Predictive Multi-Hazard Hurricane Data-Based Fragility Model for Residential Homes

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%.

CC Massarra, CJ Friedland, BD Marx, JC Dietrich (2019). “Predictive Multi-Hazard Hurricane Data-Based Fragility Model for Residential Homes.” Coastal Engineering, 151, 10-21, DOI: 10.1016/j.coastaleng.2019.04.008.

Analytic Solution for Wind-Driven Setup

In their manuscript “Analytic Solutions for Computer Flow Model Testing,” Lynch and Gray present solutions for water levels and depth-averaged velocities, for tidal and/or wind forcing, and for Cartesian and polar domains. These solutions have been useful for model validation, especially for tides, and especially within the ADCIRC community — the first example problem in the ADCIRC documentation is based on one of their solutions. That problem, for tidal flows in a polar domain, has been used to validate several model advancements over the years.

However, we found an error in their solution for wind-driven setup on a polar domain. It appears to be a typographical error — the variables are not updated correctly at the last step, when the solution is generalized for a wind with arbitrary direction. This solution is not used frequently, and we did not find a correction to this error in the literature (although we were unable to access every subsequent manuscript that cited the Lynch and Gray solution). So we are documenting it here.

Continue reading

Influence of Storm Timing and Forward Speed on Tides and Storm Surge during Hurricane Matthew

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.

A Thomas, JC Dietrich, TG Asher, M Bell, BO Blanton, JH Copeland, AT Cox, CN Dawson, JG Fleming, RA Luettich (2019). “Influence of Storm Timing and Forward Speed on Tide-Surge Interactions during Hurricane Matthew.” Ocean Modelling, 137, 1-19, DOI: 10.1016/j.ocemod.2019.03.004.

News: Modeling Florence’s Storm Surge

2019/04/26 – NCSU College of Engineering
After the Storm

ncsu-engr

Dr. Casey Dietrich, an assistant professor in the Department of Civil, Construction, and Environmental Engineering (CCEE), leads the Coastal and Computational Hydraulics Team and develops computational models that predict storm surge and coastal flooding. Using the model ADCIRC, the team makes predictions about how high sea waters will rise, which areas will be flooded and for how long. These predictions are made for the entire coastline, and then his team visualizes the flooding at the scales of individual buildings and coastal infrastructure. During Florence, Dietrich’s team and collaborators acted as liaisons for state emergency managers to aid their decision making.

“The models are just one data point among many, but they’re helpful in understanding hazards and used to make predictions in real time — partly to make decisions about evacuation, where to deploy resources after, safe places to put emergency vehicles and water supplies,” he said.

The state emergency managers are able to use the flooding predictions to get immediate estimates on damages, which helps communities that are figuring out how much recovery will cost.

After Hurricane Matthew in 2016, Dietrich and his colleagues improved the models’ ability to forecast encroaching water along shorelines. Post-Florence, Dietrich said the research focus is to speed up the model and allow for more permutations to see what might happen if a storm slows down or shifts direction.

News: Connecting Erosion to Flooding

2019/04/26 – NC Sea Grant Coastwatch Currents
Model Predicts Storm Impacts on Beaches and Dunes

ncsg

During storms, strong waves and currents can erode beaches and dunes and create low-lying areas vulnerable to flooding. We use field surveys and a computer model called XBeach to predict this erosion, as well as to understand its interactions with storm-driven flooding of larger regions.

Computer models allow us to see how the storm surge and waves impact the beach over time, and which locations are vulnerable to large-scale damage. Good predictions of such storm impacts help emergency managers take better-informed measures to protect coastal areas. Understanding vulnerabilities also instructs highway access design and residential area planning.

We used the XBeach computer model on more than 30 kilometers of Hatteras Island between Avon and Rodanthe to explore how to connect erosion predictions to larger areas. Could XBeach cover more of the island, yet still provide good erosion predictions at beach and dune scales? And how could we connect erosion predictions to other models for storm surge and flooding?

Conference: FEF 2019

Downscaling ADCIRC Flooding Inundation Extents Using Kalpana

The ADCIRC modeling system is used often to predict coastal flooding due to tropical cyclones and other storms. The model uses high resolution to represent the coastal environment, including flow pathways (inlets, man-made channels, rivers) and hydraulic controls (barrier islands, raised features). However, due to the use of large domains to represent hazards on coastlines in an entire state or multiple states, the highest resolution is typically about 20 to 50 m in coastal regions. Thus, there is a potential gap between the flooding predictions and the true flooding extents. We have developed a geospatial software to downscale the flooding extents to higher resolution.

ADCIRC vs. extrapolated water levels, plan view. This image shows the difference in prediction of flooding extents, with the pale blue portion representing the original ADCIRC flooding extents and red representing the extrapolated extents.

The following documentation is for downscaling the flooding predictions by using Kalpana. This software was created originally to view ADCIRC outputs as either ESRI shapefiles or KML files (for viewing in Google Earth). ADCIRC (the ADvanced CIRCulation model) uses finite element methods to predict water levels throughout the modeled domain. Although this model is able to provide accurate predictions in a matter of minutes, these predictions have a limited resolution and are not able to provide information at the scale of buildings, roadways, and other critical infrastructure.

Continue reading