Challenges and advances in predicting waves in the nearshore · Phase averaged nearshore wave...
Transcript of Challenges and advances in predicting waves in the nearshore · Phase averaged nearshore wave...
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Challenges and advances in predicting waves in the
nearshore
Jeff Hansen (UWA), Ryan Lowe (UWA), Tom Baldock (UQ), Ian Turner (UNSW),
Alex Babanin (UMel), Mark Hemer (CSIRO), Diana Greenslade (BOM)
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Characteristics and importance of the
nearshore zone
− <25 m depth (including surf zone)
− Abrupt wave transformation, nonlinear energy transfers, wave-driven circulation
− Rapid sediment transport & morphological changes
Characteristics
- The coastline is vital to Australia’s livelihood (~90% of population lives <50 km
from the coast)
− Vital for Australia’s economy (e.g. tourism, ports).
Importance
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Importance of accurate nearshore wave
models
Beach safety Coastal planning Defence
Port operationsCoastal water quality
and ecosystemsCoastal erosion
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Background on wave models
Phase-averaged/spectral models
𝑂 10 − 1000 Wave action balance
(e.g. SWAN, WAVEWATCH,WAM)
Scale(wave periods/lengths)
Scale(wave periods/lengths)
Phase-resolving wave-flow models
𝑂 10 − 100 Mild slope equations (e.g. MILDwave)
𝑂 10 Boussinesq/non-hydrostatic models
(e.g. Funwave, Mike21 BW, HWAVE, SWASH, XBeach-NH)
𝑂 1 Computational Fluid Dynamics (e.g. OpenFOAM/ SPH) (Schmitt et al., 2012)
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Challenges of predicting waves in the
nearshore: Wave breaking
Wave breaking• Dominant dissipation
mechanism of wave energy.
• Drives wave-driven currents
and setup/setdown.
• Poorly modelled by just about
all classes of model (except
maybe CFD/SPH).
• Presently parameterized even
in phase resolving models.
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Challenges of predicting waves in the
nearshore: Complex morphology
Complex morphology• Large changes in depth are
difficult to account for.
• Morphology dictates rate of
energy dissipation and thus
setup, currents, energy
transfers.
• Even the best model is useless
without “good” bathymetry.
Gnarabup Beach, WA
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Challenges of predicting waves in the
nearshore: Bottom roughness
Bottom roughness• In environments with rough
bottoms (e.g. reefs, sea grass,
ripples), frictional dissipation
can be significant (or dominate
in some cases).
• Wave models crudely account
for roughness, primarily with a
catch all (empirical) wave
friction factor.
• Phase-resolving models can do
a bit better job (at very high
resolutions) but still not great.
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Challenges of predicting waves in the
nearshore: Energy transfers
• The offshore incident wave
spectrum is dominated by sea-
swell (periods 5-25 s).
• At the shoreline the wave
spectrum is dominated by
infragravity (IG) waves
(periods>25 s).
• IG wave dynamics dictate wave
run up and have considerable
influence on total water levels.
• Energy transfer from sea-swell to
IG waves is not modelled at all in
phase-averaged wave models.
Peak 14 s
Peak
140 s
Spectral evolution: energy transfers
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Phase averaged nearshore wave models
• Existing phase-averaged wave models (e.g. WW3 / SWAN / Mike21 SW) have
proven extremely useful for both operational and research purposes.
• Generally do well in the nearshore (<25 m) for parametric statistics (Hs, Tp, Dp).
Unstructured WW3 from BOM SWAN, Hansen et al. 2015
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Disadvantages of phase averaged
models in the nearshore
Run up
Infragravity waves
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Disadvantages of phase averaged
models in the nearshore
Diffraction
Run up
(Abanades et al., 2015)
Infragravity waves
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Phase resolving models
• Remain computationally expensive, but continually getting better.
• Have the advantage of intrinsically including nonlinear energy transfers (i.e. IG
wave generation).
• Will result in improved predictions of run up and thus potential for coastal
flooding/infrastructure damage.
•Include non-stationarity associated with waves and wave groups.
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Example: Predictions of run up
elevation
• Wave run up elevation, which determines if dune/structure overtopping occurs is
often determined via empirical relationships (e.g. Stockdon et al., 2006)
• However, these formulations have been developed and tested in a narrow range of
conditions at mostly mild sloping beaches.
Stockdon et al. 2006
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Run up predictions from a phase-
resolving model
• A variety of field and lab data
sets indicate resonance can
occur in fringing/shore attached
reef environments.
•In these cases runup at the
shoreline is strongly impacted
by the geometry of the reef-
beach system, and can be
dominated by very-low
frequency (VLF) motions.
Buckley et al. (in prep)
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Run up predictions from a phase-
resolving model
• A variety of field and lab data
sets indicate resonance can
occur in fringing/shore attached
reef environments.
•In these cases runup at the
shoreline is strongly impacted
by the geometry of the reef-
beach system, and can be
dominated by very-low
frequency (VLF) motions.
Buckley et al. (in prep)
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Sources of coastal flooding:
Laboratory (physical model) example
• Sea-swell waves (SS)
• Low frequency waves (IG and VLF)
• Wave setup
45%
wave setup
16%
IG
8%
SS
31%
VLF
Example: Hrms = 2.1 m, Tp = 19 s, hr = 1.5 m
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Sources of coastal flooding:
Laboratory (physical model) example
• Sea-swell waves (SS)
• Low frequency waves (IG and VLF)
• Wave setup
45%
wave setup
16%
IG
8%
SS
31%
VLF
Phase-averaged
models
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Offshore Hsig [m]
1.4 2.9 7.24.3 5.8 8.6
1.8
3.6
5.4
0
2%
runup
[m]
Smooth
bed
Rough
bed
Increasing wave height
Contributions to run up elevation
Varying wave height and bottom roughness
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Can phase-resolving models be made
“operational”
• Phase-averaged and resolving models can be coupled.
• For example, ~30 min SWASH simulations (a few minutes to run on a
supercomputer) using input from BOM’s AUSWAVE-R + water levels model as
a boundary condition could be run for vulnerable sites to generate total water
level forecast.
Rijnsdorp et al., 2015
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What is needed to continually improve
nearshore wave predictions in Australia?
Bathymetry
• Without good bathymetry nearshore wave models are of limited use.
Hacker, Hansen, Lowe
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What is needed to continually improve
nearshore wave predictions in Australia?
Bathymetry
• Without good bathymetry nearshore wave models are of limited use.
Geraldton Port, WA DOT/Planning
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What is needed to continually improve
nearshore wave predictions in Australia?
Boundary conditions
• Spectral (rather than parametric) wave data, from buoys and regional wave
models.
• The cost of wave buoys is decreasing rapidly.
MHL
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Concluding remarks
• Given modest computing resources, phase-resolving models can be used to
accurately predict waves and currents in the nearshore.
• Coupling phase-resolving models with regional wave/circulation models can
improve prediction/forecast of total water levels, coastal hazards/flooding,
sediment transport, and port conditions.
• Nearshore wave models will continue to improve, but will always need good
boundary conditions and bathymetry as inputs.
Hansen, 2016
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Concluding remarks
Thanks!