Presentation by Stuart G. Pearson (Deltares, TU Delft) at the XBeach X (10th Year Anniversary) Conference, during Delft Software Days - Edition 2017. Thursday, 2 November 2017, Delft.
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DSD-INT 2017 Beware: Bayesian Estimation Of Wave Attack In Reef Environments - Pearson
1. 1
BEWARE: Bayesian estimation of
wave attack in reef environments
Stuart Pearson1,2, Ap van Dongeren1, Curt Storlazzi3,
Marion Tissier2, Ad Reniers2, John Phillip Lapidez4,
Yoshimitsu Tajima4, Takenori Shimozono4
2. Background
2
Reef-fronted tropical coastlines are faced with an
increasing threat of wave-induced flooding
However, this flooding is challenging to predict due to:
Variations in offshore hydrodynamic forcing
Vast range of coral reef morphologies
Complexity of coral reef hydrodynamics
(USGS, 2014)
3. Methodology: XBeach Non-Hydrostatic
3
Limited field data available!
Need to generate synthetic dataset
Used XBeach Non-Hydrostatic model
Validated for reefs using lab data of Demirbilek et al (2007)
Varied reef morphology and hydrodynamic forcing
Carried out ~174,000 simulations (7 params, 3-7 variations)
Idealized Reef
Profile
4. Methodology: Bayesian Networks Example
4
What is the likelihood of flooding on an island 2 m
above sea level?
Prior prediction (no additional information):
Updated (Posterior) Prediction (with additional information):
High Tide
Hs=3.0 m
Tp=18 s
Wreef = 150 m
Cf = 0.01
Reef Slope = 1/2
Beach Slope = 1/10
Runup > 2 m
(83% chance)
All Possible
Hydrodynamic
Conditions
All Reefs in
Database
Runup > 2 m
(40% chance)
Prunup
(%)
Runup
Prunup
(%)
Runup
5. Probabilistic graphical model
Visually represents conditional probabilities through a
series of nodes and connections
“Trained” using real or synthetic dataset
Fast and proven in other coastal contexts
Methodology: Bayesian Network
5
𝐻 𝑉𝐿𝐹
Hydrodynamic
Forcing
Reef
Morphology
Hazard Outputs
𝜂0
𝜂
𝛽𝑓
𝛽 𝑏
𝑐𝑓
𝑊𝑟𝑒𝑒𝑓
𝐻0
𝐻0 𝐿0
𝐻 𝑆𝑆
𝐻IG
𝑅2
%
𝑇 𝑚−1,0
7. Results: Bayesian Network Validation
7
Limited cases available
BN can predict majority
of tested cases
R2% predictions vary
SS, LF predictions good
Setup overestimated
Need more field data!
8. Ongoing: Application to Typhoon Meranti in Philippines
8
Ran 86 392 new cases in XBNH based on Typhoon Meranti
Compared with runup measurements from Tajima et al. (2017)
Tendency to overestimate runup
Due to simplifications made in schematizing the model?
1D XBeach model overestimates IG component
We do not account for directional effects or refraction/diffraction
Batanes
9. Results: Bayesian Network Predictive Skill
9
How well can the network predict cases it has not
seen before?
Performed k-fold validation on XBNH dataset
Good predictions of high runup events
Less predictive skill for VLF waves resonance?
10. Results: Log-Likelihood Ratios
10
Which variables are most important for predictions?
Withhold each input variable one-at-a-time from the BN
More complex
process?
Less
important
More important
Prediction
using
entire
network
11. Conclusions
11
XBNH can reproduce wave transformation processes on
fringing reefs, including resonant reef flat amplification
BEWARE shows high predictive skill for flooding
conditions from the XBNH model
Validated for a limited number of case studies
Offshore wave conditions, water level, and reef width are
the most important parameters to estimate flood hazards
Having knowledge of the reef roughness or beach slope
appears less important
BEWARE can form the basis for early-warning systems
and scenario assessment applications on reef-lined coasts
e.g. SLR, wave climate, reef restoration scenarios
Couple with 2D inundation models and damage estimators
12. Recommendations
12
Collect more validation data
Small-Scale
Field measurements of hydrodynamics (especially runup)
e.g. Estimates of storm impacts and flooding
Large-Scale
Need more data on reef morphology & offshore forcing
e.g. Remote sensing of reef flats, offshore waves
Use BEWARE as a tool in real-life cases
Early warning systems
Climate change impact assessments
(USGS, 2016)
13. Further Information
13
BEWARE Database:
Available online soon (or by request: s.g.pearson@tudelft.nl)
Related Publications:
Pearson, S. G.; C.D. Storlazzi; A.R. van Dongeren; M.F.S. Tissier; and
A.J.H.M. Reniers. 2017. “A Bayesian-Based System to Assess Wave-
Driven Flooding Hazards on Coral Reef-Lined Coasts.” Journal of
Geophysical Research: Oceans (In Press).
Tajima, Y.; J.P. Lapidez; J. Camelo; M. Saito; Y. Matsuba; T. Shimozono;
D. Bautista; M. Turiano; and E Cruz. 2017. “Post-Disaster Survey of
Storm Surge and Waves Along the Coast of Batanes, the
Philippines, Caused by Super Typhoon Meranti/Ferdie.” Coastal
Engineering Journal 59 (1): 1750009.
Thank you for your time!