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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ftdoajarticles:oai:doaj.org/article:6546e9238527488a97ce29d97586f620 2023-05-15T17:31:40+02:00 An integration and assessment of multiple covariates of nonstationary storm surge statistical behavior by Bayesian model averaging T. E. Wong 2018-12-01T00:00:00Z https://doi.org/10.5194/ascmo-4-53-2018 https://doaj.org/article/6546e9238527488a97ce29d97586f620 EN eng Copernicus Publications https://www.adv-stat-clim-meteorol-oceanogr.net/4/53/2018/ascmo-4-53-2018.pdf https://doaj.org/toc/2364-3579 https://doaj.org/toc/2364-3587 doi:10.5194/ascmo-4-53-2018 2364-3579 2364-3587 https://doaj.org/article/6546e9238527488a97ce29d97586f620 Advances in Statistical Climatology, Meteorology and Oceanography, Vol 4, Pp 53-63 (2018) Oceanography GC1-1581 Meteorology. Climatology QC851-999 Probabilities. Mathematical statistics QA273-280 article 2018 ftdoajarticles https://doi.org/10.5194/ascmo-4-53-2018 2022-12-31T04:07:22Z 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. Article in Journal/Newspaper North Atlantic North Atlantic oscillation Directory of Open Access Journals: DOAJ Articles Advances in Statistical Climatology, Meteorology and Oceanography 4 1/2 53 63 |
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Open Polar |
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Directory of Open Access Journals: DOAJ Articles |
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English |
topic |
Oceanography GC1-1581 Meteorology. Climatology QC851-999 Probabilities. Mathematical statistics QA273-280 |
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Oceanography GC1-1581 Meteorology. Climatology QC851-999 Probabilities. Mathematical statistics QA273-280 T. E. Wong An integration and assessment of multiple covariates of nonstationary storm surge statistical behavior by Bayesian model averaging |
topic_facet |
Oceanography GC1-1581 Meteorology. Climatology QC851-999 Probabilities. Mathematical statistics QA273-280 |
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 |
Article in Journal/Newspaper |
author |
T. E. Wong |
author_facet |
T. E. Wong |
author_sort |
T. E. Wong |
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 |
publisher |
Copernicus Publications |
publishDate |
2018 |
url |
https://doi.org/10.5194/ascmo-4-53-2018 https://doaj.org/article/6546e9238527488a97ce29d97586f620 |
genre |
North Atlantic North Atlantic oscillation |
genre_facet |
North Atlantic North Atlantic oscillation |
op_source |
Advances in Statistical Climatology, Meteorology and Oceanography, Vol 4, Pp 53-63 (2018) |
op_relation |
https://www.adv-stat-clim-meteorol-oceanogr.net/4/53/2018/ascmo-4-53-2018.pdf https://doaj.org/toc/2364-3579 https://doaj.org/toc/2364-3587 doi:10.5194/ascmo-4-53-2018 2364-3579 2364-3587 https://doaj.org/article/6546e9238527488a97ce29d97586f620 |
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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1766129344872185856 |