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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 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]

On March 11, 2011, a large earthquake and subsequent tsunami near the east coast of Japan destroyed the Fukushima Daiichi nuclear power plant (FD-NPP), causing a massive release of nuclear contaminants. In this paper, a Pacific basin-wide physical dispersion model is developed and used to investigate the transport of nuclear contaminants. The Pacific circulation model, based on the Regional Ocean Modeling System (ROMS), is forced with air-sea flux climatology derived from COADS (the Comprehensive Ocean-Atmosphere Data Set). It is shown that ocean current dominates nuclear contaminant transport. Following the Kuroshio Extension and North Pacific Current, nuclear contaminants at the surface will move eastward in the Pacific as far as 140°W, thereafter dividing into two branches. For the south branch, nuclear contaminants will be transported westward by the equatorial current, and can reach the Philippines after 10 years' time. In contrast, the north branch will arrive at the American west coast and then migrate to the Bering Sea. At 200 m water depth, part of the nuclear materials will move southwestward along with deep ocean circulation, which could potentially reach the east coast of Taiwan. The other part will move to the west coast of America and separate into two branches. One will move northward along the west coast of Alaska, while the other will travel southward to the Hawaiian Islands. The transport of radiation contaminants below 500 m is slow, and will primarily remain in the central Pacific. The physical dispersion model results show that high concentrations of the radioactive isotope cesium-137 (Cs) will move eastward and reach the central Pacific and west coast of North America in two and eight years, respectively. The sea areas influenced by the nuclear contaminants continue to expand, while peak concentrations decrease in the North Pacific. [ABSTRACT FROM AUTHOR]