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DSD-INT 2018 Assessment of runup reduction potential due to coral reef restoration - Pearson

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Presentation by Stuart Pearson, TU Delft/Deltares, at the XBeach User Day 2018, during Delft Software Days - Edition 2018. Thursday, 15 November 2018, Delft.

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DSD-INT 2018 Assessment of runup reduction potential due to coral reef restoration - Pearson

  1. 1. 1 Assessment of Runup Reduction Potential Due to Coral Reef Restoration Stuart Pearson1,2, Marlies van der Lugt1, Ap van Dongeren1, Gerben Hagenaars1, Andreas Burzel1, Boris Ton van Zanten3
  2. 2. Background 2  Reef-fronted tropical coastlines are faced with an increasing threat of wave-induced flooding  Coral reef restoration may increase the resilience of the reef  Increases friction and reduces runup  Commissioned by the World Bank to investigate the runup reduction potential on three islands in the Seychelles (USGS, 2014)
  3. 3. Background: Seychelles 3
  4. 4. Motivation 4 (Source: Tobias Alt, 2008) Waves breaking on reef crest La Digue Waves completely dissipated at shoreline
  5. 5. Motivation 5  Catastrophic bleaching hit Seychelles reefs in 1998  Mean coral cover reduced to <11% of its historic extent  Coral bleaching reduces “structural complexity” = hydraulic roughness
  6. 6. Objective 6  Identify priority sites with high coastal flood risk and strong potential for flood risk reduction via coral restoration
  7. 7. Methodology 7 1. Define ~300 transects around the Seychelles 2. Estimate reef geometry via remote sensing 3. Feed reef properties and hydrodynamic forcing into the XBeach-based BEWARE system 4. Estimate runup as a function of changes in coral cover  Runup potential = proxy for coastal flooding
  8. 8. Methodology: Define Transects 8 Mahe Praslin La Digue• 327 transects schematized for use in BEWARE system • Focused on fringing reefs
  9. 9. Methodology: BEWARE 9  BEWARE (Bayesian Estimation of Wave Attack in Reef Environments)  Generated synthetic dataset using XBeach Non-Hydrostatic model  Expanded existing dataset to cover Seychelles parameter space (Pearson et al., 2017)
  10. 10. Methodology: BEWARE 10  To feed BEWARE, we used remote sensing- derived bathymetry and hydrodynamic forcing represented by 9 wave and water level conditions  Bayesian Network (Netica) then used to query database Variable Values Hs0 3, 4, 5, 6, 7 Hs0/L0 0.05, 0.1, 0.2 η0 0, 0.5, 1, 1.5, 2, 2.5, 3 Wreef 0, 50, 100, 150, 200, 250, 300, 350, 400, 500 βf 0.05, 0.1, 0.2, 1.0 βb 0.01, 0.05, 0.1, 0.5 cf 0.001, 0.01, 0.05, 0.5 (Pearson et al., 2017)
  11. 11. Methodology: Bayesian Network Example 11  What is the most likely runup for given conditions?  Prior prediction (no additional information):  Updated (Posterior) Prediction (with additional information): High Tide Hs=3.0 m Tp=18 s Wreef = 150 m Cf = 0.05 Reef Slope = 1/2 Beach Slope = 1/10 1 to 2 m Runup (83% chance) All Possible Hydrodynamic Conditions All Reefs in Database Equal Probability (~20% chance) Prunup (%) Runup Prunup (%) Runup
  12. 12. Methodology: Bayesian Network Example 12  What is the most likely runup for given conditions?  Prior prediction (no additional information):  Updated (Posterior) Prediction (with additional information): High Tide Hs=3.0 m Tp=18 s Wreef = 150 m Cf = 0.001 Reef Slope = 1/2 Beach Slope = 1/10 3 to 4 m Runup (83% chance) All Possible Hydrodynamic Conditions All Reefs in Database Equal Probability (~20% chance) Prunup (%) Runup Prunup (%) RunupWhat if roughness changes due to coral die-off?
  13. 13. Methodology: Remote-Sensed Bathymetry 13 Near Infrared Red signal Landward Shoreline Seaward  Methodology developed by Hagenaars et al. (2017)  Shoreline position  Normalized Difference Water Index  Reef break  Near Infra Red signal Breakerline Reef Width
  14. 14. Reef Width: 222 m Depth: 1.5 m Methodology: Remote-Sensed Bathymetry 14  Reef depth and offshore slope found from aerosol (green and red bands)  Depths are referenced to MSL using tide information Βf: 1/28
  15. 15. Interpreting the Results 15  A: Increasing roughness strongly reduces runup  Higher restoration potential  B: Increasing roughness has little effect on runup  Low restoration potential  C: Profile is highly sensitive to changes in roughness  Little effect for small increases  Strong runup reduction for high roughness
  16. 16. Results: La Digue – 4 m Waves 16 East Side • Narrower reefs • Medium cover has little effect West Side • Wider reefs • Medium cover has some effect • High cover is most effective General • Low cover has no effect Large reduction at priority site
  17. 17. Results: La Digue – 6 m Waves (+2 m) 17 Increasing Wave Height • Differences between “no-high” roughness become more pronounced
  18. 18. Results: Mahe 18 • Runup reduction shows high spatial variation due to alongshore differences in: • Wave climate • Reef geometry • Depth • Width • Slopes
  19. 19. Limitations: Determining Roughness 19  Translating baseline coral cover/structural complexity to hydrodynamic roughness  Tested sensitivity to range of roughness coefficients, representing relative degrees of coral coverage  Cf = 0.001 (sandy beach)  Cf = 0.1 (high coral coverage)
  20. 20. Reef Crest ???? Limitations: Remote Sensing 20  Remote sensing-derived bathymetry is less reliable for reefs without a defined crest and reef flat
  21. 21. Conclusions 21  Generally, low coral cover offers little benefit as flood protection when compared with medium or high coral cover.  No point in partial restorations (from flood risk perspective)  As wave height increases, the absolute differences between high and no (or low) roughness also increase.  “Lost protection” of a dead reef will become more apparent during storms
  22. 22. Conclusions 22  Shows the protective value of healthy, rough reefs  Demonstrates viability of BEWARE system for rapid assessments of flood risk
  23. 23. What Next? 23  Site-specific data collection and modelling is necessary  Optimize cross-shore placement of restorations  Improve handling of complex bathymetry  Remote Sensing: Move beyond “classical” fringing reef  XBeach Modelling: Schematize 2D effects, diverse profile shapes
  24. 24. Thank you for your time! s.g.pearson@tudelft.nl 24

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