Vulnerability occurs at the intersection of natural geophysical forces and human settlement decisions. When humans decide to place themselves and their homes in harm’s way and disinvest in mitigation measures, vulnerability ensues. Human decisions have and continue to play a large role in furthering vulnerability, especially in coastal communities. With roughly 50 percent of the United States’ population currently located on the coast and with rapid development only projected to continue, coastal communities will be faced with a future of exacerbated flood events that will result in increased surface runoff, flooding, and economic losses. This report focuses on better understanding how the build environment exacerbates coastal vulnerability. This research involves the creation of a spatial vulnerability index to flood risk for Galveston County which uncovers the degree with which the built environment is exposed to flood risk and how this vulnerability can be responded to in a manner that builds coastal resiliency. / text

Tsunami and storm surge are two of the main destructive and costly natural hazards faced by coastal communities around the world. To enhance coastal resilience and to develop effective risk management strategies, accurate and efficient tsunami and storm surge prediction models are needed. However, existing physics-based numerical models have the disadvantage of being difficult to satisfy both accuracy and efficiency at the same time. In this dissertation, several surrogate models are developed using statistical and machine learning techniques that can rapidly predict a tsunami and storm surge without substantial loss of accuracy, with respect to high-fidelity physics-based models. First, a tsunami run-up response function (TRRF) model is developed that can rapidly predict a tsunami run-up distribution from earthquake fault parameters. This new surrogate modeling approach reduces the number of simulations required to build a surrogate model by separately modeling the leading order contribution and the residual part of the tsunami run-up distribution. Secondly, a TRRF-based inversion (TRRF-INV) model is developed that can infer a tsunami source and its impact from tsunami run-up records. Since this new tsunami inversion model is based on the TRRF model, it can perform a large number of tsunami forward simulations in tsunami inversion modeling, which is impossible with physics-based models. And lastly, a one-dimensional convolutional neural network combined with principal component analysis and k-means clustering (C1PKNet) model is developed that can rapidly predict the peak storm surge from tropical cyclone track time series. Because the C1PKNet model uses the tropical cyclone track time series, it has the advantage of being able to predict more diverse tropical cyclone scenarios than the existing surrogate models that rely on a tropical cyclone condition at one moment (usually at or near landfall). The surrogate models developed in this dissertation have the potential to save lives, mitigate coastal hazard damage, and promote resilient coastal communities. / Doctor of Philosophy / Tsunami and storm surge can cause extensive damage to coastal communities; to reduce this damage, accurate and fast computer models are needed that can predict the water level change caused by these coastal hazards. The problem is that existing physics-based computer models are either accurate but slow or less accurate but fast. In this dissertation, three new computer models are developed using statistical and machine learning techniques that can rapidly predict a tsunami and storm surge without substantial loss of accuracy compared to the accurate physics-based computer models. Three computer models are as follows: (1) A computer model that can rapidly predict the maximum ground elevation wetted by the tsunami along the coastline from earthquake information, (2) A computer model that can reversely predict a tsunami source and its impact from the observations of the maximum ground elevation wetted by the tsunami, (3) A computer model that can rapidly predict peak storm surges across a wide range of coastal areas from the tropical cyclone's track position over time. These new computer models have the potential to improve forecasting capabilities, advance understanding of historical tsunami and storm surge events, and lead to better preparedness plans for possible future tsunamis and storm surges.

Living shorelines and other nature-based solutions have become more widely accepted as a cost-effective, multi-functional, and sustainable approach to coastal resilience. However, in spite of growing stakeholder support, a planning-level understanding of the hydrodynamic impact of living shorelines is not well-developed. Not only do these features vary in size, shape, and structural characteristics, but the wave environment in which they exist can be quiescent or extreme. The work presented in this paper explores the hydrodynamic effects of living shoreline features in such a way that can be generalized across a range of varying physical environments. In a series of Simulation WAves Nearshore (SWAN) simulations, we investigate the effect of wave period, wave height, bed slope, living shoreline feature length in the cross-shore direction, and feature friction coefficient on wave attenuation. Results showed that higher wave period, higher wave height, milder slopes, longer feature lengths, and higher feature roughness largely correlated with higher wave attenuation. However, only on mild slopes did additional feature lengths result in appreciable additional attenuation. Characteristic lengths were thus computed to better illustrate the cost-effectiveness of additional feature lengths given a particular wave environment. These characteristic lengths provide one way to evaluate the hydraulic efficacy of proposed living shoreline projects. In this way, regardless of the particularities of individual project sites, we aim to help planners screen potential living shoreline projects before pursuing more detailed, costly analyses. / Master of Science / Living shorelines and other nature-based solutions have become more widely accepted as a cost-effective, multi-functional, and sustainable approach to coastal resilience. However, in spite of growing stakeholder support, a planning-level understanding of the hydrodynamic impact of living shorelines is not well-developed. Not only do these features vary in size, shape, and structural characteristics, but the wave environment in which they exist can be quiescent or extreme. The work presented in this paper explores the hydrodynamic effects of living shoreline features in such a way that can be generalized across a range of varying physical environments. In a series of Simulation WAves Nearshore (SWAN) simulations, we investigate the effect of wave period, wave height, bed slope, living shoreline feature length in the cross-shore direction, and feature friction coefficient on wave attenuation. Results showed that higher wave period, higher wave height, milder slopes, longer feature lengths, and higher feature roughness largely correlated with higher wave attenuation. However, only on mild slopes did additional feature lengths result in appreciable additional attenuation. Characteristic lengths were thus computed to better illustrate the cost-effectiveness of additional feature lengths given a particular wave environment. These characteristic lengths provide one way to evaluate the hydraulic efficacy of proposed living shoreline projects. In this way, regardless of the particularities of individual project sites, we aim to help planners screen potential living shoreline projects before pursuing more detailed, costly analyses.