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A dense cabled observation network, called the seafloor observation network for earthquakes and tsunami along the Japan Trench (S-net), was installed in 2017. Since then, the tsunami forecasting methods using S-net data have been developed vigorously. Especially, tsunami assimilation techniques or waveform inversion techniques to obtain tsunami initial conditions were newly developed. However, the ocean bottom pressure data are always contaminated by non-tsunami components such as sea-bottom acceleration change, ocean acoustic wave or P wave due to the earthquake. Therefore, tsunami components are needed to be separated from those non-tsunami components before those recent tsunami forecasting methods are applied. In this study, we developed a new tsunami forecasting method using the time average of ocean bottom pressure data (S-net). Therefore, pre-data process to eliminate non-tsunami components form original data is not necessary. To test our method, tsunamis from the various fault models distributed along the plate interface along the Japan Trench were first computed. Then, time averages of computed ocean bottom pressure data at S-net stations were calculated by changing the time average windows. Second, tsunami initial conditions, initial ocean surface deformations, were estimated using interpolation of those time averaged ocean pressure data at S-net stations. Third, forecasted tsunamis were computed from those estimated tsunami initial conditions. Finally, forecasted tsunami waveforms along the coast were compared with the tsunami waveforms computed from the fault model at first. We conclude that this tsunami forecasting method performed well in general with some underestimations of tsunami heights along the coast., The 28th IUGG General Assembly (IUGG2023) (Berlin 2023)

Early warning systems should be available for volcanic tsunamis. Present tsunami warning systems have been specialized for earthquake-generated tsunamis, and it remains challenging to rapidly evaluate the tsunami potential accompanied by a volcanic eruption and/or volcanic sector collapse. By targeting the volcanic sector collapse of Anak Krakatau in 2018, which generated a large tsunami as a case event, we develop a real-time forecasting method for tsunamis accompanied by such eruptions in Indonesia. The proposed forecasting method is based on the utilization of observation stations near the source area and a pre-computed database. In this study, six virtual observation stations were placed in the vicinity of the volcano. Then, a pre-computed waveforms database was constructed from various collapse scenarios using a numerical simulation of sector collapse and tsunami propagation. In the real application, tsunami forecasting computation will be conducted using the initial conditions which are selected from the best combination of scenarios inside the database through waveforms fitting at six observation stations. The reliability of the method was examined using three hypothetical collapse scenarios of Anak Krakatau assuming three different sliding directions. Based on numerical experiments, the forecasted tsunami along the coast of Java and Sumatra by our method resulted in satisfactory performance. Our results indicate that the combination of a pre-computed database and the existence of observation stations near the source area was able to produce appropriate tsunami forecasting for the coastal area. We conclude that our method has a reasonable predictive skill for volcanic-induced tsunamis in Anak Krakatau., The 28th IUGG General Assembly (IUGG2023) (Berlin 2023)

Additional file1: Table S1. Computational conditions. Fig. S1. Relationship between the wave period and the normalized maximum water surface elevation observed at the inlet head. Fig. S2. Comparison of observed and simulated heights for the 1611 tsunami and geometries of the sources assumed in this study.

Additional file 1: S1. Comparison of observed and computed waveforms at 106 ocean bottom pressure sensors shown in top-left figure (a). b) Black lines are observed waveforms, red lines are computed waveforms. Station name shown in bottom-left corner. Long-period waves in the first 100 min in the observed waveforms are an effect of the bandpass filter.

Additional file 1: Figure S1. Cumulative inundation area in the computation domain without the port breakwaters. Table S1. Summary of cross-sectional slices. Table S2. Summary of computational conditions.

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.