An integration and assessment of multiple covariates of nonstationary storm surge statistical behavior by Bayesian model averaging

Projections of coastal storm surge hazard are a basic requirement for effective management of coastal risks. A common approach for estimating hazards posed by extreme sea levels is to use a statistical model, which may use a time series of a climate variable as a covariate to modulate the statistica...

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Published in:Advances in Statistical Climatology, Meteorology and Oceanography
Main Author: Wong, Tony E.
Format: Text
Language:English
Published: 2018
Subjects:
Online Access:https://doi.org/10.5194/ascmo-4-53-2018
https://ascmo.copernicus.org/articles/4/53/2018/
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spelling ftcopernicus:oai:publications.copernicus.org:ascmo71129 2023-05-15T17:31:38+02:00 An integration and assessment of multiple covariates of nonstationary storm surge statistical behavior by Bayesian model averaging Wong, Tony E. 2018-12-06 application/pdf https://doi.org/10.5194/ascmo-4-53-2018 https://ascmo.copernicus.org/articles/4/53/2018/ eng eng doi:10.5194/ascmo-4-53-2018 https://ascmo.copernicus.org/articles/4/53/2018/ eISSN: 2364-3587 Text 2018 ftcopernicus https://doi.org/10.5194/ascmo-4-53-2018 2020-07-20T16:23:01Z Projections of coastal storm surge hazard are a basic requirement for effective management of coastal risks. A common approach for estimating hazards posed by extreme sea levels is to use a statistical model, which may use a time series of a climate variable as a covariate to modulate the statistical model and account for potentially nonstationary storm surge behavior (e.g., North Atlantic Oscillation index). Previous works using nonstationary statistical approaches to assess coastal flood hazard have demonstrated the importance of accounting for many key modeling uncertainties. However, many assessments have typically relied on a single climate covariate, which may leave out important processes and lead to potential biases in the projected flood hazards. Here, I employ a recently developed approach to integrate stationary and nonstationary statistical models, and characterize the effects of choice of covariate time series on projected flood hazard. Furthermore, I expand upon this approach by developing a nonstationary storm surge statistical model that makes use of multiple covariate time series, namely, global mean temperature, sea level, the North Atlantic Oscillation index and time. Using Norfolk, Virginia, as a case study, I show that a storm surge model that accounts for additional processes raises the projected 100-year storm surge return level by up to 23 cm relative to a stationary model or one that employs a single covariate time series. I find that the total model posterior probability associated with each candidate covariate, as well as a stationary model, is about 20 %. These results shed light on how including a wider range of physical process information and considering nonstationary behavior can better enable modeling efforts to inform coastal risk management. Text North Atlantic North Atlantic oscillation Copernicus Publications: E-Journals Advances in Statistical Climatology, Meteorology and Oceanography 4 1/2 53 63
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description Projections of coastal storm surge hazard are a basic requirement for effective management of coastal risks. A common approach for estimating hazards posed by extreme sea levels is to use a statistical model, which may use a time series of a climate variable as a covariate to modulate the statistical model and account for potentially nonstationary storm surge behavior (e.g., North Atlantic Oscillation index). Previous works using nonstationary statistical approaches to assess coastal flood hazard have demonstrated the importance of accounting for many key modeling uncertainties. However, many assessments have typically relied on a single climate covariate, which may leave out important processes and lead to potential biases in the projected flood hazards. Here, I employ a recently developed approach to integrate stationary and nonstationary statistical models, and characterize the effects of choice of covariate time series on projected flood hazard. Furthermore, I expand upon this approach by developing a nonstationary storm surge statistical model that makes use of multiple covariate time series, namely, global mean temperature, sea level, the North Atlantic Oscillation index and time. Using Norfolk, Virginia, as a case study, I show that a storm surge model that accounts for additional processes raises the projected 100-year storm surge return level by up to 23 cm relative to a stationary model or one that employs a single covariate time series. I find that the total model posterior probability associated with each candidate covariate, as well as a stationary model, is about 20 %. These results shed light on how including a wider range of physical process information and considering nonstationary behavior can better enable modeling efforts to inform coastal risk management.
format Text
author Wong, Tony E.
spellingShingle Wong, Tony E.
An integration and assessment of multiple covariates of nonstationary storm surge statistical behavior by Bayesian model averaging
author_facet Wong, Tony E.
author_sort Wong, Tony E.
title An integration and assessment of multiple covariates of nonstationary storm surge statistical behavior by Bayesian model averaging
title_short An integration and assessment of multiple covariates of nonstationary storm surge statistical behavior by Bayesian model averaging
title_full An integration and assessment of multiple covariates of nonstationary storm surge statistical behavior by Bayesian model averaging
title_fullStr An integration and assessment of multiple covariates of nonstationary storm surge statistical behavior by Bayesian model averaging
title_full_unstemmed An integration and assessment of multiple covariates of nonstationary storm surge statistical behavior by Bayesian model averaging
title_sort integration and assessment of multiple covariates of nonstationary storm surge statistical behavior by bayesian model averaging
publishDate 2018
url https://doi.org/10.5194/ascmo-4-53-2018
https://ascmo.copernicus.org/articles/4/53/2018/
genre North Atlantic
North Atlantic oscillation
genre_facet North Atlantic
North Atlantic oscillation
op_source eISSN: 2364-3587
op_relation doi:10.5194/ascmo-4-53-2018
https://ascmo.copernicus.org/articles/4/53/2018/
op_doi https://doi.org/10.5194/ascmo-4-53-2018
container_title Advances in Statistical Climatology, Meteorology and Oceanography
container_volume 4
container_issue 1/2
container_start_page 53
op_container_end_page 63
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