Rapid Assessment of Damaged Homes in the Florida Keys after Hurricane Irma

On September 10, 2017, Hurricane Irma made landfall in the Florida Keys and caused significant damage. Informed by hydrodynamic storm surge and wave modeling and post-storm satellite imagery, a rapid damage survey was soon conducted for 1600+ residential buildings in Big Pine Key and Marathon. Damage categorizations and statistical analysis reveal distinct factors governing damage at these two locations. The distance from the coast is significant for the damage in Big Pine Key, as severely damaged buildings were located near narrow waterways connected to the ocean. Building type and size are critical in Marathon, highlighted by the near-complete destruction of trailer communities there. These observations raise issues of affordability and equity that need consideration in damage recovery and rebuilding for resilience.


Introduction
Hurricane Irma made landfall near Cudjoe Key (lower Florida Keys) on September 10, 2017, as a Category 3 storm. Irma caused widespread damage to the Florida Keys due to storm surge and waves. Informed by hydrodynamic modeling and post-storm satellite imagery, we carried out a field survey soon after (September 21-24) the event to investigate the damage to the Keys, particularly the Big Pine Key and Marathon areas.
Post-hurricane damage studies have improved our understanding of coastal vulnerability (e.g. Xian et al., 2015 andHatzikyriakou et al., 2015 for Hurricane Sandy;Eamon et al., 2007 andvan de Lindt et al., 2007 for Hurricane Katrina; Wang et al. 2017, Shao et al. for general cases). Here, we conduct a rapid damage survey and assessment for Hurricane Irma, and we use a statistical regression approach to quantify the contribution of specific vulnerability factors to the damage.
Such rapid post-event assessments can provide crucial information for implementing post-storm response measures (Lin et al., 2014;Horner et al., 2011;AL-Kanj et al., 2016). The raw and analyzed data from this study appear on DesignSafe 1 , a web-based research platform of the National Science Foundation's (NSF) Natural Hazards Engineering Research Infrastructure (NHERI).

Storm Surge and Wave Simulation
To understand the hazard and inform the field survey, we first use the coupled hydrodynamic and wave model ADCIRC+SWAN (Dietrich et al. 2012, Marsooli and to simulate the storm tide (i.e., water level) and wave height for Hurricane Irma. To simulate Irma's storm tide and wave

Damage Survey and Analysis
NOAA's post-storm satellite imagery 2 provides an overview of Irma's impact. The two selected survey areas in Florida Keys, the Big Pine Key and Marathon, suffered the most severe damage, according to the satellite imagery, and experienced high water levels and wave heights, indicated by hydrodynamic modeling.
Field surveys can provide detailed information for analyzing damage mechanisms. However, traditional on-site surveys require a significant time and effort, as surveyors must walk through affected areas and photograph damaged properties. Thus, we applied a rapid survey method.
Rather than walking, we drove at a speed of 10 mph throughout the affected areas, taking GPSinformed pictures from the rare side windows. Over two days, the team took 3700+ pictures for 1600+ residential buildings comprised of single family and mobile homes (e.g., trailers).
Using the collected photos and the satellite images, we categorized the damage state for each surveyed residential house. Satellite images were primarily used to assess roof damage. More detailed damage mechanisms were further evaluated from the photos. We adopted FEMA's damage state criteria used in the damage assessment study for Hurricane Sandy 3 . The categories include: No/very limited damage; Minor damage; Major damage; and Destroyed.
We found that the destroyed and severely damaged buildings were caused largely by hydrodynamic forces induced by storm surge/waves. For example, Fig. 2a shows that storm surge/waves completely crashed the lower part of a building in Big Pine Key. Fig. 2b shows debris from damaged trailers floating in the water in a trailer community in Marathon. The observed storm surge damage is consistent with the high surge and wave heights estimated for the two sites Statistical analysis confirms these general observations. We use an ordered logistic regression model to correlate the damage state with the following factors: distance from the coastline (m), building type, and building size (m 2 ). Our analysis for Big Pine Key shows that the distance from the coastline is the single significant predictor of damage state (p-value < 0.001; Table 1a), as the damage is dominated by buildings located near narrow waterways connected to the ocean. For Marathon, although many damaged houses are near the coast, house type and house size are the two significant predictors (p-value < 0.001; Table 1b)