Probabilistic Sea Level Rise Flood Projections Using a Localized Ocean Reference Surface

dc.contributor.advisor Fletcher, Charles H. Paoa Kannegiesser, Noah Atariki
dc.contributor.department Earth and Planetary Sciences 2022-03-03T19:55:05Z 2022-03-03T19:55:05Z 2021 M.S.
dc.subject Environmental geology
dc.subject bathtub model
dc.subject flooding
dc.subject reanalysis data
dc.subject sea level rise
dc.subject uncertainty
dc.title Probabilistic Sea Level Rise Flood Projections Using a Localized Ocean Reference Surface
dc.type Thesis
dcterms.abstract Projecting sea level rise (SLR) impacts requires defining ocean surface variability as asource of uncertainty. But tidal gauge data for this purpose are sparse. We analyze data from a Regional Ocean Modelling System (ROMS) reanalysis for the region surrounding the main Hawaiian Islands to incorporate the uncertainty of the ocean surface in mapping SLR flood probabilities. To validate the use of the ROMS reanalysis data we scanned it for daily highest high water using a 24-hour window and scanned the Honolulu tidal station data over the same time period (2007-2017) and at the same sampling interval (3hr). The mean higher high water (MHHW) value calculated with ROMS (0.296 ± 0.115 m) closely matches the MHHW calculated from the Honolulu tidal station data (0.304 ± 0.108 m above MSL). By analyzing the ocean surface height component of the ROMS reanalysis, we create an ocean surface reference (ORS) as a proxy for MHHW. We model the NOAA Intermediate, Intermediate-high and High regional SLR scenarios provided by Sweet et al. (2017) for the years 2050 and 2100 at three field sites around Oꞌahu; Waikīkī, Hauꞌula, Haleꞌiwa. We calculate a probability density function (PDF) by convolving the PDF of water level derived from the ROMS reanalysis data with the PDF of error associated with a digital elevation model of the study sites. The resulting joint-PDF of flood depth allows us to create two types of probability-based flood projections: (1) Maps illustrating varying flood depths for a given probability threshold and, (2) maps illustrating varying probability for a specific flood depth. We compare 80% probability flood projections using our ORS approach to projections using the TCARI grid, the standard NOAA method. We highlight the importance of uncertainty and user-defined probability in identifying pixels that function as tipping points distinguishing flooding styles.
dcterms.extent 27 pages
dcterms.language en
dcterms.publisher University of Hawai'i at Manoa
dcterms.rights All UHM dissertations and theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission from the copyright owner.
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