Using a two-step framework for the investigation of storm impacted beach/dune erosion

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Dissanayake, P.; Brown, J.; Sibbertsen, P.; Winter, C.: Using a two-step framework for the investigation of storm impacted beach/dune erosion. In: Coastal Engineering 168 (2021), 103939. DOI: https://doi.org/10.1016/j.coastaleng.2021.103939

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To cite the version in the repository, please use this identifier: https://doi.org/10.15488/12387

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Sum total of downloads: 64




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Abstract: 
Long-term coastal management of beach/dune systems requires the definition and assessment of storm events. This study presents a framework using statistical analyses and numerical modelling (XBeach) to characterize storm events and investigate their impact on beach/dune erosion. The method is developed using exemplary data from Formby Point on the Sefton coast (UK), which has a complex beach morphology and frontal dunes. Relevant storm events are classified by a versatile univariate response function taking into account both nearshore water levels and offshore significant wave heights (Hs). It is shown that compared to the established storm classification (Hs ≥ 2.5 m) 35% more storm events that are relevant for beach/dune erosion are identified. Also the events exceed critical conditions for longer durations, and cause greater erosion impact (12%) along the beach/dune profile. The proposed classification of storm events thus captures relevant events for the storm erosion and can inform coastal management strategies. This framework is widely applicable to other beach/dune systems. © 2021 The Authors
License of this version: CC BY-NC-ND 4.0 Unported
Document Type: Article
Publishing status: publishedVersion
Issue Date: 2021
Appears in Collections:Wirtschaftswissenschaftliche Fakultät

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pos. country downloads
total perc.
1 image of flag of Germany Germany 31 48.44%
2 image of flag of United States United States 18 28.12%
3 image of flag of Tanzania, United Republic of Tanzania, United Republic of 4 6.25%
4 image of flag of China China 3 4.69%
5 image of flag of Austria Austria 2 3.12%
6 image of flag of Taiwan Taiwan 1 1.56%
7 image of flag of Russian Federation Russian Federation 1 1.56%
8 image of flag of New Zealand New Zealand 1 1.56%
9 image of flag of Israel Israel 1 1.56%
10 image of flag of France France 1 1.56%
    other countries 1 1.56%

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