This dissertation contributes to the pressing need to improve the connections between quantitative scientific studies and policy in the context of human vulnerability to costal hazards, at multiple spatial and temporal scales. This dissertation integrates diverse tools and methods (including GIS, Machine Learning Clustering Techniques, and spatial indices), to assess risks and vulnerabilities of coastal communities to select natural hazards, at multiple geographies and scales (i.e. California, Florida, and Latin America and Caribbean). Statistically sound methods were applied to integrate data from multiple disciplines, including: natural hazards, geographical distribution of natural habitats, population, and assets, as well as socioeconomic vulnerability, providing insights to adaptation alternatives.

The first chapter of this dissertation demonstrates that flood losses in California could be mitigated through action that meets both flood risk reduction and conservation objectives. The study demonstrates that government funded buyouts, followed by restoration of targeted lands, can support social, environmental, and economic objectives: reduction of flood exposure, restoration of natural resources, and efficient use of limited governmental funds.

In the second chapter of this dissertation, a revised and improved version of the model developed in chapter 1 is applied to the state of Florida. In addition to flood exposure and natural habitats, social vulnerability was also included in the prioritization scheme. Further, inland habitats were also included, expanding the focus of the analysis beyond just the coast. Results identified lands in Florida that are eligible to receive federal funds to attain multiple benefits: (i) reduce flood risk to home owners; (ii) reduce FEMA’s financial burden (from future flood claim payments); (iii) restore/protect natural habitats; (iv) remediate social vulnerability, and (v), identify potential sources of funding for projects. There were at least 10,000 km2 of land in Florida where such objectives may be achieved simultaneously. In a targeted case-study our model identified 92 RLPs in Miami-Dade located in areas of high social vulnerability, high flood exposure, and where natural habitats coexist. Collectively, these 92 RLPs filed 207 claims against NFIP from 1978 to 2011.

In the third chapter, I employed a combination of machine learning clustering techniques (Self Organizing Maps and K-Means algorithms) and a spatial index (GIS), to assess coastal risks in Latin America and the Caribbean (LAC) on a comparative scale. The third study meets multiple objectives, including the identification of hotspots and key drivers of coastal risk, and the ability to process large-volume multidimensional and multivariate datasets - effectively reducing sixteen variables related to coastal hazards, geographic exposure, and socioeconomic vulnerability, into a single index.

El Niño events affect the wave climate in the eastern north Pacific which can have a significant impact on the behavior of west coast beaches. In California, El Niño winters are characterized by anomalously higher and more southerly winter wave energy. This study seeks to quantify beach changes during the 2009/2010 El Niño Modoki from Point Conception to the Los Angeles-Long Beach Harbor, a 265-km portion of the Southern California Bight. Two LiDAR datasets from September of 2009 and September of 2010 were used to measure the change in position of the mean high water (MHW) and mean sea level (MSL) shorelines at over 5,000 transects spaced 50 m apart, as well as determine beach volume changes. Over the entire study area, during this one-year period, the MHW shoreline accreted by an average of 0.51 m while the mean beach elevation dropped by 0.05 m, but local beach response within the region was highly variable. Distinct cells of erosion and accretion throughout the region suggest that the observed beach changes are a result of a combination of factors including geography, shoreline orientation, beach morphology, artificial structures, sediment supply, and alongshore variations in wave energy and direction. Predictions of the increasing frequency and/or intensity of El Niño events under global warming conditions underscore the importance of understanding how coastlines will respond to changes in wave climate and may help in future coastal management.

Coastal erosion threatens to damage nearly 87,000 homes along the shoreline of the United States in the next 60 years, according to a report released in 2000 by FEMA. Population growth, rising sea levels, hurricanes, dams (that block the resupply of sand to beaches) and severe storms associated with El Niño events are escalating concerns that damage from shoreline erosion could cost hundreds of millions in coming decades. In California, these concerns are intensified by demo-graphics and geography: about 80 percent of the state’s 34 million residents live within 50 kilometers of the ocean and about 86 percent of the state’s shoreline is classified as actively eroding. Coastal bluff erosion is of particular concern in counties such as San Diego and Santa Cruz, where homes have been built on landslide-prone bluffs and where heavy surf scours the shore.

Using ArcGIS, a sea-level rise inundation comparison was conducted using four different techniques under five sea-level rise conditions for the Kauai, Hawaii, towns of Hanalei Bay, Kapa'a, and Waimea. Sea-level rise was mapped in 0.5 m increments from 0.0 m of rise to 2.0 m of rise. Datasets used in the analysis include a digital elevation model (DEM) layer, wave height data, tidal elevation data, and land cover data. The four techniques illustrating projected inundation serve as a comparison of passive versus dynamic models. The primary goals of this study were to not only compare passive and dynamic sea-level rise inundation models, but also to provide a realistic representation of what future sea-level rise will look like on Kauai, and which areas would be inundated at specific future water surface levels. The results of this analysis can be used to aid Kauai government officials in planning for the future and to aid in prioritizing where and what infrastructure and development will need to be considered before actual sea-level rise impacts occur.