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.
As coastal circulation models have evolved to predict storm-induced flooding, they must include progressively more overland regions that are normally dry, to where now it is possible for more than half of the domain to be needed in none or only some of the computations. While this evolution has improved real-time forecasting and long-term mitigation of coastal flooding, it poses a problem for parallelization in an HPC environment, especially for static paradigms in which the workload is balanced only at the start of the simulation. In this study, a dynamic rebalancing of computational work is developed for a finite-element-based, shallow-water, ocean circulation model of extensive overland flooding. The implementation has a low overhead cost, and we demonstrate a realistic hurricane-forced coastal flooding simulation can achieve peak speed-ups near 45% over the static case, thus operating now at 80−90% efficiency.
During coastal storms, forecasters and researchers use numerical models to predict the magnitude and extent of coastal flooding. These models must represent the large regions that may be affected by a storm, and thus, they can be computationally costly and may not use the highest geospatial resolution. However, predicted flood extents can be downscaled (by increasing resolution) as a post-processing step. Existing downscaling methods use either a static extrapolation of the flooding as a flat surface, or rely on subsequent simulations with nested, full-physics models at higher resolution. This research explores a middle way, in which the downscaling includes simplified physics to improve accuracy. Using results from a state-of-the-art model, we downscale its flood predictions with three methods: (1) static, in which the water surface elevations are extrapolated horizontally until they intersect the ground surface; (2) slopes, in which the gradient of the water surface is used; and (3) head loss, which accounts for energy losses due to land cover characteristics. The downscaling methods are then evaluated for forecasts and hindcasts of Hurricane Florence (2018), which caused widespread flooding in North Carolina. The static and slopes methods tend to over-estimate the flood extents. However, the head loss method generates a downscaled flooding extent that is a close match to the predictions from a higher-resolution, full-physics model. These results are encouraging for the use of these downscaling methods to support decision-making during coastal storms.
Predicting building damage as a function of hurricane hazards, building attributes, and the interaction between hazard and building attributes is a key to understanding how significant interaction reflects variation hazard intensity effect on damage based on building attribute levels. This paper develops multihazard hurricane fragility models for wood structure homes considering interaction between hazard and building attributes. Fragility models are developed for ordered categorical damage states (DS) and binary collapse / no collapse. Exterior physical damage and building attributes from rapid assessment in coastal Mississippi following Hurricane Katrina (2005), high-resolution numerical hindcast hazard intensities from the Simulating WAves Nearshore and ADvanced CIRCulation (SWAN+ADCIRC) models, and base flood elevation values are used as model input. Leave-one-out cross-validation (LOOCV) is used to evaluate model prediction accuracy. Eleven and forty-nine combinations of global damage response variables and main explanatory variables, respectively, were investigated and evaluated. Of these models, one DS and one collapse model met the rejection criteria. These models were refitted considering interaction terms. Maximum 3-s gust wind speed and maximum significant wave height were found to be factors that significantly affect damage. The interaction between maximum significant wave height and number of stories was the significant interaction term for the DS and collapse models. For every 0.3 m (0.98 ft) increase in maximum significant wave height, the estimated odds of being in a higher rather than in a lower damage state for DS model were found to be 1.95 times greater for one- rather than for two-story buildings. For every 0.3 m (0.98 ft) increase in maximum significant wave height, the estimated odds of collapse were found to be 2.23 times greater for one- rather than for two-story buildings. Model prediction accuracy was 84% and 91% for DS and collapse models, respectively. This paper does not consider the full hazard intensity experienced in Hurricane Katrina; rather, it focuses on single-family homes in a defined study area subjected to wind, wave, and storm surge hazards. Thus, the findings of this paper are not applicable for events with hazards that exceed those experienced in the study area, from which the models were derived.
This research describes a method to improve the accuracy and efficiency of coastal flooding predictions. First, an existing model is used to explore the effect of storm forward speed and timing on tides and storm surge during Hurricane Matthew (2016). It is hypothesized that the spatial variability of Matthew’s effects on total water levels is due to the surge interacting nonlinearly with tides. If the storm occurred a few hours earlier or later, then the largest surges would have been shifted to other regions of the U.S. southeast coast. A change in forward speed of the storm also should alter its associated flooding due to differences in the duration over which the storm impacts the coastal waters. If the storm had moved faster, then the peak water levels would have increased along the coast, but the overall volume of inundation would have decreased. Then this research explores ways to increase the model’s accuracy and efficiency. To better represent Matthew’s effects, a mesh with detailed coverage of the coastal regions from Florida to North Carolina was developed by combining regional meshes originally developed for floodplain mapping. Compared to predictions using the earlier model, the new mesh allows for simulations of inundation that better match to observations especially inland.
Then, to best utilize this new mesh, a multi-resolution approach is implemented to use meshes of varying resolution when and where it is required. It is hypothesized that by `switching’ from coarse- to fine-resolution meshes, with the resolution in the fine mesh concentrated only at specific coastal regions influenced by the storm at that point in time, both accuracy and computational gains can be achieved. As the storm approaches the coastline and the landfall location becomes more certain, the simulation will switch to a fine-resolution mesh that describes the coastal features in that region. Application of the approach during Hurricanes Matthew and Florence revealed the predictions to improve in both accuracy and efficiency, as compared to that from single simulations on coarse- and fine-resolution meshes, respectively.