Abstract

Storm surge is the deadliest component of extreme sea levels with one of the highest global death tolls per event. Tide gauges are the primary sources for historical sea-level measurements from which storm surge data is extracted. However, tide gauges are unevenly distributed across the globe, and most records are short in length and have gaps; this creates a challenge to assess long-term trends and perform robust extreme value analysis. This dissertation introduces a data-driven storm surge modeling framework that trains statistical and machine learning models with atmospheric and oceanographic variables. Data-driven models (DDMs) are trained and validated for more than 800 tide gauges globally using datasets that are obtained from tide gauges, satellites, and atmospheric reanalyses. By forcing DDMs with five atmospheric reanalyses, a database of global daily maximum storm surge reconstructions (GSSR, http://gssr.info) is provided for 882 tide gauges covering the 1836-2019 period. The reconstruction datasets provide an opportunity to perform long-term trend analysis and robust extreme value analysis. However, some atmospheric reanalyses have inhomogeneities that translate to surge reconstructions, introducing spurious trends not reflected in observed surges. A Bayesian change point detection method has been applied to identify and remove spurious trends from GSSR surge reconstructions. It is shown in this dissertation, that after the change point analysis, GSSR provides several decades of additional reconstructed surge data in addition to what is already available from sea-level measurements. Utilizing the post-processed surge reconstructions, a long-term trend analysis of storm surge climate has been carried out globally, particularly with respect to the magnitude and frequency of storm surges. Trends are also separately computed for the satellite-era where all five GSSR reconstructions and observed surges overlap. It is shown that the use of ensemble surge reconstructions is advisable, if possible, rather than using a single reconstruction to account for uncertainties stemming from atmospheric reanalyses.

Notes

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Graduation Date

2022

Semester

Spring

Advisor

Wahl, Thomas

Degree

Doctor of Philosophy (Ph.D.)

College

College of Engineering and Computer Science

Department

Civil, Environmental, and Construction Engineering

Degree Program

Civil Engineering

Format

application/pdf

Identifier

CFE0009066; DP0026399

URL

https://purls.library.ucf.edu/go/DP0026399

Language

English

Release Date

May 2023

Length of Campus-only Access

1 year

Access Status

Doctoral Dissertation (Open Access)

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