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We studied the earthquake detection capacity of DONET (Dense Oceanfloor Network system for Earthquakes and Tsunamis) operating in the Nankai Trough, a target region monitored for future megathrust earthquakes. The focus of this paper was to evaluate the impact on this capacity from the malfunction of parts of the network. For this purpose, the completeness magnitude, above which all earthquakes are considered to be detected by a seismic network, was used. Then, a catalog that includes events observed by DONET was used. We found spatiotemporal variability of completeness magnitude, ranging from values below 1 in one of the areas where stations are densely deployed to values above 2 at the periphery and outside of the DONET area. We conducted a simulation computation for cases of malfunction of densely distributed stations. The results showed that completeness estimates in the area near the malfunctioning stations were about 1 magnitude larger. This implies that malfunction repair and/or replacement with new stations would be desirable because they pronouncedly affect earthquake monitoring. We then demonstrated an example of how to use the information of completeness magnitude as prior knowledge to compute the b value of the Gutenberg-Richter distribution. The result indicates the b value as a proxy that can help to image stress heterogeneity when there is a magnitude-6 class slow slip event on the Nankai Trough plate boundary. [ABSTRACT FROM AUTHOR]

The devastating consequences of tsunamis on coastal infrastructure have highlighted the urgent need for effective disaster risk reduction strategies. To mitigate tsunami disasters, the insurance industry plays a vital role in implementing risk transfer measures by providing financial protection against asset damage. However, the current research on catastrophe insurance policies for coastal infrastructure lacks consideration of climate change effects. It is essential to take into account the non-stationary effects of sea-level rise to develop long-term tsunami disaster mitigation measures and promote socioeconomic resilience in coastal communities. This paper aims to provide an insurance portfolio optimization framework for coastal residential buildings subjected to tsunamis considering non-stationary sea-level rise effects based on a stochastic simulation approach. A spatiotemporal probabilistic sea-level rise hazard assessment is carried out by utilizing available climate models and considering several emission scenarios. Tsunami propagation analyses under various sea-level rise cases are performed to evaluate the time-variant tsunami hazard curves based on Monte Carlo simulation. A life cycle-based stochastic insurance claim model associated with the cumulative loss of building assets is developed based on a non-stationary compound renewal process. Finally, a sample average approximation method is leveraged to estimate the optimum basic insurance premium rate by maximizing the insurer's profit under a cost-constrained insurance purchase decision of homeowners. As a case study, the proposed framework is applied to multiple municipalities situated in the tsunami-prone region of Kochi Prefecture, Japan. Sea-level rise substantially decreases the maximum profits of tsunami insurers and increases the premium rate and ruin probability. [ABSTRACT FROM AUTHOR]

Understanding the location of risk to natural hazards, namely the areas of high exposure and vulnerability is a major priority that was identified by the Sendai framework for Disaster Reduction 2015–2030 in order to reach substantial reduction of disaster risk. It is also a necessary decision-making tool for disaster mitigation policy-makers in Japan and around the world. This paper successfully develops a simple methodology using only open data to build the first large-scale (whole country), fine resolution (sub-municipal level) social vulnerability analysis in the context of five different types of natural hazards (earthquake, tsunami, storm surge, flooding and landslide). The result is then compared to an indicator of exposure of population to these hazards in Japan in order to propose a representation of disaster risk. Results show that vulnerability in Japan is highly heterogeneous with urban/rural and north/south fractures. Combining the social vulnerability index with exposure analysis, results show a wide variety of spatial patterns of risk areas in Japan. [ABSTRACT FROM AUTHOR]

