A Comprehensive Analysis of Hurricane Damage across the U.S. Gulf and Atlantic Coasts Using Geospatial Big Data

(1) Background: Hurricane events are expected to increase as a consequence of climate change, increasing their intensity and severity. Destructive hurricane activities pose the greatest threat to coastal communities along the U.S. Gulf of Mexico and Atlantic Coasts in the conterminous United States. This study investigated the historical extent of hurricane-related damage, identifying the most at-risk areas of hurricanes using geospatial big data. As a supplement to analysis, this study further examined the overall population trend within the hurricane at-risk zones. (2) Methods: The Sea, Lake, and Overland Surges from Hurricanes (SLOSH) model and the HURRECON model were used to estimate the geographical extent of the storm surge inundation and wind damage of historical hurricanes from 1950 to 2018. The modeled results from every hurricane were then aggregated to a single unified spatial surface to examine the generalized hurricane patterns across the affected coastal counties. Based on this singular spatial boundary coupled with demographic datasets, zonal analysis was applied to explore the historical population at risk. (3) Results: A total of 777 counties were found to comprise the “hurricane-prone coastal counties” that have experienced at least one instance of hurricane damage over the study period. The overall demographic trends within the hurricane-prone coastal counties revealed that the coastal populations are growing at a faster pace than the national average, and this growth puts more people at greater risk of hurricane hazards. (4) Conclusions: This study is the first comprehensive investigation of hurricane vulnerability encompassing the Atlantic and Gulf Coasts stretching from Texas to Maine over a long span of time. The findings from this study can serve as a basis for understanding the exposure of at-risk populations to hurricane-related damage within the coastal counties at a national scale.

A RESILIENCE-BASED ASSESSMENT OF THE PERFORMANCE OF RESIDENTIAL COMMUNITIES SUBJECT TO HURRICANE HAZARD

Hurricanes are among the most devastating and costliest natural hazards. This devastating impact urged governments and policymakers to implement mitigation plans and strategies that can enhance the community’s resilience against hurricanes. A fundamental step to gauge the performance and effectiveness of these mitigations plans is to develop computational frameworks that can provide a probabilistic assessment of the resilience of the community. Therefore, this paper presents a framework to probabilistically estimate the resilience of residential wooden buildings against hurricane winds. The framework estimates the post-hurricane damage due to dynamic wind pressure and the impact of windborne debris using an engineering-based hurricane vulnerability. The building recovery function is then estimated by integrating the estimated damage with a building-level recovery model. By aggregating building recovery functions, the community recovery function is obtained. The Monte Carlo simulation method is used to account for uncertainties related to the hazard intensity, community vulnerability, and recovery process. The framework is applied to a residential neighborhood in Miami, FL. This framework can help decision-makers to compare current community resilience with target levels, identify the gap, and set strategies to improve community resilience.

MULTI-HAZARD HURRICANE VULNERABILITY MODEL TO ENABLE RESILIENCE-INFORMED DECISION

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.

Uncertainty Reduction Through Data Management in the Development, Validation, Calibration, and Operation of a Hurricane Vulnerability Model

Catastrophe models estimate risk at the intersection of hazard, exposure, and vulnerability. Each of these areas requires diverse sources of data, which are very often incomplete, inconsistent, or missing altogether. The poor quality of the data is a source of epistemic uncertainty, which affects the vulnerability models as well as the output of the catastrophe models. This article identifies the different sources of epistemic uncertainty in the data, and elaborates on strategies to reduce this uncertainty, in particular through identification, augmentation, and integration of the different types of data. The challenges are illustrated through the Florida Public Hurricane Loss Model (FPHLM), which estimates insured losses on residential buildings caused by hurricane events in Florida. To define the input exposure, and for model development, calibration, and validation purposes, the FPHLM teams accessed three main sources of data: county tax appraiser databases, National Flood Insurance Protection (NFIP) portfolios, and wind insurance portfolios. The data from these different sources were reformatted and processed, and the insurance databases were separately cross-referenced at the county level with tax appraiser databases. The FPHLM hazard teams assigned estimates of natural hazard intensity measure to each insurance claim. These efforts produced an integrated and more complete set of building descriptors for each policy in the NFIP and wind portfolios. The article describes the impact of these uncertainty reductions on the development and validation of the vulnerability models, and suggests avenues for data improvement. Lessons learned should be of interest to professionals involved in disaster risk assessment and management.

Using rapid damage observations from social media for Bayesian updating of hurricane vulnerability functions: A case study of Hurricane Dorian

Rapid impact assessments immediately after disasters are crucial to enable rapid and effective mobilization of resources for response and recovery efforts. These assessments are often performed by analysing the three components of risk: hazard, exposure and vulnerability. Vulnerability curves are often constructed using historic insurance data or expert judgments, reducing their applicability for the characteristics of the specific hazard and building stock. Therefore, this paper outlines an approach to the creation of event-specific vulnerability curves, using Bayesian statistics (i.e., the zero-one inflated beta distribution) to update a pre-existing vulnerability curve (i.e., the prior) with observed impact data derived from social media. The approach is applied in a case study of Hurricane Dorian, which hit the Bahamas in September 2019. We analysed footage shot predominantly from unmanned aerial vehicles (UAVs) and other airborne vehicles posted on YouTube in the first 10 days after the disaster. Due to its Bayesian nature, the approach can be used regardless of the amount of data available as it balances the contribution of the prior and the observations.

The Bahamas

Growth has returned after years of stagnation. The reform-oriented government is tackling structural issues, with notable achievements in the fiscal area. The financial sector is sound and resilient to current stability threats, but credit remains scarce. The offshore sector faces heightened international scrutiny and the government is taking steps to strengthen compliance with AML/CFT and tax transparency standards. Tourism and related foreign investments will support growth in the near-term, while low competitiveness and diversification dampen medium-term prospects. Growth would suffer from a slowdown in the U.S. or higher oil prices, and hurricane vulnerability persists. Reform momentum could stall, undermining fiscal consolidation efforts.