H210-04
Coastal Probabilistic Flood Hazard Assessment Due to Coincident Occurrence of Tropical Cyclone-Induced Surge and Precipitation

Wednesday, 16 December 2020: 16:12
Virtual
Somayeh Mohammadi1, Michelle Bensi2, Shih-Chieh Kao3, Scott DeNeale4, Elena Yegorova5, Joseph Kanney5 and Meredith L Carr6, (1)University of Maryland College Park, College Park, MD, United States, (2)University of Maryland College Park, Department of Civil and Environmental Engineering, College Park, MD, United States, (3)Oak Ridge National Laboratory, Oak Ridge, TN, United States, (4)Oak Ridge National Laboratory, Environmental Sciences Division, Oak Ridge, TN, United States, (5)Nuclear Regulatory Commission, Washington, MD, United States, (6)CRREL, Hanover, NH, United States
Recorded Presentation
Abstract:
Flood events can result from the occurrence of one flood mechanism or a combination of flood mechanisms (e.g., surge, wave, and precipitation). Traditional probabilistic flood hazard assessment approaches usually focus on a single flood mechanism. In some applications, this is not sufficient and will generally lead to underestimation of flood hazards. However, consideration of more than one flood mechanism in the analysis introduces new challenges. These challenges include understanding and characterizing the dependence between flood mechanisms (and the associated quantitative parameters) and developing models to simulate the interaction between involved flood mechanisms.

Coastal areas are exposed to simultaneous occurrence of different flooding mechanisms as a result of tropical cyclone (TC) events. In this study, changes in river discharge due to simultaneous occurrences of TC-induced precipitation and surge is analyzed in a coastal area located along the North Atlantic. A Bayesian motivated approach is used to analyze the compound effects of surge and precipitation.

While previous studies have employed copulas and other strategies to estimate joint distributions of parameters associated with compound flooding, they require specific statistical assumptions (e.g., choice of distributions and copulas) and are often limited by the length of observational records. Application of a Bayesian probabilistic approach is an alternate strategy to assess impacts from combinations of flood mechanisms. Bayesian-motivated approaches allow to include physical process through use of expert knowledge and information from numerical and other models. However, these approaches increase the computational demand.

This study explores the potential benefits and challenges in applying Bayesian-motivated approaches to estimate TC-induced compound flooding. It further identifies potential opportunities to reduce computational costs through application of surrogate modeling strategies.

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