Ph.D. Thesis Defense Announcement

A Scalable and Adaptable Coastal-Urban Flood Modeling Framework for Changing Climate

By Youngjun Son

Advisor(s):

Dr. Emanuele Di Lorenzo (EAS) and Dr. Jian Luo (CEE)

Committee Members:  

Dr. Kevin Haas (CEE), Dr. Joseph Montoya (BIOS), and Dr. Matthew Bilskie (University of Georgia)

Date and Time:  July 17, 2023, 09:00 AM Eastern Time

Location: Ford ES&T L1114 and Zoom https://gatech.zoom.us/j/91649013179

Coastal communities in the United States are threatened by a diverse range of flood risks, such as high
tides, storm surges, heavy rainfall, and groundwater floods. In addition, global climate change further
exacerbates the severity and frequency of floods by raising sea levels and intensifying extreme weather
events. Urban flood models are vital for coastal communities to effectively assess the emerging risks of
floods and prepare resilience strategies in response to changing climates.
In the present research, a flood modeling framework is developed for applications in coastal-urban
systems. The framework introduces an accessible urban flood model for coastal applications, called WRFHydro-
CUFA, which combines two open-source models, namely WRF-Hydro and SWMM. In a pilot study
for the City of Tybee Island in Georgia, USA, the WRF-Hydro-CUFA model simulations successfully
reproduce two distinct flood events: nuisance flooding caused by the perigean spring tides in 2012 and
extreme flooding resulting from Hurricane Irma in 2017. Furthermore, a web-based dashboard is built for
operational flood predictions, integrating modeling information and existing flood-related resources, such as
real-time camera feeds and nearby water level measurements. The platform aims to facilitate the
integration of flood-related knowledge and observations from researchers, local experts, and community
practitioners. To leverage the ongoing deployments of hyper-local water level sensors along the U.S.
Georgia coasts, the flood modeling framework includes the development of a physics-based empirical
modeling approach to assimilate estuarine water levels directly using the sensor observations. The physicsbased
empirical modeling approach implements the Objective Analysis procedure, which combines
empirical observations from the water level monitoring network with spatial covariance statistics derived
from physics-based model simulations. The efficient assimilation of coastal water levels enables community
officials to reliably identify localized flood threats, particularly to critical infrastructures in coastal regions,
such as bridges and marinas.
The established flood modeling framework provides coastal communities with an accessible option to
understand emerging flood risks, which can empower them to identify effective and sustainable resilience
strategies informed by scientific insights.