Efficient modeling of wave generation and propagation in a semi-enclosed estuary

This is a Preprint and has not been peer reviewed. The published version of this Preprint is available: https://doi.org/10.1016/j.ocemod.2023.102231. This is version 1 of this Preprint.

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Authors

Sean Christopher Hunter Crosby, Kees Nederhoff, Nathan VanArendonk, Eric Grossman

Abstract

Accurate, and high-resolution wave statistics are critical for regional hazard mapping and planning. However, long-term simulations at high spatial resolution are often computationally prohibitive. Here, multiple rapid frameworks including fetch-limited, look-up-table (LUT), and linear propagation are combined and tested in a large estuary exposed to both remotely (swell) and locally generated waves. Predictions are compared with observations and a traditional SWAN implementation coupled to a regional hydrodynamic model. Fetch-limited and LUT approaches both perform well where local winds dominate with errors about 10-20% larger than traditional SWAN predictions. Combinations of these rapid approaches with linear propagation methods where remotely generated energy is present also perform well with errors 0-20% larger than traditional SWAN predictions. Model-model comparisons exhibit lower variance than comparisons to observations suggesting that, while model implementation impacts prediction skill, model boundary conditions (winds, offshore waves) may be a dominant source of error. Overall results suggest that with a relatively small loss in prediction accuracy, simulations computation cost can be significantly reduced (by 2-4 orders of magnitude) allowing for high resolution and long-term predictions to adequately define regional wave statistics.

DOI

https://doi.org/10.31223/X5R94V

Subjects

Oceanography, Oceanography and Atmospheric Sciences and Meteorology, Physical Sciences and Mathematics

Keywords

ocean waves, prediction, Validation, reduced-computation

Dates

Published: 2023-03-14 12:15

License

CC BY Attribution 4.0 International

Additional Metadata

Data Availability (Reason not available):
Upon request