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ItemA Scalable and Adaptable Coastal-Urban Flood Modeling Framework for Changing Climates(Georgia Institute of Technology, 2023-07-24) Son, YoungjunCoastal 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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ItemCharacterization of Low-Frequency Ship Wake Along the Margins of Confined Channels and Connected Waterways(Georgia Institute of Technology, 2022-12-08) Muscalus, Alexandra C.Maritime shipping is essential to the global economy, but the ship wake produced by large cargo ships in the coastal environment can cause erosion and threaten shoreline features. As large ships navigate confined channels near ports, they produce low-frequency (LF) wake that stems from a water surface depression bound to the ship. At the margins of the channel, LF wake creates a tsunami-like effect for several minutes as the ship passes. Afterwards, LF waves called “trailing waves” can persist in the channel for over 30 minutes. In this research, LF wake is characterized at the shipping channel margins and connected waterways using numerical modeling and field measurements from the Savannah River, Georgia. In the first study, the significance of LF wake to coastal erosion is assessed using power as a proxy for erosion potential. Results show that LF wake is more powerful than tidal currents and wind waves and is the dominant driver of erosion. The second study examines the propagation of LF wake out of the shipping channel and into the “far-field” with field measurements in a waterway network connected to the river. It reveals that LF wake readily reaches far-field rivers and creeks, propagating at least 11 km as a long wave. The power of far-field LF wake is similar to that of tidal currents but is reduced by waterway junctions and dissipation. In the third study, trailing waves, often assumed to be cross-channel seiches, are investigated with FUNWAVE-TVD modeling and field measurements. Modeled ship passages in a simple channel generate trailing waves that are found to be edge waves propagating on the channel margins. For passages modeled in a realistic channel, the wave field is more complex: trailing waves reflect off of shoreline protrusions and propagate in multiple directions, which is congruent with field observations. The model shows that this complex wave field too consists of edge waves. As a whole, this research provides a characterization of LF wake near shipping channel margins and nearby waterways that is supported with extensive field observations and can inform wake prediction, wake mitigation strategy, and future studies about the coastal impacts of LF wake.
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ItemStatistical & dynamical multiple-scale predictability of the North Pacific ocean(Georgia Institute of Technology, 2021-07-29) Xu, TongtongProlonged ocean surface warming in North Pacific, such as those extreme events known as marine heatwaves, could lead to significant impact on coastal ecosystems. As such, predicting the North Pacific sea surface temperatures days to weeks to months or even years in advance, especially prolonged marine heatwaves and coastal variability, helps provide guidance to decision makers to understand the future ecosystem variation and to utilize the adverse situation to their benefits. Therefore, it is of vital importance to construct credible and effective ocean forecast systems on various spatial and temporal scales. While statistical models capture the large-scale dynamics and provide forecast skill comparable to the state-of-the-art climate models, regional dynamical models are necessary to resolve high resolution coastal processes and to improve coastal prediction skill. Thus, this thesis combined the use of a Linear Inverse Model (LIM) and the Regional Ocean Modeling System (ROMS), a widely-used empirical model and a commonly-accepted dynamical ocean model, to understand the North Pacific extremes and to evaluate North Pacific forecast on multiple spatial and temporal scales. This includes: (1) using LIM to analyze the statistical behaviors, progression, and prediction of marine heatwaves in Northeast Pacific; (2) using LIM to explore the prediction of North Pacific coastal systems and the impact of tropical versus extratropical Pacific on the prediction; (3) using a multi-scale nesting configuration of ROMS to resolve coastal processes and to explore the near-real time forecast skill around Pt Sal, California; (4) using the fine resolution grid of ROMS to quantify the impact of different forcings, including initial conditions, boundary forcings and atmospheric surface forcings, on the near-real time forecast.