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In this study, we present an optimization method for determining a cost‐effective sparse configuration for tsunami gauges to realize the reconstruction of high‐resolution wave height distribution throughout the target region based on the concept of super‐resolution. This optimization method consists of three procedures. First, we generate time series data of tsunami wave heights at synthetic gauges by conducting numerical simulations of various earthquake and tsunami scenarios at the target site. Next, we apply proper orthogonal decomposition to the synthetic tsunami data to extract the spatial features of the wave height distribution. Finally, according to these spatial features, an optimization process is performed to determine a sparse configuration of synthetic gauges. In the optimization, the optimal gauges are sequentially selected from the set of synthetic gauges to reconstruct the wave height distribution with the highest accuracy. Targeting hypothetical Nankai Trough earthquakes and tsunamis, we determine the optimal configuration near Shikoku and demonstrate the wave height reconstruction capability of the approach by comparing the performance of networks with optimally and randomly placed gauges. The results indicate that coastal gauges contribute more to improving the reconstruction accuracy and that a configuration with 21 optimal gauges has satisfactory performance. In addition, we assess the performance of the existing NOWPHAS network installed in the Shikoku region and find that the reconstruction performance of the existing network is equivalent to that of the optimal gauge network. Plain Language Summary: This study introduces a method of optimizing the sparse locations where actual tsunami gauges should be installed to obtain information on tsunami wave heights at any given point. By optimizing the locations of the observation points, it is possible to extend observations recorded at only a small number of points to obtain a good approximation to data at other points where the tsunami was not directly observed. First, numerical simulations are performed based on assumed earthquake and tsunami scenarios to generate synthetic time series data of tsunami waves. Then, by applying proper orthogonal decomposition to the obtained synthetic data, the characteristics of the tsunami wave height distribution are extracted. Finally, these characteristics are used to perform optimization by sequentially selecting the best gauges from among a set of candidate points to reconstruct wave height information for the entire target area, thereby determining the placements of a limited number of gauges. In a numerical demonstration example simulating a Nankai Trough earthquake and tsunami, the placement of gauges off the coast of Shikoku is optimized, and it is shown that the wave heights at arbitrary points over the entire area can be reproduced using data from at least 21 optimally placed gauges. Key Points: An optimization method implementing a greedy algorithm is presented to design a cost‐effective observational network of tsunami gaugesHigh‐resolution information about tsunami wave height is constructed from sparse observations based on the concept of super‐resolutionThe optimization process considering the existing observation network locations identifies their sufficiency or deficiency [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]

This study proposes a new method for the uncertainty estimation of coseismic slip distribution on the plate interface deduced from real-time global navigation satellite system (GNSS) data and explores its application for tsunami inundation prediction. Jointly developed by the Geospatial Information Authority of Japan and Tohoku University, REGARD (REal-time GEONET Analysis system for Rapid Deformation monitoring) estimates coseismic fault models (a single rectangular fault model and slip distribution model) in real time to support tsunami prediction. The estimated results are adopted as part of the Disaster Information System, which is used by the Cabinet Office of the Government of Japan to assess tsunami inundation and damage. However, the REGARD system currently struggles to estimate the quantitative uncertainty of the estimated result, although the obtained result should contain both observation and modeling errors caused by the model settings. Understanding such quantitative uncertainties based on the input data is essential for utilizing this resource for disaster response. We developed an algorithm that estimates the coseismic slip distribution and its uncertainties using Markov chain Monte Carlo methods. We focused on the Nankai Trough of southwest Japan, where megathrust earthquakes have repeatedly occurred, and used simulation data to assume a Hoei-type earthquake. We divided the 2951 rectangular subfaults on the plate interface and designed a multistage sampling flow with stepwise perturbation groups. As a result, we successfully estimated the slip distribution and its uncertainty at the 95% confidence interval of the posterior probability density function. Furthermore, we developed a new visualization procedure that shows the risk of tsunami inundation and the probability on a map. Under the algorithm, we regarded the Markov chain Monte Carlo samples as individual fault models and clustered them using the k-means approach to obtain different tsunami source scenarios. We then calculated the parallel tsunami inundations and integrated the results on the map. This map, which expresses the uncertainties of tsunami inundation caused by uncertainties in the coseismic fault estimation, offers quantitative and real time insights into possible worst-case scenarios. [ABSTRACT FROM AUTHOR]

