A large eruption of the Hunga Tonga-Hunga Haʻapai volcano in Tonga on January 15, 2022 generated air–sea coupled tsunamis observed at the ocean-bottom pressure sensor network along the Japan Trench (S-net) in Japan. Initial tsunamis from the 2022 Tonga eruption, detected by 106 ocean-bottom pressure sensors, were well modeled by an air–sea coupled tsunami simulation, with a simple atmospheric pressure pulse as sine function, having a half-wavelength of 300 km and a peak amplitude of 2 hPa. A one-dimensional air–sea coupled tsunami simulation having a simple bathymetry shows that an input atmospheric pressure pulse with a short half-wavelength of 50 km, which is shorter than the length of the ocean bottom slopes, caused an amplitude increase via the Proudman resonance effect near the deep trench. The wavefront distortion due to the separation of the air–sea coupled wave propagating with a speed of 312 m/s and tsunami propagating with that of $$\sqrt{gd}$$gd , where g is gravity acceleration and d is the ocean depth, is also significant near the shore. In contrast, these effects are not significant for the half-wavelength of the input atmospheric pressure pulse of 300 km. These results indicate that the air–sea coupled tsunami propagating through the trench is sensitive to the wavelength of an atmospheric pressure pulse. Graphical Abstract

A new method was developed to reproduce the tsunami height distribution in and around the source area, at a certain time, from a large number of ocean bottom pressure sensors, without information on an earthquake source. A dense cabled observation network called S-NET, which consists of 150 ocean bottom pressure sensors, was installed recently along a wide portion of the seafloor off Kanto, Tohoku, and Hokkaido in Japan. However, in the source area, the ocean bottom pressure sensors cannot observe directly an initial ocean surface displacement. Therefore, we developed the new method. The method was tested and functioned well for a synthetic tsunami from a simple rectangular fault with an ocean bottom pressure sensor network using 10 arc-min, or 20 km, intervals. For a test case that is more realistic, ocean bottom pressure sensors with 15 arc-min intervals along the north–south direction and sensors with 30 arc-min intervals along the east–west direction were used. In the test case, the method also functioned well enough to reproduce the tsunami height field in general. These results indicated that the method could be used for tsunami early warning by estimating the tsunami height field just after a great earthquake without the need for earthquake source information.

Offshore real‐time ocean bottom networks of seismometers and ocean bottom pressure (OBP) gauges have been recently established such as DONET and S‐net around the Japanese islands. One of their purposes is to practice rapid and accurate tsunami forecasting. Near‐fault OBP records, however, are always contaminated by nontsunami components such as sea‐bottom acceleration change until an earthquake stops its fault or sea‐floor motions. This study proposes a new method to separate tsunami and ocean bottom displacement components from coseismic OBP records in a real‐time basis. Associated with the Off‐Mie earthquake of 2016 April 1, we first compared OBP data with acceleration, velocity, and displacement seismograms recorded by seismometers at common ocean bottom sites in both time and frequency domains. Based on this comparison, we adopted a band‐pass filter of 0.05–0.15 Hz to remove ocean‐bottom acceleration components from the OBP data. Resulting OBP waveforms agree well with the tsunami components estimated by a 100‐s low‐pass filter with records of several hundred seconds in length. Our method requires only an early portion of a given OBP record after 30 s of an origin time in order to estimate its tsunami component accurately. Our method enhances early tsunami detections with near‐fault OBP data; that is, it will make a tsunami forecasting system faster and more reliable than the previous detection schemes that require data away from source regions or after coseismic motions are over.

Throughout history, accidental hypothermia has accompanied natural disasters in cold, temperate, and even subtropical regions. We conducted a non-systematic review of the causes and means of preventing accidental hypothermia after natural disasters caused by avalanches, earthquakes, tsunamis, and floods. Before a disaster occurs, preventive measures are required, such as accurate disaster risk analysis for given areas, hazard mapping and warning, protecting existing structures within hazard zones to the greatest extent possible, building structures outside hazard zones, and organising rapid and effective rescue. After the event, post hoc analyses of failures, and implementation of corrective actions will reduce the risk of accidental hypothermia in future disasters.

A dense cabled observation network, called the seafloor observation network for earthquakes and tsunami along the Japan Trench (S-net), was installed in Japan. This study aimed to develop a near-real time tsunami source estimation technique using the ocean bottom pressure data observed at those sensors in S-net. Synthetic pressure waveforms at those sensors were computed for 64 earthquake tsunami scenarios with magnitude ranging between M8.0 and M8.8. The pressure waveforms within a time window of 500 s after an earthquake were classified into three types. Type 1 has the following pressure waveform characteristic: the pressure decreases and remains low; sensors exhibiting waveforms associated with Type 1 are located inside a co-seismic uplift area. The pressure waveform characteristic of Type 2 is that one up-pulse of a wave is within the time window; sensors exhibiting waveforms associated with Type 2 are located at the edge of the co-seismic uplift area. The other pressure waveforms are classified as Type 3. Subsequently, we developed a method to estimate the uplift area using those three classifications of pressure waveforms at sensors in S-net and a method to estimate earthquake magnitude from the estimated uplift area using a regression line. We systematically applied those methods for two cases of previous large earthquakes: the 1952 Tokachi-oki earthquake (Mw8.2) and the 1968 Tokachi-oki earthquake (Mw8.1). The locations of the large computed uplift areas of the earthquakes were well defined by the estimated ones. The estimated magnitudes of the 1952 and 1968 Tokachi-oki earthquakes from the estimated uplift area were 8.2 and 7.9, respectively; they are almost consistent with the moment magnitudes derived from the source models. Those results indicate that the tsunami source estimation method developed in this study can be used for near-real time tsunami forecasts.

Sector collapse during the 1741 eruption of Oshima‐Oshima volcano (southwestern Hokkaido, Japan) generated a large tsunami in the Japan Sea. The tsunami caused severe damage along the Oshima (Hokkaido) and Tsugaru (Honshu) peninsulas. Tsunami deposits due to the 1741 event were identified along the Okushiri and Hiyama coast in Hokkaido. In this study, we numerically simulated the landslide and tsunami generated by the 1741 Oshima‐Oshima eruption using an improved two‐layer model to explain the depositional area of the landslide, the tsunami heights written in historical records, and the distributions of tsunami deposits. Areas of erosion and deposition by the 1741 landslide were estimated from the bathymetric data on the northern slope of Oshima‐Oshima volcano. In addition, previous topography before the sector collapse was restored. From the bathymetry difference before and after the landslide, the volume of collapsed material was estimated at 2.2 km³. Based on those data, the landslide and tsunami were numerically simulated by solving equations of an improved two‐layer model that incorporates Manning's formula in the bottom friction terms of the lower layer. An apparent friction angle of 2.5 and a Manning's roughness coefficient of 0.15 were selected to explain the area of deposition estimated from the bathymetry analysis and distributions of tsunami deposits. The thickness distribution of the computed landslide mass fits relatively well with the depositional area. Computed tsunami heights match those from historical records along the Hiyama coast. Computed tsunami inundation areas cover most of the distributions of tsunami deposits identified along the coasts.