regional energy system scenarios involving centralized and decentralized low-carbon generation sources are temporally and spatially projected. It is discovered that by 2030, carbon reduction scenarios generally confer co-benefits in terms of reduced regional water consumption and pollutant emissions relative to business as usual, but require significantly more land conversion. Furthermore, the magnitude of these effects within individual scenarios shows wide spatial variation across the region. Among the decarbonization scenarios themselves, the region-wide effects vary by as much as 108,000 annual tonnes of air pollution, 145x109 annual liters of water consumption, and 491,500 cumulative square-hectares of land conversion. Focusing on the indirect environmental effects of a transition, the impacts of a shift to distributed photovoltaic electricity generation on net energy consumption along Sacramento’s electrical grid are quantified and the resulting influence on patterns of future urban growth in the city is simulated. Relative to a base growth scenario, a scenario emphasizing distributed rooftop photovoltaic energy generation would have both locally concentrating and regionally dispersing influences on future urban growth and would favor diffuse single land use development. These results suggest that, in particular, there is a trend towards significant environmental consequences from additional future direct and indirect land use associated with a low-carbon energy transition—whether generation sources are implemented at largely centralized, decentralized, or a mix of scales.

Cumulatively, the research presented in this dissertation has significant implications for energy policymaking and the field of environmental planning. Because a transition to low-carbon energy systems presents fundamental choices regarding what resources and generation scales to target, it is critical to consider the ways in which urban development and environmental impacts may change in response to different future energy system scenarios. Understanding the potential for future energy scenarios to result in such effects is critical to making informed assessments of the energy pathways available to society and to avoiding unintended environmental consequences. Moreover, the infrastructure-urban development-environment relationships conceptualized and the approaches presented herein have the potential for widespread application beyond energy systems given that many of the critical infrastructures upon which society depends are networked systems. The ability to forecast change in infrastructure systems and predict impacts before they occur can and should move the practice of environmental planning towards energy-conscious, proactive intervention at local and regional scales in order to avoid unintended environmental consequences of energy infrastructure transitions and associated urban development.

The Anthropocene epoch's highly coupled human-natural systems and unprecedented rates of change call for a paradigm shift in environmental planning to help us move from threshold-based to transformational and inclusive process-based policies. In the context of the climate change crisis, there is a very short time window to shift from short-term, single sector, and reactive practices; to policies that aim to change the fundamental attributes of socioeconomic and socioenvironmental systems in anticipation of climate change effects. Process-based policies require us to iteratively address the conditions and systems at the root cause of vulnerabilities, encouraging more systemic, integrated, and deliberately inclusive approaches. We argue that governance and spatial analysis tools can leverage this paradigm shift by better tracing the connection between decision-making power and landscape processes. Drawn from political ecology, disaster risk reduction, and complexity sciences, we hypothesize scale mismatch between policy and expected impacts from climate change to infrastructure systems results in maladaptation. Through two case studies, this dissertation explores in-depth climate risk management gaps in the transportation fuel and airport infrastructure systems. By framing the transportation fuel system (TFS) and airports as sociotechnical and socioenvironmental systems, we develop an approach that recognizes the interconnectedness, dynamic, and multiscalar challenges of networked infrastructure climate risk management. We set important next steps to enhance climate adaptation governance of complex systems.Chapter 1 introduces the background of this research together with key concepts and methods helping the reader navigate the different sections of the dissertation. Chapter 2 sets the theoretical baseline of this research by questioning the influence of critical infrastructure institutional framing of criticality metrics; how it aligns or misaligns with infrastructure resilience policy goals; what gaps are uncovered by these misalignments; and what solutions can be harnessed to address this misalignment based on disaster risk reduction and complexity science theories. Chapter 3 presents the TFS case study, showing how this infrastructure can be framed as a social, technical, and environmental system; and based on this framework, what can be learned from the contemporary TFS vulnerability to climate change that will help improve future TFS resilience and avoid maladaptation. Chapter 4 presents the airport case study and provides a method to identify how climate adaptation practices can emerge through current airport regulatory structures; and what are the barriers and pathways for airport adaptation governance. Chapter 5 combines geographical and topological concepts of space to model exposure of sociotechnical networks using empirical data of the TFS, airports, and coastal flooding projections. It provides applied examples of how to measure the links between climate-induced landscape hazards, infrastructure systems, and social entities with decision-making power; and how these interlinks identify new stakeholders, new roles, and new forms of collaboration for effective climate adaptation governance. Infrastructure systems enrich this research by providing a much needed lens to advance our knowledge on the networked nature of environmental challenges and vulnerabilities; going further than the simple co-occurrence of hazards and physical assets. Using qualitative methods (stakeholder interviews, policy review, and content analysis) and quantitative methods (Geographic Information Science and network science), infrastructure is modeled not only as technology and hardware, but as organizational and institutional networks tied together through ownership and operational jurisdiction, as well as through constitutive and operational policies. Our approach to infrastructure climate risk governance focuses on the ability of sociotechnical systems to transform adaptively from one configuration to another in anticipation of potential disturbances or shocks. Stakeholders of climate risk, and their institutions, are framed as emergent properties of complex systems, where connectivity patterns between social entities can be identified, studied, and eventually modified for improved system function and resilience. Looking at infrastructure as sociotechnical systems helps link decision-making entities and physical infrastructure across time, landscape processes, and space to optimize the scale of climate adaptation and resilience actions. It also allows expansion of ways in which infrastructure climate adaptation stakeholders can be identified and targeted for collaborative and inclusive decision-making, providing avenues for operationalizing different dimensions of environmental justice.