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A data-driven spatial approach to characterize the flood hazard

Model output of localized flood grids are useful in characterizing flood hazards for properties located in the Special Flood Hazard Area (SFHA—areas expected to experience a 1% or greater annual chance of flooding). However, due to the unavailability of higher return-period [i.e., recurrence interva...

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Autores principales: Mostafiz, Rubayet Bin, Rahim, Md Adilur, Friedland, Carol J., Rohli, Robert V., Bushra, Nazla, Orooji, Fatemeh
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9791062/
https://www.ncbi.nlm.nih.gov/pubmed/36579350
http://dx.doi.org/10.3389/fdata.2022.1022900
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author Mostafiz, Rubayet Bin
Rahim, Md Adilur
Friedland, Carol J.
Rohli, Robert V.
Bushra, Nazla
Orooji, Fatemeh
author_facet Mostafiz, Rubayet Bin
Rahim, Md Adilur
Friedland, Carol J.
Rohli, Robert V.
Bushra, Nazla
Orooji, Fatemeh
author_sort Mostafiz, Rubayet Bin
collection PubMed
description Model output of localized flood grids are useful in characterizing flood hazards for properties located in the Special Flood Hazard Area (SFHA—areas expected to experience a 1% or greater annual chance of flooding). However, due to the unavailability of higher return-period [i.e., recurrence interval, or the reciprocal of the annual exceedance probability (AEP)] flood grids, the flood risk of properties located outside the SFHA cannot be quantified. Here, we present a method to estimate flood hazards that are located both inside and outside the SFHA using existing AEP surfaces. Flood hazards are characterized by the Gumbel extreme value distribution to project extreme flood event elevations for which an entire area is assumed to be submerged. Spatial interpolation techniques impute flood elevation values and are used to estimate flood hazards for areas outside the SFHA. The proposed method has the potential to improve the assessment of flood risk for properties located both inside and outside the SFHA and therefore to improve the decision-making process regarding flood insurance purchases, mitigation strategies, and long-term planning for enhanced resilience to one of the world's most ubiquitous natural hazards.
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spelling pubmed-97910622022-12-27 A data-driven spatial approach to characterize the flood hazard Mostafiz, Rubayet Bin Rahim, Md Adilur Friedland, Carol J. Rohli, Robert V. Bushra, Nazla Orooji, Fatemeh Front Big Data Big Data Model output of localized flood grids are useful in characterizing flood hazards for properties located in the Special Flood Hazard Area (SFHA—areas expected to experience a 1% or greater annual chance of flooding). However, due to the unavailability of higher return-period [i.e., recurrence interval, or the reciprocal of the annual exceedance probability (AEP)] flood grids, the flood risk of properties located outside the SFHA cannot be quantified. Here, we present a method to estimate flood hazards that are located both inside and outside the SFHA using existing AEP surfaces. Flood hazards are characterized by the Gumbel extreme value distribution to project extreme flood event elevations for which an entire area is assumed to be submerged. Spatial interpolation techniques impute flood elevation values and are used to estimate flood hazards for areas outside the SFHA. The proposed method has the potential to improve the assessment of flood risk for properties located both inside and outside the SFHA and therefore to improve the decision-making process regarding flood insurance purchases, mitigation strategies, and long-term planning for enhanced resilience to one of the world's most ubiquitous natural hazards. Frontiers Media S.A. 2022-12-12 /pmc/articles/PMC9791062/ /pubmed/36579350 http://dx.doi.org/10.3389/fdata.2022.1022900 Text en Copyright © 2022 Mostafiz, Rahim, Friedland, Rohli, Bushra and Orooji. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Big Data
Mostafiz, Rubayet Bin
Rahim, Md Adilur
Friedland, Carol J.
Rohli, Robert V.
Bushra, Nazla
Orooji, Fatemeh
A data-driven spatial approach to characterize the flood hazard
title A data-driven spatial approach to characterize the flood hazard
title_full A data-driven spatial approach to characterize the flood hazard
title_fullStr A data-driven spatial approach to characterize the flood hazard
title_full_unstemmed A data-driven spatial approach to characterize the flood hazard
title_short A data-driven spatial approach to characterize the flood hazard
title_sort data-driven spatial approach to characterize the flood hazard
topic Big Data
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9791062/
https://www.ncbi.nlm.nih.gov/pubmed/36579350
http://dx.doi.org/10.3389/fdata.2022.1022900
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