Natural Hazards and Earth System Sciences (Sep 2023)

Sensitivity analysis of erosion on the landward slope of an earthen flood defense located in southern France submitted to wave overtopping

  • C. Houdard,
  • C. Houdard,
  • A. Poupardin,
  • P. Sergent,
  • A. Bennabi,
  • J. Jeong,
  • J. Jeong

DOI
https://doi.org/10.5194/nhess-23-3111-2023
Journal volume & issue
Vol. 23
pp. 3111 – 3124

Abstract

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The study aims to provide a complete analysis framework applied to an earthen dike located in Camargue, France. This dike is regularly submitted to erosion on the landward slope that needs to be repaired. Improving the resilience of the dike calls for a reliable model of damage frequency. The developed system is a combination of copula theory, empirical wave propagation, and overtopping equations as well as a global sensitivity analysis in order to provide the return period of erosion damage on a set dike while also providing recommendations in order for the dike to be reinforced as well as the model to be self-improved. The global sensitivity analysis requires one to calculate a high number of return periods over random observations of the tested parameters. This gives a distribution of the return periods, providing a more general approach to the behavior of the dike. The results show a return period peak around the 2-year mark, close to reported observation. With the distribution being skewed, the mean value is higher and is thus less reliable as a measure of dike safety. The results of the global sensitivity analysis show that no particular category of dike features contributes significantly more to the uncertainty of the system. The highest contributing factors are the dike height, the critical velocity, and the coefficient of seaward slope roughness. These results underline the importance of good dike characterization in order to improve the predictability of return period estimations. The obtained return periods have been confirmed by current in situ observations, but the uncertainty increases for the most severe events due to the lack of long-term data.