Previous applications of machine learning in remote sensing for the identification of damaged buildings in the aftermath of a large-scale disaster have been successful. However, standard methods do not consider the complexity and costs of compiling a training data set after a large-scale disaster. In this article, we study disaster events in which the intensity can be modeled via numerical simulation and/or instrumentation. For such cases, two fully automatic procedures for the detection of severely damaged buildings are introduced. The fundamental assumption is that samples that are located in areas with low disaster intensity mainly represent nondamaged buildings. Furthermore, areas with moderate to strong disaster intensities likely contain damaged and nondamaged buildings. Under this assumption, a procedure that is based on the automatic selection of training samples for learning and calibrating the standard support vector machine classifier is utilized. The second procedure is based on the use of two regularization parameters to define the support vectors. These frameworks avoid the collection of labeled building samples via field surveys and/or visual inspection of optical images, which requires a significant amount of time. The performance of the proposed method is evaluated via application to three real cases: the 2011 Tohoku-Oki earthquake–tsunami, the 2016 Kumamoto earthquake, and the 2018 Okayama floods. The resulted accuracy ranges between 0.85 and 0.89, and thus, it shows that the result can be used for the rapid allocation of affected buildings. [ABSTRACT FROM AUTHOR]

Earth observation (EO) technologies, such as optical imaging and synthetic aperture radar (SAR), provide excellent means to continuously monitor ever-growing urban environments. Notably, in the case of large-scale disasters (e.g., tsunamis and earthquakes), in which a response is highly time-critical, images from both data modalities can complement each other to accurately convey the full damage condition in the disaster aftermath. However, due to several factors, such as weather and satellite coverage, which data modality will be the first available for rapid disaster response efforts is often uncertain. Hence, novel methodologies that can utilize all accessible EO datasets are essential for disaster management. In this study, we developed a global multimodal and multitemporal dataset for building damage mapping. We included building damage characteristics from three disaster types, namely, earthquakes, tsunamis, and typhoons, and considered three building damage categories. The global dataset contains high-resolution (HR) optical imagery and high-to-moderate-resolution SAR data acquired before and after each disaster. Using this comprehensive dataset, we analyzed five data modality scenarios for damage mapping: single-mode (optical and SAR datasets), cross-modal (pre-disaster optical and post-disaster SAR datasets), and mode fusion scenarios. We defined a damage mapping framework for semantic segmentation of damaged buildings based on a deep convolutional neural network (CNN) algorithm. We also compared our approach to another state-of-the-art model for damage mapping. The results indicated that our dataset, together with a deep learning network, enabled acceptable predictions for all the data modality scenarios. We also found that the results from cross-modal mapping were comparable to the results obtained from a fusion sensor and optical mode analysis. [ABSTRACT FROM AUTHOR]

To develop disaster mitigation measures in coastal regions affected by earthquakes, it is important to consider the effects of both seismic and tsunami hazards on road structures and assess the social impacts associated with the economic loss and reduction in network functionality. In this paper, a risk- and resilience-based assessment framework is established for road networks under both seismic and tsunami hazards. In the proposed methodology, the risk and resilience are quantified by the economic loss and postdisaster functionality of road networks, respectively. Uncertainties associated with estimations of fault movement, hazard intensity and structural vulnerability are considered when estimating the failure probability using Monte Carlo simulation. Moreover, structural vulnerability against tsunamis is estimated considering the effects of ground motion-induced damage on the reduction in tsunami capacity. As an illustrative example, the road networks in two Japanese cities expected to be affected by the anticipated Nankai Trough earthquake, which would cause strong ground motions and a subsequent tsunami, are analyzed. Finally, the retrofitting prioritization for various structures is discussed based on the risk and resilience quantified as performance indicators for social impacts. [ABSTRACT FROM AUTHOR]

