Tsunami damage to ports: cataloguing damage to create fragility functions from the 2011 Tohoku event

Modern tsunami events have highlighted the vulnerability of port structures to these high-impact but infrequent occurrences. However, port planning rarely includes adaptation measures to address tsunami hazards. The 2011 Tohoku tsunami presented us with an opportunity to characterise the vulnerability of port industries to tsunami impacts. Here, we provide a spatial assessment and photographic interpretation of freely available data sources. Approximately 5000 port structures were assessed for damage and stored in a database. Using the newly developed damage database, tsunami damage is quantified statistically for the first time, through the development of damage fragility functions for eight common port industries. In contrast to tsunami damage fragility functions produced for buildings from an existing damage database, our fragility functions showed higher prediction accuracies (up to 75 % accuracy). Pre-tsunami earthquake damage was also assessed in this study and was found to influence overall damage assessment. The damage database and fragility functions for port industries can inform structural improvements and mitigation plans for ports against future events.

Evolution of Pneumatic Tsunami Simulators–From Concept to Proven Experimental Technique

This paper describes the evolution through three generations of pneumatic Tsunami Simulators for physical model tests of realistic tsunami. The pneumatic method, originally developed for tidal modeling in the Fifties, has been modernized to generate extraordinarily long waves in a controlled manner, with accurate reproduction of recorded free-surface tsunami field data. The paper describes how the simulator designs were developed and how they performed in the laboratory. Example results are presented from selected research studies that have validated their performance and then used to quantify tsunami effects. Having described each of the first, second, and third generation Tsunami Simulators, the paper discusses how to calibrate the wave generation control to model tsunami with model periods 20–240 s duration (equivalent to 2–20 min duration in prototype at 1:50 scale), many of which are far too long to “fit into the test flume.” The evolution of a composite approach to wave calibration is described with examples from second and third generation devices, demonstrated by successful simulation of both the 2004 Boxing Day, and the 2011 Great Eastern Japan (Tohoku) Tsunami at 1:50 scale.

Rapid Assessment of Tsunami Offshore Propagation and Inundation with D-FLOW Flexible Mesh and SFINCS for the 2011 Tōhoku Tsunami in Japan

This study demonstrates the skills of D-FLOW Flexible Mesh (FM) and SFINCS (Super-Fast INundation of CoastS) in combination with the Delft Dashboard Tsunami Toolbox to numerically simulate tsunami offshore propagation and inundation based on the example of the 2011 Tōhoku tsunami in Japan. Caused by a megathrust earthquake, this is one of the most severe tsunami events in recent history, resulting in vast inundation and devastation of the Japanese coast. The comparison of the simulated with the measured offshore water levels at four DART buoys located in the Northwestern Pacific Ocean shows that especially the FM but also the SFINCS model accurately reproduce the observed tsunami propagation. The inundation observed at the Sendai coast is well reproduced by both models. All in all, the model outcomes are consistent with the findings gained in earlier simulation studies. Depending on the specific needs of future tsunami simulations, different possibilities for the application of both models are conceivable: (i) the exclusive use of FM to achieve high accuracy of the tsunami offshore propagation, with the option to use an all-in-one model domain (no nesting required) and to add tsunami sediment dynamics, (ii) the combined use of FM for the accurate simulation of the tsunami propagation and of SFINCS for the accurate and time efficient simulation of the onshore inundation and (iii) the exclusive use of SFINCS to get a reliable picture of the tsunami propagation and accurate results for the onshore inundation within seconds of computational time. This manuscript demonstrates the suitability of FM and SFINCS for the rapid and reliable assessment of tsunami propagation and inundation and discusses use cases of the three model combinations that form an important base for tsunami risk management.

The 2011 Tohoku Tsunami from the Sky: A Review on the Evolution of Artificial Intelligence Methods for Damage Assessment

The Tohoku tsunami was a devastating event that struck North-East Japan in 2011 and remained in the memory of people worldwide. The amount of devastation was so great that it took years to achieve a proper assessment of the economical and structural damage, with the consequences still being felt today. However, this tsunami was also one of the first observed from the sky by modern satellites and aircrafts, thus providing a unique opportunity to exploit these data and train artificial intelligence methods that could help to better handle the aftermath of similar disasters in the future. This paper provides a review of how artificial intelligence methods applied to case studies about the Tohoku tsunami have evolved since 2011. We focus on more than 15 studies that are compared and evaluated in terms of the data they require, the methods used, their degree of automation, their metric performances, and their strengths and weaknesses.

Monitoring of Tsunami/Earthquake Damages by Polarimetric Microwave Remote Sensing Technique

Polarization characterizes the vector state of EM wave. When interacting with polarized wave, rough natural surface often induces dominant surface scattering; building also presents dominant double-bounce scattering. Tsunami/earthquake causes serious destruction just by inundating the land surface and destroying the building. By analyzing the change of surface and double-bounce scattering before and after disaster, we can achieve a monitoring of damages. This constitutes one basic principle of polarimetric microwave remote sensing of tsunami/earthquake. The extraction of surface and double-bounce scattering from coherency matrix is achieved by model-based decomposition. The general four-component scattering power decomposition with unitary transformation (G4U) has been widely used in the remote sensing of tsunami/earthquake to identify surface and double-bounce scattering because it can adaptively enhance surface or double-bounce scattering. Nonetheless, the strict derivation in this chapter conveys that G4U cannot always strengthen the double-bounce scattering in urban area nor strengthen the surface scattering in water or land area unless we adaptively combine G4U and its duality for an extended G4U (EG4U). Experiment on the ALOS-PALSAR datasets of 2011 great Tohoku tsunami/earthquake demonstrates not only the outperformance of EG4U but also the effectiveness of polarimetric remote sensing in the qualitative monitoring and quantitative evaluation of tsunami/earthquake damages.

Potential of deep predictive coding networks for spatiotemporal tsunami wavefield prediction

Data assimilation is a powerful tool for directly forecasting tsunami wavefields from the waveforms recorded at dense observational stations like S-Net without the need to know the earthquake source parameters. However, this method requires a high computational load and a quick warning is essential when a tsunami threat is near. We propose a new approach based on a deep predictive coding network for forecasting spatiotemporal tsunami wavefields. Unlike the previous data assimilation method, which continuously computes the wavefield when observed data are available, we use only a short sequence from previously assimilated wavefields to forecast the future wavefield. Since the predictions are computed through matrix multiplication, the future wavefield can be estimated in seconds. We apply the proposed method to simple bathymetry and the 2011 Tohoku tsunami. The results show that our proposed method is very fast (1.6 s for 32 frames of prediction with 1-min interval) and comparable to the previous data assimilation. Therefore, the proposed method is promising for integration with data assimilation to reduce the computational cost.