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.
To predict storm surge and its associated flooding in coastal areas, numerical models must have a detailed representation of the impacted region, and thus be accurate, without having too much detail, which can limit efficiency. This research examines the use of a multi-resolution approach to improve both efficiency and accuracy. The key idea is that, as a storm travels across the ocean, it will affect different regions at different times. Early on, as the storm moves in open water and far from people, efficiency is more important in the model predictions. But as the storm moves toward the coast, it becomes necessary to have a higher accuracy near coastal communities and infrastructure. This research examines the use of multi-resolution simulations in which, as a storm travels along its track, the model ‘switches’ from lower resolution in open water to higher resolution as the storm moves closer to land. The main research question is to determine when is it most beneficial to switch resolutions by determining when storm effects are first seen at the coast.
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.
The results of this research will provide valuable contributions to forecasters working tirelessly during hurricane season to produce accurate and efficient predictions of coastal flooding impacts. With this information, real-time forecasts can be delivered sooner to emergency managers for informing evacuation zones, thus saving lives.
AC Poisson (2021). “Identifying the Earliest Signs of Storm Impacts to Improve Hurricane Flooding Forecasts,” North Carolina State University.
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.
Subgrid modeling to account for unresolved topography within the context of shallow water equations relies on the use of coarse grids for computational efficiency. However, excessively coarse grids can lead to artificial cross flows between hydrologically disconnected areas separated by physical barriers smaller than the grid size. An approach based on introducing cell and edge clones, consisting of connected groups of pixels in each cell, is able to systematically remove such artificial cross flows. Such an approach considers that the subgrid barriers permanently divide flow among clones and effectively restrict flow to a predetermined path. In this work, a simple algorithm, along with the use of an overtopping formula, is proposed to extend the clone approach to a scenario in which clones are allowed to be further split and merged as needed, depending on the surface elevation during a given runtime. The algorithm is intended for accommodating the possibility of the subgrid barriers being inundated and no-longer dividing the flow during an extreme event. The performance of the proposed algorithm is demonstrated through a series of idealized and more realistic test cases, showing considerable improvements over existing methodologies.
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.