Modeling the Morphodynamics of Coastal Responses to Extreme Events

What Shape Are We In?

Review (2022)

Authors

Christopher R. Sherwood U.S. Geological Survey-Climateand Land Use Change Branch (USGS)
Ap R. van Dongeren Deltares, IHE Delft Institute for Water Education
James Doyle US Naval Research Laboratory
Christie A. Hegermiller U.S. Geological Survey-Climateand Land Use Change Branch (USGS)
Tian-Jian Hsu University of Delaware
Tarandeep S. Kalra Integrated Statistics (contracted to the US Geological Survey)
Maitane Olabarrieta University of Florida
D. Roelvink IHE Delft Institute for Water Education, Coastal Engineering - CEG , Deltares
M.A. van der Lugt Coastal Engineering - CEG , Deltares
More Authors... External organisation
Research Group
Coastal Engineering (CEG) (TU Delft)
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Published Date

2022

Language

English

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Faculty

Civil Engineering & Geosciences

Department

Hydraulic Engineering

Research Group

Coastal Engineering

Abstract

This review focuses on recent advances in process-based numerical models of the impact of extreme storms on sandy coasts. Driven by larger-scale models of meteorology and hydrodynamics, these models simulate morphodynamics across the Sallenger storm-impact scale, including swash,collision, overwash, and inundation. Models are becoming both wider (as more processes are added) and deeper (as detailed physics replaces earlier parameterizations). Algorithms for wave-induced flows and sediment transport under shoaling waves are among the recent developments. Community and open-source models have become the norm. Observations of initial conditions (topography, land cover, and sediment characteristics) have become more detailed, and improvements in tropical cyclone and wave models provide forcing (winds, waves, surge, and upland flow) that is better resolved and more accurate, yielding commensurate improvements in model skill. We foresee that future storm-impact models will increasingly resolve individual waves, apply data assimilation, and be used in ensemble modeling modes to predict uncertainties.