Making Waves with R, Python, and Quarto
summary
Hydrodynamic model outputs from proprietary formats become a single reproducible analysis pipeline using R and Python wrapped in Quarto, exporting model grids and variables as NetCDF via Xarray and placing them in space with CartoPy. The work runs 108 scenarios (3 oceanographic × 4 nearshore × 9 times) producing 28 variables each—about 3,024 extractions automated—then plots energy dissipation and wave steepness to show a filled-offshore trench shifts peak erosion energy offshore. Some runs take ~10 minutes, others up to 6 hours for 3D.
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chapters (11)
- 0:00Shoreline modeling intro
- 1:03Hydrodynamic modeling importance
- 3:10Clayton Beach restoration
- 5:17Waves with SWAN
- 7:48Manual data extraction challenges
- 8:50Open science with Xarray and CartoPy
- 10:15Erosion risks comparison
- 11:45Spatial to temporal analysis
- 13:32Results with Quarto
- 14:38Future of hydrodynamic tools
- 15:20Q&A
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