Highly destructive tsunamis occurred near the Pacific coast of Colombia in 1906 and 1979. Recent studies have established asperities within the subduction zone in this area, which can cause megathrust earthquakes triggering highly destructive tsunamis. In this study we assess the tsunami hazard in the main populated areas of Colombia Pacific coast by calculating the inundation depth and the maximum tsunami height from major scenario. A deterministic method is applied using slip deficit models and broadband slip models as tsunami sources. Result suggest that, for the worse-case scenario, the maximum deformation of the seafloor is 6.0 m within the Esmaraldas segment ; the maximum height of the tsunami is 4.66 m, 4.34 m and 0.53 m around. Tumaco Island, Morro Island, and Cascajal Island (Buenaventura Bay), respectively. Two to five meters inundation depth were calculated in over 11% and 10% of the total areas of the islands of Tumaco and Morro, respectively. Based on these result, tsunami hazard maps were alaborated and will serve to create tsunami mitigation plans in these areas. [ABSTRACT FROM AUTHOR]

• A framework for time-variant tsunami hazard assessment considering nonstationary sea-level rise effects is established. • Non-Poisson renewal process models are developed to combine randomly recurring tsunamis with nonstationary sea-level rise. • The sea-level rise uncertainty is integrated during the tsunami hazard assessment according to total probability theorem. • Tsunami hazard increases considerably when considering nonstationary sea-level rise under non-Poisson earthquake events. Tsunami-induced disasters present a significant threat to coastal communities. Under the effects of sea-level rise due to climate change, tsunami occurrence frequency and intensities would increase in a nonstationary manner due to the long-term progressive trend in the variability in sea level. Therefore, the reliability of coastal infrastructure systems affected by tsunami hazard will diminish over time. A procedure for hazard assessment that integrates tsunamis occurring at random times with evolving sea-level rise should be established. This paper presents a novel framework for the time-variant assessment of tsunami hazard considering the effects of nonstationary sea-level rise due to climate change based on a non-Poisson stochastic renewal process. A time-variant probabilistic sea-level rise is modeled by utilizing various climate models and existing data reported in several studies. The conditional tsunami hazard curves considering sea-level rise are estimated by performing tsunami propagation analyses under different sea-level rise cases and by Monte Carlo simulation, considering the uncertainties associated with earthquake fault movements. Finally, the new concept of time-variant tsunami hazard assessment considering the nonstationary effects of sea-level rise is developed according to a non-Poisson stochastic renewal process. An illustrative example is provided by applying the proposed framework to several municipalities in the Kochi Prefecture of Japan that are subjected to the anticipated Nankai-Tonankai earthquake. The effects of sea-level rise on time-variant tsunami hazard under Poisson and non-Poisson processes of earthquake occurrence are discussed. [ABSTRACT FROM AUTHOR]

• A framework for incorporating uncertainties associated with sea-level rise in probabilistic tsunami hazard assessments (PTHAs) is established. • A practical approach for probabilistic sea-level rise hazard assessment is provided by utilizing the available climate models and data. • Tsunami hazard curves considering the effects of sea-level rise are estimated based on the total probability theorem. • Neglecting the effects of sea-level rise in a low-lying coastal plain can significantly underestimate tsunami hazards. Sea-level rise due to climate change could significantly exacerbate tsunami disasters since the sea level is a critical parameter affecting the intensity of tsunamis. Considering the impacts of future climate change on the ocean, a method to consider the effects of sea-level rise on the tsunami hazard intensity is needed to precisely predict future tsunami disasters. This paper presents a novel framework for probabilistic tsunami hazard assessment, considering the sea-level rise and associated uncertainties. A probabilistic assessment of the sea-level rise hazard is performed using the available data and climate models considering several climate change emission scenarios. Conditional tsunami hazard curves are estimated by conducting tsunami propagation simulations given a sea-level rise while considering the uncertainties associated with fault movement (i.e., rake angles and average stress drops). Radial-basis-function-based surrogate models and quasi-Monte-Carlo simulations are employed to obtain tsunami hazard curves. Finally, the tsunami hazard curves considering the effects of sea-level rise are estimated by convolving the corresponding regional sea-level rise hazard with the conditional tsunami hazard curves based on the total probability theorem. An illustrative example is provided, in which the proposed framework is applied to several municipalities in the Mie Prefecture of Japan that would be affected by tsunamis during the anticipated Nankai-Tonankai earthquake. The effects of sea-level rise on the tsunami hazard intensities associated with the municipalities are discussed. [ABSTRACT FROM AUTHOR]