DSD-INT 2017 Beware: Bayesian Estimation Of Wave Attack In Reef Environments - Pearson
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The document presents a methodology using Bayesian estimation to predict wave-induced flooding in reef environments, addressing the challenges posed by varying offshore conditions and coral morphology. It details simulations conducted with the XBeach non-hydrostatic model and validates the predictions with limited field data. The findings suggest that the model can assist in early-warning systems and climate change impact assessments, highlighting the importance of certain parameters like offshore wave conditions and reef width for flood hazard estimation.
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
- 6. Results: XBeach Non-Hydrostatic 6 Runup trends reflect field and laboratory observations (a) (b) (c) (d) (e) (f) (g)
- 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!