The tsunami caused by the Tonga submarine volcanic eruption that occurred at 13:15 Japan Time (JST) on January 15, 2022, exposed a blind spot in Japan's tsunami monitoring and warning system, which was established in 1952 for local tsunamis and expanded to distant tsunamis after the 1960 Chile tsunami. This paper summarizes how the warning system responded to the unprecedented tsunami, the actual evacuation process, and the damage it caused in Japan. Initially, the tsunami from the volcanic eruption was expected to arrive at approximately midnight with amplitudes of less than 20 cm. However, a series of short waves arrived at approximately 21:00, a few hours earlier than expected. The early arrival of these sea waves coincided with a rapid increase in atmospheric pressure; then, the short-period component was predominant, and the wave height was amplified while forming wave groups. After a 1.2 m tsunami was observed in Amami City in southern Japan at 23:55 JST, the Japan Meteorological Agency issued a tsunami warning/advisory. The tsunami continued, and all advisories were cleared at 14:00 JST on January 16. Information about this tsunami and the response to it are summarized here, including the characteristics and issues of the actual tsunami evacuation situation in each region. There were no casualties, but the issues that emerged included difficulty evacuating on a winter night and traffic congestion due to evacuation by car and under the conditions of the COVID-19 pandemic. In the coastal area, damage to fishing boats and aquaculture facilities was reported due to the flow of the tsunami. In addition, damage to aquaculture facilities, including those producing oysters, scallops, seaweed and other marine products, decreased the supply of marine products, and the economic impact is likely to increase in the future. [ABSTRACT FROM AUTHOR]

One of the consequences of the devastating 2004 Indian Ocean tsunami was tremendous advancements in global tsunami science and coastal emergency management systems. The event, which killed over 235,000 people, brought to light the vital gaps in global tsunami science and observation systems. The subsequent global and national initiatives to develop Tsunami Early Warning System (TEWS) conceded positive results in reducing harm to people and damage to infrastructure. Especially, after the 2011 Japan tsunami, more significant improvements are accomplished in the last decade in tsunami science and its applications to disaster risk reduction. Although early warning systems have been a great promise for tsunami mitigation, there are still considerable uncertainties in monitoring and detection. Most of the current Tsunami Early Warning Systems are based on seismic origin earthquakes occurring at subduction zones. However, the other 'atypical' sources such as events related to submarine landslides and volcano eruptions are yet to be addressed globally. Here in this paper, we review the current state of the Indian Tsunami Early Warning System, recent advances to reduce the forecast uncertainties, and future developments to deal with 'atypical' sources. Also, we review the significant developments in community preparedness and awareness activities of India to reduce tsunami impact on the public and property. Advanced technology combined with enhanced response capabilities of the public at risk is important for a successful and sustainable Tsunami Early Warning System. [ABSTRACT FROM AUTHOR]

The Seafloor Observation Network for Earthquakes and Tsunamis along the Japan Trench (S-net) is presently the world's largest network of ocean bottom pressure sensors for real-time tsunami monitoring. This paper analyzes the efficacy of such a vast system in tsunami forecasting through exhaustive synthetic experiments. We consider 1500 hypothetical tsunami scenarios from megathrust earthquakes with magnitudes ranging from Mw 7.7–9.1. We employ a stochastic slip model to emulate heterogeneous slip patterns on specified 240 subfaults over the plate interface of the Japan Trench subduction zone and its vicinity. Subsequently, the associated tsunamis in terms of maximum coastal tsunami heights are evaluated along the 50-m isobath by means of a Green's function summation. To produce tsunami forecasts, we utilize a tsunami inversion from virtually observed waveforms at the S-net stations. Remarkably, forecasts accuracy of approximately 99% can be achieved using tsunami data within an interval of 3 to 5 min after the earthquake (2-min length), owing to the exceedingly dense observation points. Additionally, we apply an optimization technique to determine the optimal combination of stations with respect to earthquake magnitudes. The results show that the minimum requisite number of stations to maintain the accuracy attained by the existing network configuration decreases from 130 to 90 when the earthquake size increases from Mw 7.7 to 9.1. [ABSTRACT FROM AUTHOR]

