Title:
A Scalable and Adaptable Coastal-Urban Flood Modeling Framework for Changing Climates
A Scalable and Adaptable Coastal-Urban Flood Modeling Framework for Changing Climates
Author(s)
Son, Youngjun
Advisor(s)
Di Lorenzo, Emanuele
Luo, Jian
Luo, Jian
Editor(s)
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Abstract
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 WRF-Hydro-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 physics-based 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.
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Date Issued
2023-07-24
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Text
Resource Subtype
Dissertation