Coastal and pluvial compound flooding of surge, wave, tide, rainfall-runoff, sewer flow, and seawall failure at coastal urban areas

ABSTRACT

The first part of this seminar focuses on a study that investigated the primary drivers of compound flooding at Kansai International Airport during Typhoon Jebi (2018) using the newly developed SuWAT model. This numerical tool integrates coastal modules with pluvial modules using a 10-meter high-resolution nesting scheme. Key findings identified wave overtopping as the dominant flood driver, contributing 90.8% of the total flood volume. Seawall failure accounted for 7.5%, while sewer reverse flows from seawater and rainwater contributed 1.7% and 0.26%, respectively. This novel framework provides a comprehensive tool for predicting multi-hazard flooding in coastal cities, with future enhancements focusing on two-way coupling to better understand terrestrial flood dynamics.

The second part of the seminar presents a study that enhanced the SuWAT model by integrating a Morison-type vegetation module that accounts for both drag and inertia forces. Validated against mangrove physical experiments, the results demonstrate that wave energy dissipation is primarily driven by current velocity rather than water elevation. A key finding highlights the role of vegetation morphology: in shallow water, the complex, emergent mangrove root systems cause high attenuation rates, whereas efficiency decreases as increasing water depth renders the vegetation relatively submerged.

BIOGRAPHY

Dr. Sooyoul Kim is an Associate Professor at the Center for Water Cycle Marine Environment and Disaster Management (CWMD) and the Department of Civil Environmental Engineering and Architecture at Kumamoto University, Japan. Prior to joining Kumamoto University in 2020, he served as an Assistant Professor at Tottori University. He earned his PhD in Engineering from Kyoto University (2007), focusing on the effects of tidal variation on storm surges. With an academic career spanning over twenty years, his expertise lies at the intersection of coastal engineering, numerical modeling, and machine learning.