The Seafloor Observation Network for Earthquakes and Tsunamis along the Japan Trench (S-net) is presently the world's largest network of ocean bottom pressure sensors for real-time tsunami monitoring. This paper analyzes the efficacy of such a vast system in tsunami forecasting through exhaustive synthetic experiments. We consider 1500 hypothetical tsunami scenarios from megathrust earthquakes with magnitudes ranging from Mw 7.7–9.1. We employ a stochastic slip model to emulate heterogeneous slip patterns on specified 240 subfaults over the plate interface of the Japan Trench subduction zone and its vicinity. Subsequently, the associated tsunamis in terms of maximum coastal tsunami heights are evaluated along the 50-m isobath by means of a Green's function summation. To produce tsunami forecasts, we utilize a tsunami inversion from virtually observed waveforms at the S-net stations. Remarkably, forecasts accuracy of approximately 99% can be achieved using tsunami data within an interval of 3 to 5 min after the earthquake (2-min length), owing to the exceedingly dense observation points. Additionally, we apply an optimization technique to determine the optimal combination of stations with respect to earthquake magnitudes. The results show that the minimum requisite number of stations to maintain the accuracy attained by the existing network configuration decreases from 130 to 90 when the earthquake size increases from Mw 7.7 to 9.1. [ABSTRACT FROM AUTHOR]

This paper examines the effects of the 2012 revisions to the damage predictions of an anticipated Nankai Trough megathrust earthquake on subsequent inter-municipality migration in Japan's coastal areas. We find that an increase in predicted tsunami height—rather than anticipated seismic movements—after the 2012 revision is associated with a subsequent reduction in net migration. While the reducing effect of tsunami predictions on in-migration persisted throughout the study period, the effect on out-migration was only temporary. Moreover, working-age people are more likely to respond to tsunami risk and avoid moving to municipalities with a high tsunami risk after the revisions. [ABSTRACT FROM AUTHOR]

In this paper, we deal with evacuation decision making of people in the disaster area. By threshold model, we discuss whether all agents decide to evacuate or not. It is reported that not all people in the affected area evacuated during the Great East Japan Earthquake. But, many evacuation simulations treat that all people evacuate. In the previous work, on the assumption that all people decide to evacuate, we dealt with the needed time to make all people evacuate if psychological conditions exist at the time of disaster. In this paper, we deal with the condition that not all people decide to evacuate and focus on contagion of evacuation decision making on real map. We found that for contagion of evacuation decision making, local neighborhood and connection of sub network is necessary. [ABSTRACT FROM AUTHOR]

Sassa and others in the Disaster Prevention Research Institute (DPRI), Kyoto University, developed a series of undrained ring-shear apparatus to physically simulate landslide initiation and motion, from DPRI-3 (Sassa ) to DPRI-7 (Sassa et al., Landslides 1(1):7-19, ). The maximum undrained capacities in the DPRI series ranged from 300 to 650 kPa. Sassa and others in the International Consortium on Landslides (ICL) have developed a new series of undrained ring-shear apparatus (ICL-1and ICL-2) for two projects of the International Programme on Landslides (IPL-161 and IPL-175). Both projects are supported by the Science and Technology Research Partnership for Sustainable Development Program (SATREPS) of Japan. ICL-1 was developed to create a compact and transportable apparatus for practical use in Croatia; one set was donated to Croatia in 2012. ICL-2 was developed in 2012-2013 to simulate the initiation and motion of megaslides of more than 100 m in thickness. The successful undrained capacity of ICL-2 is 3 MPa. This apparatus was applied to simulate possible conditions for the initiation and motion of the 1792 Unzen-Mayuyama megaslide (volume, 3.4 × 10 m; maximum depth, 400 m) triggered by an earthquake. The megaslide and resulting tsunami killed about 15,000 people. The Unzen Restoration Office of the Ministry of Land, Infrastructure and Transport (MLIT) of Japan systematically collected various papers and reports and published two summary leaflets: one in English in 2002 and an extended version in Japanese in 2003. Samples were taken from the source area (for initiation) and the moving area (for motion). The hazard area was estimated by the integrated landslide simulation model LS-RAPID, using parameters obtained with the ICL-2 undrained ring-shear apparatus. The estimated hazard area agrees reasonably with the landslide moving area reported in the Ministry leaflets. [ABSTRACT FROM AUTHOR]