Efficient modeling of wave generation and propagation in a semi-enclosed estuary
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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.
Study Area
| Publication type | Article |
|---|---|
| Publication Subtype | Journal Article |
| Title | Efficient modeling of wave generation and propagation in a semi-enclosed estuary |
| Series title | Ocean Modeling |
| DOI | 10.1016/j.ocemod.2023.102231 |
| Volume | 184 |
| Year Published | 2023 |
| Language | English |
| Publisher | Elsevier |
| Contributing office(s) | Pacific Coastal and Marine Science Center |
| Description | 102231, 19 p. |
| Country | Canada, United States |
| State | British Columbia, Washington |
| Other Geospatial | Salish Sea |
| Google Analytic Metrics | Metrics page |