Hurricane Loss Analysis Based on the Population-Weighted Index

Abstract

This paper discusses different measures for quantifying regional hurricane loss. The main measures used in the past are normalized percentage loss and dollar value loss. In this research, we show that these measures are useful but may not properly reflect the size of the population influenced by hurricanes. A new loss measure is proposed that reflects the hurricane impact on people occupying the structure. For demonstrating the differences among these metrics, regional loss analysis was conducted for Florida. The regional analysis was composed of three modules: the hazard module stochastically modeled the wind occurrence in the region; the vulnerability module utilized vulnerability functions developed in this research to calculate the loss; and the financial module quantified the hurricane loss. In the financial module, we calculated three loss metrics for certain region. The first metric is the average annual loss (AAL) which represents the expected loss per year in percentage. The second is the average annual dollar loss which represents the expected dollar amount loss per year. The third is the average annual population-weighted loss (AAPL)-a new measure proposed in this research. Compared with the AAL, the AAPL reflects the number of people influenced by the hurricane. The advantages of the AAPL are illustrated using three different analysis examples: (1) conventional regional loss analysis, (2) mitigation potential analysis, and (3) forecasted future loss analysis due to the change in population.

A novel framework to study community-level social and physical impacts of hurricane-induced winds through synthetic scenario analysis

“…Outcomes of hazard models are then used to estimate the performance Frontiers in Built Environment frontiersin.org of buildings using deterministic approaches (Emanuel et al, 2006), probabilistic approaches (Pinelli et al, 2004;Li and Ellingwood 2006), and Bayesian capacity modelling approaches (Mishra et al, 2017;Kakareko et al, 2020). Other studies have focused on analyzing building performance against multiple hazards (Masoomi et al, 2019;Nofal et al, 2021), and subsequent loss estimation (Vickery et al, 2006b;Baradaranshoraka et al, 2017;Kakareko et al, 2017).…”

“…Previous studies (e.g., Vickery et al, 2006b;Li and Ellingwood 2006;Baradaranshoraka et al, 2017;Kakareko et al, 2017;Salman and Li 2018;Masoomi et al, 2019;Wang et al, 2020;Nofal et al, 2021) often used fragility functions as reliable tools for estimating the various level of damage probabilities for structures given hurricane-induced hazard intensity. Many of these studies are based on probabilistic approaches and have evaluated damage and losses to residential buildings subject to hurricane wind under stationary conditions (Pinelli et al, 2004;Deierlein et al, 2020;Adhikari et al, 2021).…”

“…Besides physical impacts, socioeconomic impacts also shape post-disaster recovery of a community, which is necessary to account for in community risk management (Masterson et al, 2014;van de Lindt et al, 2020). Unfortunately, hurricane risk assessment modules have often been limited to estimating physical damages to the built environment (e.g., Pinelli et al, 2004;Vickery et al, 2006a;Xu and Brown 2008;Kakareko et al, 2017;Wang et al, 2020;Adhikari et al, 2021). Studies used various proxy indicators, including health condition, age, race, gender, poverty, and income, to estimate the social vulnerability of community (Fekete 2009;Bjarnadottir et al, 2010;Abbas and Routray 2014;Rufat et al, 2019;Drakes et al, 2021).…”

“…To move toward this goal, community planners need to be provided with tools and actionable frameworks that enable them to demonstrate disparate community level impacts to make equityinformed decisions. The majority of available frameworks in the literature, however, focus on estimating damage and direct economic losses to the built environment at the individual property level and consequently ignore the social impacts of such destructions (e.g., Pinelli et al, 2004;Vickery et al, 2006a;Xu and Brown 2008;Kakareko et al, 2017;Wang et al, 2020;Adhikari et al, 2021). Therefore, we focus the scope of our study on hurricane-induced wind hazards.…”

High-Resolution Hazard Assessment for Tropical Cyclone-Induced Wind and Precipitation: An Analytical Framework and Application

“…The first category consists of comprehensive index constructions, including the weighting, normalization, and mechanism-based methods. The weighting method gives weights to different hazard factors according to their importance, occurrence probability, or hazard intensity [20]. The normalization method normalizes a variety of indices into a single index, which is convenient for comprehensive probability analysis [21,22].…”