Hurricane Wind versus Storm Surge Damage in the Context of a Risk Prediction Model

Baradaranshoraka, Mohammad; Pinelli, Jean‐Paul; Gurley, Kurtis R.; Peng, Xinlai; Zhao, Mingwei

doi:10.1061/(asce)st.1943-541x.0001824

Cited by 39 publications

(17 citation statements)

References 12 publications

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“…Specifically, modern reconnaissance instrumentation can capture rare, but critical, perishable data during and following natural hazards, including the quantification of inundation extent, flow speeds, flow depth, wave conditions, wind speeds, soil properties, erosion and accretion, and inundation-related damage to civil infrastructure and the natural environment (Kennedy et al, 2020a). These data help improve understanding of, for example, (a) the interplay between the natural landscape (land cover, topographic features), the built environment (critical infrastructure, homes), and hydrodynamics and (b) how and when concurrent multi-hazard components (e.g., wind vs. surge) lead to the functional failure of critical infrastructure-ultimately leading to more resilient communities (e.g., Baradaranshoraka et al, 2017). 2.…”

Section: Reconnaissance Instrumentation and Natural Hazard Simulationmentioning

confidence: 99%

Research Needs, Challenges, and Strategic Approaches for Natural Hazards and Disaster Reconnaissance

Wartman

Berman

Bostrom

et al. 2020

Front. Built Environ.

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Section: Reconnaissance Instrumentation and Natural Hazard Simulationmentioning

confidence: 99%

Research Needs, Challenges, and Strategic Approaches for Natural Hazards and Disaster Reconnaissance

Wartman

Berman

Bostrom

et al. 2020

Front. Built Environ.

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“…Under the sponsorship of FLOIR, the FPHLM team has recently expanded the previously hurricane wind and rain-only scope of the FPHLM to include coastal and inland flood hazards. The team's strategy was to adapt the large body of tsunamirelated building fragility curves, especially the work of Suppasri et al (2013), to coastal flood, and to adapt the work of the U.S. Army Corp of Engineers (USACE 2006(USACE , 2015 for inland flood through a semi-engineering approach (Baradaranshoraka et al 2017(Baradaranshoraka et al , 2019. Regression techniques using the flood claim data are the basis for the development of the flood contents vulnerability curves, as described later in this paper.…”

Section: Florida Public Hurricane Loss Model Vulnerability Modelsmentioning

confidence: 99%

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

Pinelli

Cruz

Gurley

et al. 2020

Int J Disaster Risk Sci

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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.

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“…Many researchers have studied the differences between wind and water damage. Baradaranshoraka et al [9] applied a statistical analysis to estimate the loss model from a hurricane in Florida, US, together with engineering judgment and hazard information (e.g., intensity and timing). The interaction of wind, storm surge, flood, and waves with low-rise structures was studied by Amoroso and Gurley [10].…”

Section: Wind Vs Storm Surge Damagementioning

confidence: 99%

Statistical Analysis of Building Damage from the 2013 Super Typhoon Haiyan and its Storm Surge in the Philippines

Chaivutitorn

Tanasakcharoen

Leelawat

et al. 2020

JDR

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In November 2013, Super Typhoon Haiyan (Yolanda) hit the Philippines. It caused heavy loss of lives and extensive damages to buildings and infrastructure. When collapsed buildings are focused on, it is interesting to find that these buildings did not collapse for the same reasons after the landfall of the typhoon and storm surge. The objective of this study is to develop a statistical model for building damage due to Super Typhoon Haiyan and its storm surge. The data were collected in collaboration with Tanauan Municipality, the Philippines. The data for the inundation map were obtained by field surveys conducted on-site to determine the cause of the damages inferred from satellite data. The maximum wind speed was derived from the Holland parametric hurricane model based on the Japan Meteorological Agency (JMA) typhoon track data and the inundation depth of storm surge was calculated using the MIKE model. Multinomial logistic regression was used to develop a model to identify the significant factors influencing the damage to buildings. The result of this work is expected to be used to prepare urban plans for preventing damage from future storms.

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Hurricane Wind versus Storm Surge Damage in the Context of a Risk Prediction Model

Cited by 39 publications

References 12 publications

Research Needs, Challenges, and Strategic Approaches for Natural Hazards and Disaster Reconnaissance

Research Needs, Challenges, and Strategic Approaches for Natural Hazards and Disaster Reconnaissance

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

Statistical Analysis of Building Damage from the 2013 Super Typhoon Haiyan and its Storm Surge in the Philippines

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