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    Multihazard Scenarios for Analysis of Compound Extreme Events

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    Name:
    Sadegh_et_al-2018-Geophysical_ ...
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    Description:
    Final Published version
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    Author
    Sadegh, Mojtaba
    Moftakhari, Hamed
    Gupta, Hoshin V.
    Ragno, Elisa
    Mazdiyasni, Omid
    Sanders, Brett
    Matthew, Richard
    AghaKouchak, Amir
    Affiliation
    Univ Arizona, Dept Hydrol & Atmospher Sci
    Issue Date
    2018-06-16
    Keywords
    compound extremes
    multihazard scenario
    copula
    Bayesian inference
    uncertainty assessment
    
    Metadata
    Show full item record
    Publisher
    AMER GEOPHYSICAL UNION
    Citation
    Sadegh, M., Moftakhari, H., Gupta, H. V., Ragno, E., Mazdiyasni, O., Sanders, B., Matthew, R., & AghaKouchak, A. (2018). Multihazard scenarios for analysis of compound extreme events. Geophysical Research Letters, 45, 5470–5480. https://doi.org/10.1029/2018GL077317
    Journal
    GEOPHYSICAL RESEARCH LETTERS
    Rights
    ©2018. American Geophysical Union. All Rights Reserved.
    Collection Information
    This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.
    Abstract
    Compound extremes correspond to events with multiple concurrent or consecutive drivers (e.g., ocean and fluvial flooding, drought, and heat waves) leading to substantial impacts such as infrastructure failure. In many risk assessment and design applications, however, multihazard scenarios of extremes and compound events are ignored. In this paper, we review the existing multivariate design and hazard scenario concepts and introduce a novel copula-based weighted average threshold scenario for an expected event with multiple drivers. The model can be used for obtaining multihazard design and risk assessment scenarios and their corresponding likelihoods. The proposed model offers uncertainty ranges of most likely compound hazards using Bayesian inference. We show that the uncertainty ranges of design quantiles might be large and may differ significantly from one copula model to the other. We also demonstrate that the choice of marginal and copula functions may profoundly impact the multihazard design values. A robust analysis should account for these uncertainties within and between multivariate models that translate into multihazard design quantiles.
    Note
    6 month embargo; published online: 11 May 2018
    ISSN
    00948276
    DOI
    10.1029/2018GL077317
    Version
    Final published version
    Sponsors
    California Energy Commission [500-15-005]; National Science Foundation Hazards-SEES Program [DMS 1331611]; National Oceanic and Atmospheric Administration Ecological Effects of Sea Level Rise Program [NA16NOS4780206]
    Additional Links
    http://doi.wiley.com/10.1029/2018GL077317
    ae974a485f413a2113503eed53cd6c53
    10.1029/2018GL077317
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