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Beach slopes from satellite-derived shorelines [Coast2Coast presentation]

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Water Research Laboratory | School of Civil & Environmental
Engineering
Co-authors: Andrew Walker, Mitchell Harley, Kriste...

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• Uses Google Earth Engine
• Landsat 5, 7, 8 + Sentinel-2
• Global coverage
• 30+ years shoreline change
time-series
• Val...

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Prof. Andy Short
Beach-face slope: a critical parameter
β
• Key limitation for coastal inundation
forecasting at large spa...

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Beach slopes from satellite-derived shorelines [Coast2Coast presentation]

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How to estimate beach slopes in the absence of in situ measurements? Here are my slides from a recent presentation at the Coast2Coast webinar (organised by @Giovanni Coco, @Kristen Splinter, @Mitchell Harley) on a new technique to estimate beach slopes using satellite-derived shorelines and a global tide model.

Beach slope data available at http://coastsat.wrl.unsw.edu.au/ and preprint at https://www.essoar.org/doi/10.1002/essoar.10502903.1.

How to estimate beach slopes in the absence of in situ measurements? Here are my slides from a recent presentation at the Coast2Coast webinar (organised by @Giovanni Coco, @Kristen Splinter, @Mitchell Harley) on a new technique to estimate beach slopes using satellite-derived shorelines and a global tide model.

Beach slope data available at http://coastsat.wrl.unsw.edu.au/ and preprint at https://www.essoar.org/doi/10.1002/essoar.10502903.1.

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Beach slopes from satellite-derived shorelines [Coast2Coast presentation]

  1. 1. Water Research Laboratory | School of Civil & Environmental Engineering Co-authors: Andrew Walker, Mitchell Harley, Kristen Splinter, Ian Turner Kilian Vos Beach slopes from satellite-derived shorelines Blackpool Sands, UK Cable Beach, WA
  2. 2. • Uses Google Earth Engine • Landsat 5, 7, 8 + Sentinel-2 • Global coverage • 30+ years shoreline change time-series • Validated against long-term in-situ shoreline data in (Vos et al. 2019) • 10-15 m horizontal accuracy CoastSat open-source toolbox (https://github.com/kvos/CoastSat)
  3. 3. Prof. Andy Short Beach-face slope: a critical parameter β • Key limitation for coastal inundation forecasting at large spatial scales • Melet et al. 2018. Under-estimated wave contribution to coastal sea-level rise. Nature Climate Change: “β was set to 0.1 globally, which is a reasonable estimate, although it may vary substantially in space and time” • Needed to tidally correct satellite- derived shorelines
  4. 4. Prof. Andy Short Beach-face slope: a critical parameter • Key limitation for coastal inundation forecasting at large spatial scales • Melet et al. 2018. Under-estimated wave contribution to coastal sea-level rise. Nature Climate Change: “β was set to 0.1 globally, which is a reasonable estimate, although it may vary substantially in space and time” • Needed to tidally correct satellite- derived shorelines
  5. 5. Prof. Andy Short Beach-face slope: a critical parameter • Key limitation for coastal inundation forecasting at large spatial scales • Melet et al. 2018. Under-estimated wave contribution to coastal sea-level rise. Nature Climate Change: “β was set to 0.1 globally, which is a reasonable estimate, although it may vary substantially in space and time” • Needed to tidally correct satellite- derived shorelines Raw time-series Tidally-corrected time-series
  6. 6. Prof. Andy Short National scale by Geoscience Australia: Bishop-Taylor et al. 2018 Inter-tidal digital elevation models We need a new approach which incorporates the dynamic nature of sandy beaches
  7. 7. From time to frequency domain Let’s create a synthetic planar beach: 0.1 fixed beach slope, 1 m tide tanβ = 0.1 TR = 1m MHWS MLWS Synthetic shoreline signal Sampled weekly 25% randomly dropped Seasonal shoreline change (20 m amplitude) White-noise (5 m STD) Horizontal tidal excursion
  8. 8. From time to frequency domain Let’s create a synthetic planar beach: 0.1 fixed beach slope, 1 m tide tanβ = 0.1 TR = 1m MHWS MLWS Lomb-Scargle transform to compute Power Spectrum Density of irregularly sampled signal Msf lunisolar synodic fortnightly Sa solar annual
  9. 9. Tidal excursion White noise Seasonal signal From time to frequency domain Time Domain tanβ = 0.1 TR = 1m MHWS MLWS Frequency Domain
  10. 10. Tidal excursion White noise Seasonal signal From time to frequency domain Time Domain tanβ = 0.1 TR = 1m MHWS MLWS Frequency Domain Tidal correction
  11. 11. Tidal excursion White noise Seasonal signal From time to frequency domain Time Domain tanβ = 0.1 TR = 1m MHWS MLWS Frequency Domain Tidal correction
  12. 12. Tidal excursion White noise Seasonal signal From time to frequency domain Time Domain tanβ = 0.1 TR = 1m MHWS MLWS Frequency Domain Tidal correction
  13. 13. Tidal excursion White noise Seasonal signal From time to frequency domain Time Domain tanβ = 0.1 TR = 1m MHWS MLWS Frequency Domain Tidal correction
  14. 14. Slope estimation algorithm
  15. 15. Slope estimation algorithm
  16. 16. Slope estimation algorithm
  17. 17. Validation Variability in: • Beach-face slope (0.025-0.14) • Sediment grain size (fine sand to gravel) • Tide range (micro to macrotidal) • Wave climate
  18. 18. Validation Variability in: • Beach-face slope (0.025-0.14) • Sediment grain size (fine sand to gravel) • Tide range (micro to macrotidal) • Wave climate
  19. 19. Regional-scale application: SE Australia and California • Demonstrates that this technique can be applied over large spatial scales • A global value of tanβ = 0.1 is not a good approximation for these two coastlines • All the data can be visualised and downloaded on a web dashboard at http://coastsat.wrl.unsw.edu.au/
  20. 20. Supplementary Slides
  21. 21. Aliasing
  22. 22. Aliasing
  23. 23. Comparison with empirical formulas Bujan et al. 2019

Editor's Notes

  • Nyquist Limit is also called the Folding Frequency

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