Exploring the Combined Risk of Sea Level Rise and Storm Surges Using a Bayesian Network Model: Application to Saemangeum Seawall

Abstract

Jung, M.-K.; Kim, J.-Y.; Lee, B., and Kwon, H.-H., 2021. Exploring the combined risk of sea level rise and storm surges using a Bayesian network model: Application to Saemangeum seawall. In: Lee, J.L.; Suh, K.-S.; Lee, B.; Shin, S., and Lee, J. (eds.), Crisis and Integrated Management for Coastal and Marine Safety. Journal of Coastal Research, Special Issue No. 114, pp. 186–190. Coconut Creek (Florida), ISSN 0749-0208. In terms of natural hazards, typhoon-induced storm surge along with heavy rainfall has been recognized as the most frequently reported hazard among water-related disasters in the coastal areas in South Korea. Moreover, it has been widely acknowledged that the frequency and intensity of typhoons (or abnormal low-pressure systems) are likely to increase over time due to the potential impact of climate change. A risk analysis covering different loading conditions has been a tool for flood risk management in these contexts. We propose a Bayesian network-based generic risk analysis tool for flood defense systems such as a levee, dike, and seawall. On the other hand, this study explores various failure modes of flood defense structures for the use of risk analysis. In this study, the proposed modeling framework is applied to Saemangeum seawall in South Korea. Based on the literature review, various failure modes for Saemangeum seawall was identified, and the selected failure mode is then translated into nodes in the Bayesian network framework. The failure probability was estimated quantitatively by computing the limit state equation, which is composed of a set of random variables. Moreover, we investigated a Bayesian network model to assess the impact of compounding risk associated with revetment erosion of the seawall from sea-level rise informed by climate change scenarios. A further discussion on the role of the uncertainty for overall risk is provided.