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Exploring the Potential for Multivariate Fragility Representations to Alter Flood Risk Estimates.

Robert A Jane1, David J Simmonds1, Ben P Gouldby2

  • 1School of Engineering, University of Plymouth, Plymouth, Devon, UK.

Risk Analysis : an Official Publication of the Society for Risk Analysis
|June 21, 2018
PubMed
Summary

This study introduces a new multivariate statistical model for flood risk analysis, improving coastal defense failure probability assessments. The approach better captures combined hydraulic loadings, leading to more accurate vulnerability predictions and effective flood risk management.

Keywords:
Copulaflood risk analysisfragilitymultidimensional fragility

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Area of Science:

  • Coastal engineering
  • Hydraulic engineering
  • Environmental risk assessment

Background:

  • Traditional flood risk analysis often uses univariate models, limiting failure probability assessments to single hydraulic loading variables.
  • Coastal defenses face multiple simultaneous loadings, where the combination, not just individual variables, dictates failure probability.
  • Existing models struggle to accurately represent the complex interplay of factors influencing coastal defense structural integrity.

Purpose of the Study:

  • To develop and apply a multivariate statistical model for deriving extreme nearshore loading conditions.
  • To create a 3D fragility representation for shingle beaches, incorporating water level, wave height, and period.
  • To compare the accuracy of the new multivariate approach with existing methods in flood risk analysis.

Main Methods:

  • Utilized a Gaussian copula to model dependencies between beach geometric parameters.
  • Simulated beach profiles based on the copula.
  • Determined failure probability using a reformulated Bradbury barrier inertia parameter model under multivariate hydraulic loads.

Main Results:

  • Substantial differences in annual failure probability distributions were observed at one site compared to existing methods.
  • At another site, the beach exhibited vulnerability only after significant crest height reduction, with a mean annual failure probability similar to current predictions.
  • The study highlights the impact of multivariate loading conditions on coastal defense performance.

Conclusions:

  • Multivariate statistical models offer a more precise representation of structural vulnerability in flood risk analysis.
  • The proposed approach enhances the accuracy of failure probability assessments for coastal defenses.
  • Further application of multivariate methods is recommended for more effective flood risk management strategies.