Linking coastal evolution and super storm dune erosion forecasts

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Linking coastal evolution and super storm dune
erosion forecasts
L.M. Bochev-van der Burgh, K.M. Wijnberg,
S.J.M.H. Hulscher, J.P.M. Mulder, M. van
Koningsveld
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Introduction (1) erosion lines

• Safety Dutch coast expressed in erosion line
  positions
• Position erosion lines DUROS (TAW, 1995)
• Status quo long-term safety present coastal
  morphologic configuration with SLR

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Introduction (2) long-term safety

• Long-term safety effects super storms
  long-term coastal evolution
• Forecasting long-term safety different modeling
  approaches
• Tools event-scale model and large-scale model
• Case DUROS and PonTos

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Tool 1 - Extreme storm events DUROS
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Tool 2 - Long term coastal behavior PonTos

• Not suitable to perform dune erosion calculation
• How to take into account cross-shore beach and
  dune morphology?

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Research questions and methodology

• Question 1 does morphology matter?
• Research aim 1 importance dune-beach morphology
  in dune erosion calculations sensitivity DUROS
• Question 2 how to reintroduce cross-shore
  information in long-term model forecast (PonTos)?
• Research aim 2 method to account for cross-shore
  morphology in long-term model forecasts
  Empirical Orthogonal Function analysis

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DUROS sensitivity (1)
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DUROS sensitivity (2) results
Conclusion shape matters in dune erosion
calculations
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Study area
Cross-shore profiles (Jarkus database) Focus
dunes
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(No Transcript)
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Dune characterization EOF

• Empirical Orthogonal Function (EOF) analysis
  decomposition of data set in terms of shape
  functions which are determined from the data
• Data reduction technique
• Shape functions explain to large degree observed
  variance
• Subtract mean profile from real profile.
  Result deviation profile

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Mean profile shapes
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EOFs

• Large part variance explained by EOF 1
• Different dune classes different dune shapes

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Next steps

• From to
• Different dune shapes as input for DUROS
• Temporal EOFs
• Role of beach morphology
• Design optimal beach-dune morphology in terms
  of safety?

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Conclusions

• Dune morphology matters in dune erosion
  calculations
• To link long-term evolution with dune erosion
  forecasts information on morphology is needed
• EOF approach to take into account morphology in
  long-term model forecasts
• Type of management longshore position affects
  shape functions

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Thank you!
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references

• Arens, S.M., Wiersma, J, 1994, The Dutch
  Foredunes Inventory and Classification, Journal
  of Coastal Research 10, 189-202
• Van Gent, M.R.A., Coeveld, E.M., Walstra, D.J.R.,
  van de Graaff, J., Steetzel, H.J., Boers, M.,
  2006, Dune erosion tests to study the inluence of
  wave periods, San Diego, proceedings ICCE 2006
• Steetzel, H., and Wang, Z.B., 2003, Development
  and application of a large-scale morphological
  model of the Dutch coast, Tech. Rep. Alkyon and
  WLdelft hydraulics
• Van de Graaff, J.,1984 Probabilistische methoden
  bij het duinontwerp, Achtergronden van de
  TAW-Leidraad Duinafslag,Tech. Rep. Delft
  University of Technology
• TAW, 1995, Basisrapport Zandige Kust,Technische
  Adviescommissie voor de Waterkeringen, Tech. Rep.

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Model workflow
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Background EOF (1)

• Definition decomposition of a data set in terms
  of orthogonal basis functions which are
  determined from the data
• basis functions are chosen to be different from
  each other, and to account for as much variance
  as possible
• series expansion (e.g. harmonic analysis,
  Fourier analysis)

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Background EOF (2)

• X profile 1
• profile 2
• ..
• profile n
• R X mean profile deviation profile 1
• deviation profile 2
• .
• deviation profile n
• R USV (singular value decomposition)
• U(S1S2 SN)V
• US1V US2V USNV (decomposition of
  all deviations)
• series expansion of all dunes in EOFs

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Background EOF (3)

• S contains N singular values (s1, , sN), where
  sN equals 0
• R s1U1VT1 s2U2VT2 sN-1UN-1VTN-1, where
  VTi is the ith EOF
• 1st row of R deviation of first profile s1.
  U11 . (1st EOF)
• 2nd row of R deviation of second profile s1
  .U21 . (1st EOF)
• Nth row of R deviation of second profile s1
  . UN1 . (1st EOF)

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Background EOF (4)