Storm surge has been the deadliest and costliest hurricane induced hazard in the coastal United States. In order to save property and lives, emergency managers must issue advisories guided by numerical models in a timely manner. However, these surge models are highly dependent on weather forecasts which contain uncertainties themselves as hurricanes traverse through the waters before making landfall. This dissertation aims to understand the contribution of uncertainties in hurricane properties, particularly the wind intensity which can contribute to uncertainties in storm surge models. Investigating these properties through numerical modeling is both computationally and time intensive. However by developing a methodology that takes into account the natural variability of wind intensities in the recent decade, it is only necessary to conduct a small number of simulations which reflect 90% of the variation of what has been observed. Using this method results in a more robust surge prediction in coastal locations by producing statistics of the expected range of storm surges, including minimum and maximum inundation volumes. Additionally, although the use of machine learning and statistical models have proven to be fast and computationally efficient, it is demonstrated that there is still a great need for deterministic modeling, especially in the changing climate. Deterministic modeling is used to show that we can expect increases in both inundation volume and area under future climate conditions. This study also showed that at the end of the century, hurricanes may produce larger surge magnitudes in concentrated areas as opposed to surges that are lower in magnitude and widespread. One notable finding of this study is that there is no single storm property that dictates the magnitude of surge inundation. Even when these properties are considered together, the results are still difficult to anticipate without explicit numeri- cal simulation. Due to dynamic hurricane properties, storm surge risk communication has been challenging. Despite communication changes from the National Hurricane Center, we have found that there is a lingering association between the Saffir-Simpson Hurricane Wind Scale (SSHWS) and storm surge risk by the general public. However, findings sug- gest that although improving communication can indeed improve risk perception, it only addresses one component of a multidisciplinary system that defines storm surge risk. To be truly effective, resilience efforts will require multidisciplinary approaches.
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Doctor of Philosophy (Ph.D.)
College of Engineering and Computer Science
Civil, Environmental, and Construction Engineering
Length of Campus-only Access
Doctoral Dissertation (Open Access)
Camelo, Jeane, "The Use of Physically Based Models and Ensemble Forecasting for Storm Surge Risk Assessment in a Changing Climate" (2021). Electronic Theses and Dissertations, 2020-. 479.