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