beach modelling iv: ensemble modelling
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Beach modelling IV: Ensemble modelling. Adonis F. Velegrakis Dept Marine Sciences University of the Aegean. Synopsis. Why model ensembles Method Effectiveness and benefits 4 Tool explained. 1 Why model ensembles. - PowerPoint PPT PresentationTRANSCRIPT
Beach modelling IV: Ensemble modelling
Adonis F. VelegrakisAdonis F. VelegrakisDept Marine SciencesDept Marine Sciences
University of the AegeanUniversity of the Aegean
Synopsis
1. Why model ensembles
2. Method
3. Effectiveness and benefits
4 Tool explained
1 Why model ensembles
Development of a new methodology/tool for beach Development of a new methodology/tool for beach management which may diagnose/predict beach retreat:management which may diagnose/predict beach retreat:
Under different long-term and short-term sea level rise Under different long-term and short-term sea level rise
And for different morphological, hydrodynamic and And for different morphological, hydrodynamic and sedimentological conditions sedimentological conditions
And which can be used locally and globally and will be better And which can be used locally and globally and will be better than existing tools than existing tools
Beach retreat is assessed through the numerical models Beach retreat is assessed through the numerical models Leont’yev Leont’yev και και SBEACHSBEACH and the static models Edelman, and the static models Edelman, Bruun and Dean. Bruun and Dean.
These models have been already run for linear profiles as well as non-These models have been already run for linear profiles as well as non-linear (natural) profiles and for many environmental conditions linear (natural) profiles and for many environmental conditions
The models can be run individually, or better use them as ensembles The models can be run individually, or better use them as ensembles ((Rixen et al., 2007) asRixen et al., 2007) as::
(i)(i) Leont’yevLeont’yev,, SBEACH SBEACH, , EdelmanEdelman: : ‘short-term’‘short-term’ ( (storm surgesstorm surges))
(ii)(ii) Bruun and DeanBruun and Dean ‘long-term’ ASLR‘long-term’ ASLR))
2 Method
2 Method (cont.)
The models have been run for different conditions (> 19000 experiments) The models have been run for different conditions (> 19000 experiments) (results are stored in the data base estimator)(results are stored in the data base estimator)::
beach slope (1/10, 1/15, 1/20, 1/25, 1/30), (1/10, 1/15, 1/20, 1/25, 1/30), wave conditions wave conditions (Η=1, 1.5, 2, 2.5, 3, 4, 5, 6 m και T= 3 - 14 s) (Η=1, 1.5, 2, 2.5, 3, 4, 5, 6 m και T= 3 - 14 s)
sediment size sediment size (d(d5050=0.2, 0.33, 0.50, 0.80, 1, 2 και 5 mm)=0.2, 0.33, 0.50, 0.80, 1, 2 και 5 mm)
For For 1144 scenaria of sea level rise scenaria of sea level rise (0.038, 0.05, 0.10, 0.15, 0.22, 0.30, 0.40, (0.038, 0.05, 0.10, 0.15, 0.22, 0.30, 0.40, 0.50, 0.75, 1, 1.25, 1.5, 2 και 3 m).0.50, 0.75, 1, 1.25, 1.5, 2 και 3 m).
The model ensembles have been also used for natural profiles from DUCK, The model ensembles have been also used for natural profiles from DUCK, n. Carolina and Christchurch Bay, UK (> 3000 experiments).n. Carolina and Christchurch Bay, UK (> 3000 experiments).
The results from natural and linear profiles have been compared. The results from natural and linear profiles have been compared. Moreover, the results have been compared with those by a Boussinesq Moreover, the results have been compared with those by a Boussinesq
model (> 1100 experiments)model (> 1100 experiments)
3 Effectiveness and benefits
The tool has been shown to be quite effective when was compared with results from the state-of-the-art Boussinesq model , which has been validated by physical experiments
The major advantage of the present tool is that requires minimum field information for an initial assessment.
