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Estimation of River Channel Shape Us...

McDermott, Riley,

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Estimation of River Channel Shape Using Big Data-Driven Approaches /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Estimation of River Channel Shape Using Big Data-Driven Approaches // Riley McDermott.
Author:	McDermott, Riley,
Description:	1 electronic resource (88 pages)
Notes:	Source: Masters Abstracts International, Volume: 86-07.
Contained By:	Masters Abstracts International86-07.
Subject:	Hydrologic sciences. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31634087
ISBN:	9798302332073

Estimation of River Channel Shape Using Big Data-Driven Approaches /
McDermott, Riley,

Estimation of River Channel Shape Using Big Data-Driven Approaches /Riley McDermott. - 1 electronic resource (88 pages)

Source: Masters Abstracts International, Volume: 86-07.

Enhanced precision in channel geometric parameters has the potential to significantly improve hydrologic modeling and the generation of flood inundation mapping (FIM). Emerging datasets containing large-scale measurements of channel morphological and hydraulic characteristics offer pathways for developing novel models estimating channel cross-sectional geometry parameters, including shape. This project develops first-order channel geometry parameter models based on large-scale datasets for the CONtiguous United States (CONUS). Four methods of reconstructing simplified channel cross-sections, using different channel shapes, from observational at-a-station data are compared to observed channel cross-sections. Statistical analysis of the channel geometry parameters cross-sectional area, wetted perimeter, and hydraulic radius are used to identify the most accurate reconstruction approach. Two reconstruction methods use the common trapezoidal channel shape and the other two implement curved channel shape parameters. The Moramarco's w reconstruction, which derives its channel shape parameter based on the ratio of mean to maximum depth observations at a site, is identified as the best channel reconstruction. The cross-sections generated from this optimal reconstruction are used for the development of models of the channel geometry parameters cross-sectional area and hydraulic radius, which can be applied for the NHDPlus V2.1 stream network to provide bathymetry estimations across the entire CONUS.

English

ISBN: 9798302332073Subjects--Topical Terms:

3168407
Hydrologic sciences.
Subjects--Index Terms:

Bathymetry

Estimation of River Channel Shape Using Big Data-Driven Approaches /
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245 1 0 $a Estimation of River Channel Shape Using Big Data-Driven Approaches / $c Riley McDermott.
264 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2024
300 $a 1 electronic resource (88 pages)
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500 $a Source: Masters Abstracts International, Volume: 86-07.
500 $a Advisors: Cohen, Sagy Committee members: Liu, Hongxing; Johnson, Michael.
502 $b M.S. $c The University of Alabama $d 2024.
520 $a Enhanced precision in channel geometric parameters has the potential to significantly improve hydrologic modeling and the generation of flood inundation mapping (FIM). Emerging datasets containing large-scale measurements of channel morphological and hydraulic characteristics offer pathways for developing novel models estimating channel cross-sectional geometry parameters, including shape. This project develops first-order channel geometry parameter models based on large-scale datasets for the CONtiguous United States (CONUS). Four methods of reconstructing simplified channel cross-sections, using different channel shapes, from observational at-a-station data are compared to observed channel cross-sections. Statistical analysis of the channel geometry parameters cross-sectional area, wetted perimeter, and hydraulic radius are used to identify the most accurate reconstruction approach. Two reconstruction methods use the common trapezoidal channel shape and the other two implement curved channel shape parameters. The Moramarco's w reconstruction, which derives its channel shape parameter based on the ratio of mean to maximum depth observations at a site, is identified as the best channel reconstruction. The cross-sections generated from this optimal reconstruction are used for the development of models of the channel geometry parameters cross-sectional area and hydraulic radius, which can be applied for the NHDPlus V2.1 stream network to provide bathymetry estimations across the entire CONUS.
546 $a English
590 $a School code: 0004
650 4 $a Hydrologic sciences. $3 3168407
650 4 $a Geomorphology. $3 542703
650 4 $a Geographic information science. $3 3432445
650 4 $a Hydraulic engineering. $3 613609
653 $a Bathymetry
653 $a Channel geometry
653 $a Flood inundation mapping
653 $a Hydraulic radius
653 $a Channel reconstruction
690 $a 0388
690 $a 0484
690 $a 0370
690 $a 0218
710 2 $a The University of Alabama. $b Geography. $e degree granting institution. $3 3820915
720 1 $a Cohen, Sagy $e degree supervisor.
773 0 $t Masters Abstracts International $g 86-07.
790 $a 0004
791 $a M.S.
792 $a 2024
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31634087