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Consideration of Elevation Uncertainty in Coastal Flood Models

Author: Christopher Joseph Amante; Waleed Abdalati
Publisher: Ann Arbor : ProQuest Dissertations & Theses, 2018.
Dissertation: Ph.D. University of Colorado at Boulder 2018.
Edition/Format:   Thesis/dissertation : Document : Thesis/dissertation : eBook   Computer File : English
Publication:Dissertation Abstracts International, 80-02B(E)
Summary:
Digital elevation models (DEMs) are critical components of coastal flood models. Both present-day storm surge models and future flood risk models require these representations of the Earth's elevation surface to delineate potentially flooded areas. The National Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Information (NCEI) develops DEMs for United States' coastal communities by
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Details

Genre/Form: Theses
Material Type: Document, Thesis/dissertation, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Christopher Joseph Amante; Waleed Abdalati
ISBN: 9780438383968 0438383966
OCLC Number: 1081337730
Language Note: English.
Notes: Source: Dissertation Abstracts International, Volume: 80-02(E), Section: B.
Advisors: Waleed Abdalati Committee members: Barbara Buttenfield; Weiqing Han; Stefan Leyk; Seth Spielman.
Description: 1 electronic resource (150 pages)
Responsibility: Christopher Joseph Amante.

Abstract:

Digital elevation models (DEMs) are critical components of coastal flood models. Both present-day storm surge models and future flood risk models require these representations of the Earth's elevation surface to delineate potentially flooded areas. The National Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Information (NCEI) develops DEMs for United States' coastal communities by seamlessly integrating bathymetric and topographic data sets of disparate age, quality, and measurement density. A current limitation of the NOAA NCEI DEMs is the accompanying non-spatial metadata, which only provide estimates of the measurement uncertainty of each data set utilized in the development of the DEM.

Vertical errors in coastal DEMs are deviations in elevation values from the actual seabed or land surface, and originate from numerous sources, including the elevation measurements, as well as the datum transformation that converts measurements to a common vertical reference system, spatial resolution of the DEM, and interpolative gridding technique that estimates elevations in areas unconstrained by measurements. The magnitude and spatial distribution of vertical errors are typically unknown, and estimations of DEM uncertainty are a statistical assessment of the likely magnitude of these errors. Estimating DEM uncertainty is important because the uncertainty decreases the reliability of coastal flood models utilized in risk assessments.

I develop methods to estimate the DEM cell-level uncertainty that originates from these numerous sources, most notably, the DEM spatial resolution, to advance the current practice of non-spatial metadata with NOAA NCEI DEMs. I then incorporate the estimated DEM cell-level uncertainty, as well as the uncertainty of storm surge models and future sea-level rise projections, in a future flood risk assessment for the Tottenville neighborhood of New York City to demonstrate the importance of considering DEM uncertainty in coastal flood models. I generate statistical products from a 500-member Monte Carlo ensemble that incorporates these main sources of uncertainty to more reliably assess the future flood risk. The future flood risk assessment can, in turn, aid mitigation efforts to reduce the vulnerability of coastal populations, property, and infrastructure to future coastal flooding.

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