Multi-hazard hurricane data-based fragility models are able to represent multiple predictor variables, be validated based on observed data, and consider variability in building characteristics and hazard variables. This paper develops predictive hurricane, multi-hazard, single-family building fragility models for ordered categorical damage states (DS) and binary complete failure/non-complete failure using proportional odds cumulative logit and logistic regression models, respectively. In addition to their simplicity, these models are able to represent multiple hurricane hazard variables and include variable interactions, thus improving model fitting and damage prediction. Surveys of physical damage in coastal Mississippi following Hurricane Katrina (2005) and high-resolution numerical hindcast hazard intensities from the Simulating WAves Nearshore and ADvanced CIRCulation (SWAN + ADCIRC) models are used as model input. Prediction accuracy is expressed in terms of cross-validation (CV) and evaluated using leave-one-out cross-validation (LOOCV).
Thirty-nine combinations of global damage response variables were investigated. Of these models, six DS and one complete failure model met the evaluation criteria. Maximum significant wave height was the only significant hazard variable for the DS models, while maximum 3-s gust wind speed, maximum surge depth, and maximum water speed were found to be significant predictors for the complete failure model. Model prediction external accuracy ranged from 81% to 87%.
The amount and extent of coastal flooding caused by hurricanes can be sensitive to the timing or speed of the storm. For storms moving parallel to the coast, the hazards can be stretched over a larger area. Hurricane Matthew was a powerful storm that impacted the southeastern U.S. during October 2016, moving mostly parallel to the coastline from Florida through North Carolina. In this study, three sources for atmospheric forcing are considered for a simulation of Matthew’s water levels, which are validated against extensive observations, and then the storm’s effects are explored on this long coastline. It is hypothesized that the spatial variability of Matthew’s effects on total water levels is partly due to the surge interacting nonlinearly with tides. By changing the time of occurrence of the storm, differences in storm surge are observed in different regions due to the storm coinciding with other periods in the tidal cycles. These differences are found to be as large as 1m and comparable to the tidal amplitude. 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. With respect to the forward speed, the present study contributes to established results by considering the scenario of a shore-parallel hurricane. A faster storm caused an increase in peak water levels along the coast but a decrease in the overall volume of inundation. On the other hand, a slower storm pushed more water into the estuaries and bays and flooded a larger section of the coast. Implications for short-term forecasting and long-term design studies for storms moving parallel to long coastlines are discussed herein.
In 2017, Hurricanes Harvey, Irma, and Maria caused more than $200 billion dollars of damage in the United States, as well as the incalculable cost of the loss of life and mental trauma associated with these disasters. In a changing climate, sea level rise and the potential for increasing tropical cyclone intensity can result in even more devastating damages. Therefore, engineers, community planners, and coastal residents need accurate, timely, and accessible forecasting of storm processes and their impact on coastal communities to bolster national resilience and reduce risk to life and property during these events. However, along with uncertainties in understanding and modeling of storm processes, there are complex challenges associated with determining and meeting the needs of end users who rely on these forecasts for emergency management decisions.
The study begins with an application of a state-of-the-art storm surge model to examine the effects of meteorological forecast errors on coastal flooding predictions along the North Carolina (NC) coastline. As Hurricane Arthur (2014) moved over Pamlico Sound, it increased the total water levels to 2.5 m above sea level; this water pushed first into the river estuaries and against the inner banks, and then moved eastward to threaten the sound-side of the barrier islands. It is hypothesized that a combination of storm track and intensity errors caused errors in the forecast winds and water levels along the NC coast during Arthur. Model results reveal that, as the forecast storm track and intensity errors increase, the errors in forecast wind speeds also increase, but the errors in forecast water levels remain relatively the same, signifying the non-linear response of the coastal ocean to wind effects. By separating the forecast errors in storm track and storm strength, this study quantifies their effects on the coastal ocean, which provides useful guidance for designing relevant forecast ensembles.
