In this dissertation, first a morphodynamic model is used to explore the effects of Isabel (2003) on the NC Outer Banks, with the focus on a large domain that covers 30 km of the barrier island from Rodanthe to Avon. It is hypothesized that the model can be coarsened and expanded to a large domain while preserving accuracy. Model predictions for dune erosion and overwash are in good agreement with post-storm observations. Sensitivity studies show that the model accuracy is less sensitive to the alongshore resolution of the mesh. Then, the topographic elevation changes are upscaled to a region-scale flooding model to allow overwash and inundation behind the dunes. The loose coupling of these process-based models improves the flooding predictions in region-scale model significantly.
Then, a more complex case of beaching and its impacts on larger-scale circulations are explored. Isabel (2003) breached the barrier island near the town of Hatteras and formed three channels connecting the ocean to the sound. Two-way coupling of high-resolution numerical models for coastal erosion and flooding is implemented to study the temporal and spatial evolution of the breach and its contribution to the hydrodynamics in the sound. It is hypothesized that the channels were formed due to the combined effects of ocean-side dune erosion and lagoon-side elevated water levels. The model shows that the flow from the sound to the ocean has an important role in deepening the breached channels. The morphodynamic model can predict the initiation and approximate location of the breach. However, it failed to accurately capture the channels’ depths. Several flooding scenarios are considered to implement the ground surface changes in the flooding model. The evolving breach can affect the timing and extent of flow into the lagoon. The model results show that the breach has region-scale effects on flooding that extend about 10 km into the lagoon.
Finally, the erosion of nourished beaches subjected to multiple storms is investigated. Beach nourishment provides a buffer during extreme events in the short-termbut has a finite lifespan as the beach responds to subsequent storms. Numerical models are widely used to predict the beach morphodynamics, however, they are computationally expensive. In this research, a surrogate model is developed by coupling deterministic and probabilistic models to improve the computational efficiency and to include the randomness of possible future scenarios. A large data set of storm data and beach profiles is used to create a library of thousands of hypothetical scenarios to train the surrogate model. It is hypothesized that adding the beach profile variability in the analysis can improve the model in the sense that it can be applied to any beach state. The results show that predicted erosion volume by the surrogate model is very close to the numerical model predictions. The model produced the results in a few seconds which shows a significant improvement in computational time compared to numerical models.
This research has the potential to improve the prediction of storm-driven erosion, overwash, inundation and breaching. The loose coupling of hydro- and morphodynamic models allows for better predictions of storm-driven flooding into previously protected areas, such as coastal communities and back-barrier regions. The use of observed beach profiles in the development of a surrogate model can improve its predictions of nourishment response to single and successive storms. These better predictions can enable better planning and design for mitigation of future hazards.
Storm surge and coastal flooding predictions can require high resolution of critical flow pathways and barriers, typically with simulations using grids/meshes with millions of cells/elements to represent a coastal region. However, the cost of this resolution can slow forecasts during a storm. To add resolution when and where it is needed, previous studies have used adaptive mesh methods, which update resolution at single or multiple cells but which require hierarchies of and thresholds for refinement, and nesting methods, which update resolution at subdomains but which require additional simulations. This research proposes a middle way, in which predictions from a coarse mesh are mapped, mid-simulation, onto a fine mesh with increased resolution near the storm’s projected landfall location. The coarse and fine meshes are pre-developed, thus removing any refinement decisions during the simulation, the solution mapping uses a widely used framework, thus enabling an efficient interpolation, and the same simulation is continued, thus eliminating a separate full-domain simulation. For four historical storms, results show efficiency gains of up to 53 percent, with minimal accuracy losses relative to a static simulation.
One of the most unpredictable and deadly parts of a coastal storm is the storm surge, which can cause devastating flooding of coastal regions, and can result in loss of property and life. Storm surge is a result of winds pushing water from the nearshore ocean to rise above regular tide levels. Storm surge can have a short duration; elevated water levels are limited to when the storm winds are strongest at the coast, typically for a few hours as the storm makes landfall. This short duration is a challenge for predictions of when storm surge will start, how long it will persist, and which regions will be flooded.
This research will explore the arrival of storm effects for Florence, which made landfall along the North Carolina coast during September 2018. It is an ideal storm for this research as its track was shore-normal, and thus its coastal effects increased as it approached landfall. This will allow for investigating the most optimal switch by focusing on a single switch between a lower-resolution mesh to a higher-resolution mesh. The switches will be initiated by several triggers, including wind speeds and water levels at the coast and inland locations, and with several lead times, including near and several days before landfall. Model performance will be quantified via comparisons to observations of storm effects in the region, as well as to a single, high-resolution simulation for the full storm. It will be shown that switching from a coarse resolution mesh to a fine resolution mesh will lead to an increase in efficiency gains across all switching simulations with the most optimal switch time resulting in the most accurate predictions of water levels as compared to our full high-resolution simulation.
Hurricanes or typhoons are multi-hazard events that usually result in strong winds, storm surge, waves, and debris flow. A community-level multi-hazard hurricane risk analysis approach is proposed herein to account for the combined impacts of hazards driven by hurricanes including surge, wave, and wind. A tightly coupled ADCIRC and SWAN model is used to account for the surge and wave hazard. Community-level exposure analysis is conducted using a portfolio of building archetypes associated with each hazard. A building-level hurricane vulnerability model is developed using fragility functions to account for content, building envelope, and structural damage. These fragility functions calculate the exceedance probability of predefined damage states associated with each hazard. Then, a building damage state is calculated based on the maximum probability of being in each damage state corresponding to each hazard. The proposed hurricane risk model is then applied to Waveland, Mississippi, a community that was severely impacted by Hurricane Katrina in 2005. The main contribution of this research is modeling the community-level hurricane vulnerability in terms of damage to the building envelope and interior contents driven by surge, wave, and wind using fragility functions to provide a comprehensive model for resilience-informed decision-making.
