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Probabilistic sea level rise flood projections using a localized ocean reference surface

Projecting sea level rise (SLR) impacts requires defining ocean surface variability as a source of uncertainty. We analyze ocean surface height data from a Regional Ocean Modeling System reanalysis to produce an ocean reference surface (ORS) as a proxy for the local mean higher high water. This meth...

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Autores principales: Paoa, Noah, Fletcher, Charles H., Anderson, Tiffany R., Coffman, Makena, Habel, Shellie
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9908876/
https://www.ncbi.nlm.nih.gov/pubmed/36755034
http://dx.doi.org/10.1038/s41598-023-29297-2
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author Paoa, Noah
Fletcher, Charles H.
Anderson, Tiffany R.
Coffman, Makena
Habel, Shellie
author_facet Paoa, Noah
Fletcher, Charles H.
Anderson, Tiffany R.
Coffman, Makena
Habel, Shellie
author_sort Paoa, Noah
collection PubMed
description Projecting sea level rise (SLR) impacts requires defining ocean surface variability as a source of uncertainty. We analyze ocean surface height data from a Regional Ocean Modeling System reanalysis to produce an ocean reference surface (ORS) as a proxy for the local mean higher high water. This method allows incorporation of ocean surface level uncertainty into bathtub modeling and generation of probability-based projections of SLR-induced flooding. For demonstration, we model the NOAA Intermediate, Intermediate-high and High regional SLR scenarios at three locations on the island of Oʻahu, Hawai’i. We compare 80% probability-based flood projections generated using our approach to those generated using the Tidal Constituents and Residual Interpolation (TCARI) method. TCARI is the predecessor of VDatum, the standard method used by NOAA available only for the continental U.S., Puerto Rico, and U.S. Virgin Islands. For validation, ORS pixel values representing the Honolulu tide gauge location are compared to tide gauge observations. The more realistic distribution of daily higher high water provided by ORS improves projections of SLR-induced flooding for locations where VDatum is not available. We highlight the importance of uncertainty and user-defined probability in identifying locations of flooding and pathways for additional sources of flooding.
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spelling pubmed-99088762023-02-10 Probabilistic sea level rise flood projections using a localized ocean reference surface Paoa, Noah Fletcher, Charles H. Anderson, Tiffany R. Coffman, Makena Habel, Shellie Sci Rep Article Projecting sea level rise (SLR) impacts requires defining ocean surface variability as a source of uncertainty. We analyze ocean surface height data from a Regional Ocean Modeling System reanalysis to produce an ocean reference surface (ORS) as a proxy for the local mean higher high water. This method allows incorporation of ocean surface level uncertainty into bathtub modeling and generation of probability-based projections of SLR-induced flooding. For demonstration, we model the NOAA Intermediate, Intermediate-high and High regional SLR scenarios at three locations on the island of Oʻahu, Hawai’i. We compare 80% probability-based flood projections generated using our approach to those generated using the Tidal Constituents and Residual Interpolation (TCARI) method. TCARI is the predecessor of VDatum, the standard method used by NOAA available only for the continental U.S., Puerto Rico, and U.S. Virgin Islands. For validation, ORS pixel values representing the Honolulu tide gauge location are compared to tide gauge observations. The more realistic distribution of daily higher high water provided by ORS improves projections of SLR-induced flooding for locations where VDatum is not available. We highlight the importance of uncertainty and user-defined probability in identifying locations of flooding and pathways for additional sources of flooding. Nature Publishing Group UK 2023-02-08 /pmc/articles/PMC9908876/ /pubmed/36755034 http://dx.doi.org/10.1038/s41598-023-29297-2 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Paoa, Noah
Fletcher, Charles H.
Anderson, Tiffany R.
Coffman, Makena
Habel, Shellie
Probabilistic sea level rise flood projections using a localized ocean reference surface
title Probabilistic sea level rise flood projections using a localized ocean reference surface
title_full Probabilistic sea level rise flood projections using a localized ocean reference surface
title_fullStr Probabilistic sea level rise flood projections using a localized ocean reference surface
title_full_unstemmed Probabilistic sea level rise flood projections using a localized ocean reference surface
title_short Probabilistic sea level rise flood projections using a localized ocean reference surface
title_sort probabilistic sea level rise flood projections using a localized ocean reference surface
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9908876/
https://www.ncbi.nlm.nih.gov/pubmed/36755034
http://dx.doi.org/10.1038/s41598-023-29297-2
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