Typhoon Jebi in 2018 caused severe damage along areas of Osaka Bay, Japan. After the storm, a Japan Society of Civil Engineers (JSCE) coastal survey group surveyed the coastal impacts and recorded watermarks inside Osaka Bay and along the Pacific. The maximum storm tide recorded by a tide gauge was 3.29 m at the Osaka Tide Station, which is a historical high for Osaka Bay. Coastal flooding consisted of both storm surge and wave run-up in almost all locations, with flooding exceeding 5 m and 12 m in elevation from mean sea level in the inner parts of Osaka Bay and along the Pacific coast, respectively. Coastal damage was severe in areas located outside of coastal barriers, such as ports and harbors, while residential areas were mainly well protected and spared due to coastal protection systems. In addition, some inland flooding occurred in the Kobe-Ashiya regions due to the storm surge propagating up several small rivers. This survey report summarizes the findings of the post-event survey by JSCE and analyzes the relation between maximum water level and resulting damage.

Empirical tsunami fragility curves are developed based on a Bayesian framework by accounting for uncertainty of input tsunami hazard data in a systematic and comprehensive manner. Three fragility modeling approaches, i.e. lognormal method, binomial logistic method, and multinomial logistic method, are considered, and are applied to extensive tsunami damage data for the 2011 Tohoku earthquake. A unique aspect of this study is that uncertainty of tsunami inundation data (i.e. input hazard data in fragility modeling) is quantified by comparing two tsunami inundation/run-up datasets (one by the Ministry of Land, Infrastructure, and Transportation of the Japanese Government and the other by the Tohoku Tsunami Joint Survey group) and is then propagated through Bayesian statistical methods to assess the effects on the tsunami fragility models. The systematic implementation of the data and methods facilitates the quantitative comparison of tsunami fragility models under different assumptions. Such comparison shows that the binomial logistic method with un-binned data is preferred among the considered models; nevertheless, further investigations related to multinomial logistic regression with un-binned data are required. Finally, the developed tsunami fragility functions are integrated with building damage-loss models to investigate the influences of different tsunami fragility curves on tsunami loss estimation. Numerical results indicate that the uncertainty of input tsunami data is not negligible (coefficient of variation of 0.25) and that neglecting the input data uncertainty leads to overestimation of the model uncertainty.

Due to ongoing climate change, overtopping risk is increasing. In order to have effective countermeasures, it is useful to understand overtopping processes in details. In this study overtopping flow on a dike with gentle and shallow foreshores are investigated using a non-hydrostatic wave-flow model, SWASH (an acronym of Simulating WAves till SHore). The SWASH model in 2DV (i.e., flume like configuration) is first validated using the data of long crested wave cases with second order wave generation in the physical model test conducted. After that it is used to produce overtopping flow in different wave conditions and bathymetries. The results indicated that the overtopping risk is better characterized by the time dependent h (overtopping flow depth) and u (overtopping flow velocity) instead of h_max (maximum overtopping flow depth) and u_max (maximum overtopping flow velocity), which led to overestimation of the risk. The time dependent u and h are strongly influenced by the dike configuration, namely by the promenade width and the existence of a vertical wall on the promenade: the simulation shows that the vertical wall induces seaward velocity on the dike which might be an extra risk during extreme events.

Nankai–Tonankai megathrust earthquakes and tsunamis pose significant risks to coastal communities in western and central Japan. Historically, this seismic region hosted many major earthquakes, and the current national tsunami hazard assessments in Japan consider megathrust events as those having moment magnitudes between 9.0 and 9.1. In responding to the lack of rigorous uncertainty analysis, this study presents an extensive tsunami hazard assessment for the Nankai–Tonankai Trough events, focusing on the southwestern Pacific region of Japan. A set of 1000 kinematic earthquake rupture models is generated via stochastic source modelling approaches, and Monte Carlo tsunami simulations are carried out by considering high-resolution grid data of 10 m and coastal defence structures. Significant advantages of the stochastic tsunami simulation methods include the enhanced capabilities to quantify the uncertainty associated with tsunami hazard assessments and to effectively visualize the results in an integrated manner. The results from the stochastic tsunami simulations can inform regional and local tsunami risk reduction actions in light of inevitable uncertainty associated with such tsunami hazard assessments and complement conventional deterministic tsunami scenarios and their hazard predictions, such as those developed by the Central Disaster Management Council of the Japanese Cabinet Office.

This study examined the validity of using an artificial neural network (ANN) to predict tsunami water levels at several locations in Osaka Bay. The metropolitan areas of Osaka Bay have short warning times for tsunamis; a real-time tsunami forecast will allow for improved evacuation plans and will reduce the effect of these coastal disasters. Different tsunami conditions changing the relative strength of the asperities and background sources, such as fault displacement, fault length, fault width, fault slope, depth from sea bottom, and strike, were used for training the ANN; the data sets were generated by applying the nonlinear shallow water wave equations assuming different earthquake fault models. The linear activation function produced optimal results for the ANN output units, and the tangent-sigmoid function yielded good results for the ANN hidden layer units. The Levenberg-Marquardt method with Bayesian regulation was employed for the training of the ANN. Output from the trained ANN was the prelimina...

Experiments were conducted at a 1:20 length scale in a large tsunami flume to measure wave evolution and pressures on and around structural elements. The water surface profiles of waves propagating...