Benchmarking of numerical models for wave overtopping at dikes with shallow mildly sloping foreshores

Accuracy versus speed

Journal Article (2020)
Author(s)

Christopher H. Lashley (TU Delft - Hydraulic Structures and Flood Risk)

Barbara Zanuttigh (University of Bologna)

Jeremy D. Bricker (University of Michigan, TU Delft - Hydraulic Structures and Flood Risk)

Jentsje van der Meer (IHE Delft Institute for Water Education, Van der Meer Consulting Bv)

Corrado Altomare (Universiteit Gent, Universitat Politecnica de Catalunya)

Tomohiro Suzuki (Flanders Hydraulics Research, TU Delft - Environmental Fluid Mechanics)

Volker Roeber (Université de Pau et des Pays de l'Adour)

Patrick Oosterlo (TU Delft - Hydraulic Structures and Flood Risk)

Research Group
Hydraulic Structures and Flood Risk
DOI related publication
https://doi.org/10.1016/j.envsoft.2020.104740
More Info
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Publication Year
2020
Language
English
Related content
Research Group
Hydraulic Structures and Flood Risk
Volume number
130
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Abstract

Practitioners often employ diverse, though not always thoroughly validated, numerical models to directly or indirectly estimate wave overtopping (q) at sloping structures. These models, broadly classified as either phase-resolving or phase-averaged, each have strengths and limitations owing to the physical schematization of processes within them. Models which resolve the vertical flow structure or the full wave spectrum (i.e. sea-swell (SS) and infragravity (IG) waves) are considered more accurate, but more computationally demanding than those with approximations. Here, we assess the speed-accuracy trade-off of six well-known models for estimating q, under shallow foreshore conditions. The results demonstrate that: i) q is underestimated by an order of magnitude when IG waves are neglected; ii) using more computationally-demanding models does not guarantee improved accuracy; and iii) with empirical corrections to incorporate IG waves, phase-averaged models like SWAN can perform on par, if not better than, phase-resolving models but with far less computational effort.