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Hybrid Approach for Evaluating the Erosion Potential of Coastal Storms.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Hybrid Approach for Evaluating the Erosion Potential of Coastal Storms./
作者:	Lemke, Laura.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
面頁冊數:	280 p.
附註:	Source: Dissertations Abstracts International, Volume: 83-02, Section: B.
Contained By:	Dissertations Abstracts International83-02B.
標題:	Ocean engineering. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28319104
ISBN:	9798534673166

Hybrid Approach for Evaluating the Erosion Potential of Coastal Storms.
Lemke, Laura.

Hybrid Approach for Evaluating the Erosion Potential of Coastal Storms. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 280 p.

Source: Dissertations Abstracts International, Volume: 83-02, Section: B.

Thesis (Ph.D.)--Stevens Institute of Technology, 2021.

This item must not be sold to any third party vendors.

Coastal erosion is driven by both a storm's erosion potential and by an area's vulnerability. Therefore, the problem of estimating storm-induced impacts can be approached in two-step. The first includes an assessment of erosion potential based on readily available storm parameters, while the second combines this information with highly localized parameters, describing vulnerability, to more directly predict local impacts. The work presented here addresses each of these steps by first focusing on how we characterize erosion potential and then by exploring the relationship between erosion potential and observed impacts when combined with local pre-storm morphology in a data-driven model.Here, erosion potential was evaluated using the Storm Erosion Index (SEI), a measure developed by Miller and Livermont (2008), which combines the three primary storm-related drivers of coastal erosion (wave height, total water level, and storm duration). It was used to reevaluate several decades of storms in New Jersey and investigate the spatial variation in storm intensity during Hurricane Michael (Florida Panhandle, October 2018). Results highlighted key differences between SEI, a descriptor of cumulative erosion potential, and Peak Erosion Intensity (PEI), a descriptor of maximum erosive power. The two studies suggested a link between SEI, PEI, and observed impacts. Two models, each an ensemble of decisions trees, were then developed utilizing a combination of storm intensity (SEI, PEI) and morphological parameters (e.g. dune crest elevation, dune volume, berm volume) to predict storm-induced dune erosion. The models were trained and tested on a robust data set comprised of observed morphological changes resulting from eighteen historical storms in New Jersey. The models indicated that in the prediction of dune erosion, the most important predictor variable was PEI. PEI is indicative of storm wave height, total water level, and the timing of the two respective maxima. On its own PEI was successful at distinguishing between storms most likely to result in minimal impacts (PEI 102) regardless of the beach conditions. For intensities in between, knowledge of the beach conditions was critical in estimating impacts.The developed models benefit from their simple inputs and their computational efficiency. These characteristics enable their use in a wide array of applications including hindcasting, forecasting, and climatology studies. Here, it was applied to assess the vulnerability of dunes in New Jersey to events of different intensities. Nearly 100 profiles throughout the state were tested against approximately 700 different storms. Vulnerability was evaluated under two historical sets of beach conditions as well as under design conditions dictated by the federal coastal protection projects. The results suggested the benefit in maintaining a minimum design profile in New Jersey following the construction of beach nourishment projects and highlighted the importance of both dune volume and berm width in reducing vulnerability.

ISBN: 9798534673166Subjects--Topical Terms:

660731
Ocean engineering.
Subjects--Index Terms:

Beach nourishment

Hybrid Approach for Evaluating the Erosion Potential of Coastal Storms.
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500 $a Source: Dissertations Abstracts International, Volume: 83-02, Section: B.
500 $a Advisor: Miller, Jon K.
502 $a Thesis (Ph.D.)--Stevens Institute of Technology, 2021.
506 $a This item must not be sold to any third party vendors.
520 $a Coastal erosion is driven by both a storm's erosion potential and by an area's vulnerability. Therefore, the problem of estimating storm-induced impacts can be approached in two-step. The first includes an assessment of erosion potential based on readily available storm parameters, while the second combines this information with highly localized parameters, describing vulnerability, to more directly predict local impacts. The work presented here addresses each of these steps by first focusing on how we characterize erosion potential and then by exploring the relationship between erosion potential and observed impacts when combined with local pre-storm morphology in a data-driven model.Here, erosion potential was evaluated using the Storm Erosion Index (SEI), a measure developed by Miller and Livermont (2008), which combines the three primary storm-related drivers of coastal erosion (wave height, total water level, and storm duration). It was used to reevaluate several decades of storms in New Jersey and investigate the spatial variation in storm intensity during Hurricane Michael (Florida Panhandle, October 2018). Results highlighted key differences between SEI, a descriptor of cumulative erosion potential, and Peak Erosion Intensity (PEI), a descriptor of maximum erosive power. The two studies suggested a link between SEI, PEI, and observed impacts. Two models, each an ensemble of decisions trees, were then developed utilizing a combination of storm intensity (SEI, PEI) and morphological parameters (e.g. dune crest elevation, dune volume, berm volume) to predict storm-induced dune erosion. The models were trained and tested on a robust data set comprised of observed morphological changes resulting from eighteen historical storms in New Jersey. The models indicated that in the prediction of dune erosion, the most important predictor variable was PEI. PEI is indicative of storm wave height, total water level, and the timing of the two respective maxima. On its own PEI was successful at distinguishing between storms most likely to result in minimal impacts (PEI 102) regardless of the beach conditions. For intensities in between, knowledge of the beach conditions was critical in estimating impacts.The developed models benefit from their simple inputs and their computational efficiency. These characteristics enable their use in a wide array of applications including hindcasting, forecasting, and climatology studies. Here, it was applied to assess the vulnerability of dunes in New Jersey to events of different intensities. Nearly 100 profiles throughout the state were tested against approximately 700 different storms. Vulnerability was evaluated under two historical sets of beach conditions as well as under design conditions dictated by the federal coastal protection projects. The results suggested the benefit in maintaining a minimum design profile in New Jersey following the construction of beach nourishment projects and highlighted the importance of both dune volume and berm width in reducing vulnerability.
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650 4 $a Environmental engineering. $3 548583
650 4 $a Hurricanes. $3 551051
650 4 $a Datasets. $3 3541416
650 4 $a Coasts. $3 545629
650 4 $a Beaches. $3 555979
653 $a Beach nourishment
653 $a CART analysis
653 $a Dune erosion
653 $a Erosion potential
653 $a New Jersey
653 $a Storm intensity
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710 2 $a Stevens Institute of Technology. $b Schaefer School of Engineering & Science. $3 3175777
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