In addition to flooding impacts, storms can also cause dramatic changes in estuarine salinities, which can negatively impact estuarine ecosystems. Baroclinic models are useful tools for predicting estuarine salinity response under changing environmental conditions. In the present work, the features of wind- and plume-driven circulation in the vicinity of Choctawhatchee Bay (CB) and Destin Inlet, Florida, are analyzed with a recently-enhanced, three-dimensional, baroclinic model. Satellite imagery showed a visible brackish surface plume at Destin during low tide. The goal of the present study is to quantify variability in the plume signature due to changes in tidal and wind forcing. Modeled tides, salinities and plume signature are validated against in-situ observations and satellite imagery and then applied to analyze plume response in two scenarios. In the first case, model plume behavior is analyzed on successive days of near-constant tidal amplitudes and changing wind directions due to passing cold fronts. In the second case, plume response is investigated during consecutive days of neap-spring variability in the tides and near-constant wind speeds. Model results reveal a larger plume during spring tides and periods of weak wind forcing. Oshore winds enhance the north-south expansion of the plume, whereas onshore winds restrict the plume to the coastline.
Finally, the validated model is applied to identify salinity and transport characteristics within CB. Based on past studies, it is hypothesized that CB is a stratified system with limited flushing and zones of distinct salinity gradients. These hypotheses are tested by analyzing bay salinities from the validated model during a period of low river flows. Model surface salinities indicate brackish conditions (20 psu) throughout the bay except for near the river mouth. Stratification (10 to 15 psu) within the bay is unaffected by the passage of cold fronts and neap-spring tidal variability. The residence time within the Choctawhatchee Bay, an important indicator of estuarine health, is computed via particle tracking and is equal to roughly 40 days.
Storm surge prediction models rely on an accurate representation of the wind conditions. In this paper, we examine the sensitivity of surge predictions to forecast uncertainties in the track and strength of a storm (storm strength is quantified by the power dissipation of the associated wind field). This analysis is performed using Hurricane Arthur (2014), a Category 2 hurricane, which made landfall along the North Carolina (NC) coast in early July 2014. Hindcast simulations of a coupled hydrodynamic-wave model are performed on a large unstructured mesh to analyze the surge impact of Arthur along the NC coastline. The effects of Arthur are best represented by a post-storm data assimilated wind product with parametric vortex winds providing a close approximation. Surge predictions driven by forecast advisories issued by the National Hurricane Center (NHC) during Arthur are analyzed. The storm track predictions from the NHC improve over time. However, successive advisories predict an unrealistic increase in the storm’s strength. Due to these forecast errors, the global root mean square errors of the predicted wind speeds and water levels increase as the storm approaches landfall. The relative impacts of the track and strength errors on the surge predictions are assessed by replacing forecast storm parameters with the best known post-storm information about Arthur. In a “constant track” analysis, Arthur’s post storm determined track is used in place of the track predictions of the different advisories but each advisory retains its size and intensity predictions. In a “constant storm strength” analysis, forecast wind and pressure parameters are replaced by corresponding parameters extracted from the post storm analysis while each advisory retains its forecast storm track. We observe a strong correlation between the forecast errors and the wind speed predictions. However, the correlation between these errors and the forecast water levels is weak signifying a non-linear response of the shallow coastal waters to meteorological forcing.
Tackling critical questions often requires the collaboration of researchers from different disciplines or institutions. Coastal hazards research is necessarily interdisciplinary and multi- methodological and often requires a team of researchers, due to its combination of storm-induced changes to the coastal environment, the effects of these changes on built infrastructure, and the combined effects on decision-making for individuals and communities. This paper introduces an interdisciplinary coastal hazard risk model that combines high resolution geospatial data, storm impact forecasts, and an agent-based model in the analysis, and then describes the model’s implementation in a data science cyberinfrastructure. Lessons learned and limitations are also outlined.
Storm surge and overland flooding can be predicted with computational models at high levels of resolution. To improve efficiency in forecasting applications, surge models often use atmospheric forcing from parametric vortex models, which represent the surface pressures and wind fields with a few storm parameters. The future of storm surge prediction could involve real-time coupling of surge and full-physics atmospheric models; thus, their accuracies must be understood in a real hurricane scenario. The authors compare predictions from a parametric vortex model (using forecast tracks from the National Hurricane Center) and a full-physics coupled atmosphere-wave-ocean model during Hurricane Isaac (2012). The predictions are then applied within a tightly coupled, wave and surge modeling system describing the northern Gulf of Mexico and the floodplains of southwest Louisiana. It is shown that, in a hindcast scenario, a parametric vortex model can outperform a data-assimilated wind product, and given reasonable forecast advisories, a parametric vortex model gives reasonable surge forecasts. However, forecasts using a full-physics coupled model outperformed the forecast advisories and improved surge forecasts. Both approaches are valuable for forecasting the coastal impacts associated with tropical cyclones