4.5 Article

Numerical extensions to incorporate subgrid corrections in an established storm surge model

期刊

COASTAL ENGINEERING JOURNAL
卷 65, 期 2, 页码 175-197

出版社

TAYLOR & FRANCIS LTD
DOI: 10.1080/21664250.2022.2159290

关键词

Subgrid model; storm surge modeling; numerical method; SLOSH; mathematical modeling

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Inundation models, representing coastal regions with a grid of computational points, made advancements by incorporating subgrid corrections based on coarse grid solutions. This study extended a storm surge model with numerical approaches to include subgrid corrections. The extensions were limited to maintain continuity and added a finite volume method for incorporating corrections to mass and momentum equations using high-resolution ground surface elevations, as well as modifying the no-slip condition for flows in channels with widths comparable to cell size. Numerical results demonstrated improved accuracy of coastal flooding predictions with low additional computational cost.
Inundation models represent coastal regions with a grid of computational points, often with varying resolution of flow pathways and barriers. Models based on coarse grid solutions of shallow water equations have been improved recently via the use of subgrid corrections, which account for information (ground surface elevations, roughness characteristics) at smaller scales. In this work, numerical approaches of an established storm surge model are extended to include subgrid corrections. In an attempt to maintain continuity with existing users and results, model extensions were limited to those needed to provide basic subgrid capabilities, and included two major additions. First, a finite volume method is used to incorporate corrections to the mass and momentum equations using high-resolution ground surface elevations. Second, the no-slip condition imposed on the B-grid wet/dry interface in the model is modified to a slip condition to enable flows in channels with widths comparable to cell size. Numerical results demonstrate these numerical extensions can significantly enhance the accuracy of the model's predictions of coastal flooding, with low additional computational cost.

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