If however such information is available then the range of the prediction envelope can be reduced
The tool consists of 3 platforms [1] Coastal retreat estimator on the basis of an existing data base; [2] Coastal retreat estimator- static models; and [3] Coastal retreat estimator-dynamic models
4 The tool [1]
4 The tool [2]
4 The tool [3]
Model Scale Spatial resolution
CostL. :<$10,000 Μ :<$50,000
H : >$100,000
Weaknesses and requirements
Inundation model
Local-Global VariableDEM: 90 m – 10 km
LowHigh unceratinty
No sediment transport
SimCLIM Local-Global VariableLow-Medium Data requirement
DIVA National-Global
Coastal sections (mean length
70km) DEM: 90 m
MediumData requirement /
know-how requirement
SLAMMLocal-regional
(<1 km2 - 100,000 km2)≈ 10 – 100 mDEM : 90 m
Low-MediumData requirement / know-how requirement
BTELSSLocal-regional
(<1 km2 - 100,000 km2)1 km2 High
High data requirementl high
know-how requiremet high computer power
requirement
Existing tools
Beach retreat under storm surges
Fig. 1 Estimations of beach retreat (16384 experiments) by the models Leont’yev, SBEACH και Edelman. x-axis, sea level rise; y-axis beach retreat
Lower limit:s= 0.54α2+7.08α - 0.31(R2 = 0.99)
Higher limit: s= 1.23α2+29.52α+4.71(R2 = 0.99)
Where s beach retreat (in m) and α sea level rise (in m).
Lower limit s= -0.001α2+7.9α + 0.1(R2 = 0.99)
Higher limit : s= 5Ε-05α2+28α+5.2 (R2 = 0.99)
Where s beach retreat (in m) and α sea level rise (in m).
Fig. 2 Estimations of beach retreat using the models Bruun and Dean (2752 experiments). x-axis, sea level rise; y-axis beach retreat; yellow dash line, mean limits
Long-term beach retreat
Fig. 3. Result ranges from models of the short-term ensemble for natural profile (mean profile from Delilah experiment in DUCK). x-axis, sea level rise; y-axis beach retreat; black dash line, mean limits
Beach retreat under storm surges
Natural profiles- Results not stored in the data base estimator
Comparison between linear (γραμμικές) and natural profiles
Leont’ yev SBEACH Edelman
30% 30% maximum deviation maximum deviation ~10% ~10% maximum deviationmaximum deviation ~16% ~16% maximum deviationmaximum deviation
11 Bed slopes in natural and linear profiles have been compared at the swash Bed slopes in natural and linear profiles have been compared at the swash zoneszones
Fig. 4. Comparisons of the results from 3 models (short-term ensemble) between linear (blue lines) and natural profiles- SandyDuck experiment (red lines) x-axis, sea level rise; y-axis beach retreat.
Bruun Dean
6.76.7% % maximum deviationmaximum deviation11 6.36.3% % maximum deviationmaximum deviation
Comparison between linear (γραμμικές) and natural profiles for the long-term ensemble
Fig. 5. Comparisons of the results from 3 models (long-term ensemble) between linear (blue lines) and natural profiles- SandyDuck experiment (red lines) x-axis, sea level rise; y-axis beach retreat. 11 Bed slopes in natural and linear profiles have been compared at Bed slopes in natural and linear profiles have been compared at the active profile (Bruun) and the surf zone (Dean)the active profile (Bruun) and the surf zone (Dean)
The unified ensemble shows similar range with the Boussinesq model
6.36.3% % maximum deviation maximum deviation
Fig. 6. The mean limits of the predictions of the unified ensemble 2 together with comparisons of the predictions of the short- and long-term ensembles and the unified ensemble with the Boussinesq model predictions.
Mean limits of the unified Mean limits of the unified ensemble ensemble
Lower limitLower limit:: ss= 0.33 α= 0.33 α22 + 7.4 α – 0.14 + 7.4 α – 0.14 ((RR22 =0.99=0.99))
Higher limit Higher limit ::ss= 0.74 α= 0.74 α22 + 28.9 α + 4.9 + 28.9 α + 4.9 ((RR22 =0.99=0.99))
H = 1.20 m, T = 5s, d50 = 0.3 mm
SlopeSlope 1/10 1/10 Slope Slope 1/151/15
Fig. 7Fig. 7. Comparison of Boussinesq results with the experiments of . Comparison of Boussinesq results with the experiments of Dette et al. Dette et al. (1998) ((1998) (HYDRALABHYDRALAB)). Blue line, initial profle; red line final profile; dots . Blue line, initial profle; red line final profile; dots experimental data. (Monioudi, 2011). experimental data. (Monioudi, 2011).
slopeslope 1/20 1/20