A Bayesian probabilistic approach for impacts of sea level rise on coastal engineering design practice

The real impact of sea level rise (SLR) on coastal and ocean engineering infrastructures is anticipated to be significant. The associated huge costs of coastal flooding and lasting socio-economic crisis would require planners, decision-makers and engineers to use effectively all available knowledge and data to optimize flood defense protection systems. In this paper, we introduce a Bayesian approach that integrates knowledge from previous performance history of structures (data, models and analysis) with more recent information from the simulations performed using the latest data, methods and modeling technology. These two sets of knowledge and information on past and present status of system contain various uncertainties and errors introduced by different input sources and analysis methods. We employ the concept of global uncertainty to quantify the total uncertainty affecting the design, functionality and maintenance of coastal flood defense systems in order to reduce damages resulting from the SLR and other extreme water level changes (e.g., storm surges by hurricanes, increased precipitation and ice melting). Our objective in this paper is to show coastal engineers how to use the prior knowledge with the most current information to improve the safety of flood defense systems. We demonstrate the proposed method in an example for the failure analysis of the 17th Street Flood Wall in New Orleans, where we estimate uncertainties that affected the design of the I-wall. We provide a methodology that integrates the contribution of SLR with all other available prior information to determine uncertainty levels for failure analysis of the flood defenses. Various uncertainties are present in engineering practice, explicit or implicit, and quantification of these is essential to safety and efficacy of coastal flood protection systems.