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.
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.
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
Rising sea level represents a significant threat to coastal communities and ecosystems, including altered habitats and increased vulnerability to coastal storms and recurrent inundation. This threat is exemplified in the northern Gulf of Mexico, where low topography, marshes, and a prevalence of tropical storms have resulted in extensive coastal impacts. The ability to facilitate adaptation and mitigation measures relies, in part, on the development of robust predictive capabilities that incorporate complex biological processes with physical dynamics. Initiated in 2010, the 6-year Ecological Effects of Sea Level Rise—Northern Gulf of Mexico project applied a transdisciplinary science approach to develop a suite of integrated modeling platforms informed by empirical data that are capable of evaluating a range of climate change scenarios. This special issue highlights resultant integrated models focused on tidal hydrodynamics, shoreline morphology, oyster ecology, coastal wetland vulnerability, and storm surges that demonstrate the need for dynamic models to incorporate feedbacks among physical and biological processes in assessments of sea level rise effects on coastal systems. Effects are projected to be significant, spatially variable and nonlinear relative to sea level rise rates. Scenarios of higher sea level rise rates are projected to exceed thresholds of wetland sustainability, and many regions will experience enhanced storm surges. Influenced by an extensive collaborative stakeholder engagement process, these assessments on the coastal dynamics of sea level rise provide a strong foundation for resilience measures in the northern Gulf of Mexico and a transferable approach for application to other coastal regions throughout the world.
The SWAN+ADCIRC shallow-water circulation model, validated for Hurricane Ike (2008), was used to develop five synthetic storm surge scenarios for the upper Texas coast in which wind speed was increased and landfall location was shifted 40 km westward. The Hurricane Ike simulation and the synthetic storms were used to study the maximum water elevations in Galveston Bay, as well as the timing and behavior of surge relative to the hurricane track. Sixteen locations indicative of surge behavior in and around Galveston Bay were chosen for analysis in this paper. Results show that water surface elevations present in Galveston Bay are dominated by the counterclockwise hurricane winds and that increasing wind speeds by 15% results in approximately 23% (+/−3%) higher surge. Furthermore, shifting the storm westward causes higher levels of surge in the more populated areas due to more intense, higher shore-normal winds. This research helps to highlight the vulnerability of the upper Texas Gulf Coast to hurricane storm surge and lends insight to storm surge and flood mitigation studies in the Houston–Galveston region.
On large geographic scales, ocean waves are represented in a spectral sense via the action balance equation, which propagates action density through both geographic and spectral space. In this paper, a new computational spectral wave model is developed by using discontinuous Galerkin (DG) methods in both geographic and spectral space. DG methods allow for the use of unstructured geographic meshes and higher-order approximations for action propagation in both geographic and spectral space, which we show leads to increased accuracy. This DG spectral wave propagation model is verified and validated through comparisons to manufactured and analytic solutions as well as to the Simulating WAves Nearshore (SWAN) model. Coupled wave/circulation models are needed for many applications including for the interaction between waves and currents during daily wind and tide driven flows. We loosely couple the newDGspectralwavemodel to the DG Shallow Water Equation Model (DG-SWEM), an existing DG circulation model. In addition to formulating the DG method for the coupled wave/circulation model, we derive an a priori error estimate. Preliminary numerical results of the DG coupled wave/circulation model are presented with comparisons to DG-SWEM coupled tightly to SWAN.