ORCID

0000-0002-9819-5170

Keywords

Compound Flooding, Boundary Condition Generation, Probabilistic Modeling, Joint Probability Analysis, Synthetic Storm Events, Flood Hazard Assessment

Subject Categories

Civil Engineering | Environmental Engineering

Abstract

Flooding in coastal riverine environments is often driven by the combined influence of multiple hydrometeorological flood drivers, including storm surge, rainfall, waves, and river discharge, which is referred to as compound flooding. A robust flood hazard assessment requires simulation of a wide range of flood driver conditions through dynamic compound flood models to capture the variability in resultant flooding. However, such assessments remain limited by the short length of observational records and the high computational cost of physics-based methods used to generate temporally varying boundary conditions. This dissertation develops a statistical framework to characterize compound flood drivers and generate large ensembles of synthetic storm events for use in dynamic compound flood modeling. First, a copula-based mixed-population framework is introduced to estimate joint probabilities of flood drivers while explicitly accounting for different storm-generating processes. Second, the framework is applied at large spatial scales to assess the role of atmospheric rivers in compound flooding along the U.S. West Coast, showing that atmospheric rivers contribute to the majority of compound rainfall–coastal water level events and dominate joint exceedance probabilities across the region. Third, the joint probability framework is integrated into an event-generation approach that produces large ensembles of synthetic but physically plausible storm-tide hydrographs and rainfall fields for use as boundary conditions in dynamic flood models. Application to Gloucester City, New Jersey, demonstrates the importance of explicitly accounting for the variability in tides and mean sea level, which can alter simulated flood depths by more than 1 m. Finally, the framework is extended to generate tri-variate synthetic storm events consisting of storm-tide hydrographs, rainfall fields, and river discharge hydrographs. Collectively, this dissertation advances statistical approaches for compound flood hazard assessment and provides flexible tools for generating large ensembles of realistic synthetic storm events for comprehensive flood risk assessments.

Completion Date

2026

Semester

Summer

Committee Chair

Thomas Wahl

Degree

Doctor of Philosophy (Ph.D.)

College

College of Engineering and Computer Science

Department

Civil, Environmental, and Construction Engineering

Format

PDF

Document Type

Dissertation

Language

English

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