Keywords

season, coastal, sea level, storm surge, ocean, waves

Abstract

Changes in the seasonal cycle of extreme sea level components can modulate the flooding risk along coastlines. This dissertation evaluates the changes in and potential co-occurrence of the seasonal cycles of mean sea level, storm surge, and ocean waves. Seasonal cycles characterize the generally expected climate conditions throughout the year with distinct high and low patterns. These regular and predictable cycles have shown non-stationarity in some locations across the globe. This means the timing of the expected peak may shift to occur earlier or later. Shifts in the timing of the maximum sea level impact coastlines with increased risk of flooding if the higher mean sea level occurs at a time of year when factors like storms or high tides are also at their peak. A moving harmonic analysis is applied to decompose the data into amplitude and phase components which describe the variability and timing of the annual peak, respectively. Clustering techniques reveal coherent regions in the North Atlantic coast with similar patterns of variability in their annual sea level cycle. Dominant modes of large-scale climate variability influence changes in the seasonal cycle of both sea level and storm surge. The seasonal cycles of mean sea level and storm surge peak within 30 days of each other at a major portion of tide gauges studied. Extreme value analysis shows that the month of highest storm surge return levels often changes from winter to summer or fall when focusing on longer return periods in places where tropical cyclones occur. Globally, ocean wave seasonality is more stable over time with peaks occurring mostly within the same month over the time period studied. Significant wave height measured from satellite altimetry versus estimated from multi-model products show agreeable estimates yet also point to regions where uncertainties are relatively larger.

Completion Date

2025

Semester

Summer

Committee Chair

Wahl, Thomas

Degree

Doctor of Philosophy (Ph.D.)

College

College of Engineering and Computer Science

Format

PDF

Identifier

DP0029518

Language

English

Document Type

Thesis

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