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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]

On March 11th, 2011, the Pacific coast of Japan was hit by a tsunami generated by the largest earthquake (M9.0) in the history of the country and causing a wide range of devastating damage. Using preliminary reported data from many sources, some topics such as tsunami fatality ratio and tsunami fragility curves for structural damage are discussed and compared with other countries. This paper aims to discuss the damage characteristics of this tsunami as well as its mechanism, as observed through field surveys conducted over the 4 months following the tsunami. The field survey covers 13 areas in the Miyagi prefecture from Kesennuma city in the northernmost region to Yamamoto town in the southernmost region. The arrival time of the first tsunami along the coastal areas in the Miyagi prefecture was confirmed by stopped clocks found during the survey. The damage mechanism of coastal structures such as breakwaters, seawalls, tsunami gates, and evacuation buildings was investigated and discussed. Damage characteristics for each area, i.e., urban areas, port, coastal structures, fisheries, and agricultural areas, were also summarized. The conclusions drawn from the data analysis suggest that experience and education (soft countermeasures) are important to reduce the loss of life, as shown for example in the Sanriku area. The field surveys indicate that wood and reinforced-concrete (RC) structures should be balanced to survive both earthquake and tsunami forces, and the structural design for buildings should be reconsidered after the example in Onagawa town. In addition, coastal structures for tsunami countermeasures (hard countermeasures) should be more properly designed for survival instead of becoming floating debris upon being overturned by a tsunami. The combination of both hard and soft measures is especially necessary for optimizing the outcomes following a great disaster. These recommendations should be taken into consideration in the reconstruction efforts for better tsunami countermeasures in the future. [ABSTRACT FROM AUTHOR]

The authors visually inspected the building damage caused by the 2011 Tohoku earthquake tsunami, using the pre and post-event aerial photos. First, we prepared the mosaic of post-tsunami aerial photos acquired by Geospatial Information Authority of Japan (GSI), and conducted the visual inspection of buildings damage to classify the damage. The damage classification results are compiled with building shape files on GIS for mapping the structural vulnerability in the tsunami inundation zone. Finally, we discussed the structural vulnerability in the tsunami affected area based on mapping results of building damage. [ABSTRACT FROM AUTHOR]

We revisited the lessons of the 2011 Great East Japan Earthquake Tsunami disaster specifically on the response and impact, and discussed the paradigm shift of Japan's tsunami disaster management policies and the perspectives for reconstruction. Revisiting the modern histories of Tohoku tsunami disasters and pre-2011 tsunami countermeasures, we clarified how Japan's coastal communities have prepared for tsunamis. The discussion mainly focuses on structural measures such as seawalls and breakwaters and non-structural measures of hazard map and evacuation. The responses to the 2011 event are discussed specifically on the tsunami warning system and efforts to identify the tsunami impacts. The nation-wide post-tsunami survey results shed light on the mechanisms of structural destruction, tsunami loads and structural vulnerability to inform structural rehabilitation measures and land-use planning. Remarkable paradigm shifts in designing coastal protection and disaster mitigation measures were introduced, leading with a new concept of potential tsunami levels: Prevention (Level 1) and Mitigation (Level 2) levels according to the level of 'protection'. The seawall is designed with reference to Level 1 tsunami scenario, while comprehensive disaster management measures should refer to Level 2 tsunami for protection of human lives and reducing potential losses and damage. Throughout the case study in Sendai city, the proposed reconstruction plan was evaluated from the tsunami engineering point of view to discuss how the post 2011 paradigm was implemented in coastal communities for future disaster mitigation. The analysis revealed that Sendai city's multiple protection measures for Level 2 tsunami will contribute to a substantial reduction of the tsunami inundation zone and potential losses, combined with an effective tsunami evacuation plan. [ABSTRACT FROM AUTHOR]

ABSTRACT Udo, K.; Takeda, Y., and Koshimura, S., 2018. Detection of coastal damage using differences in automatically generated digital surface models (DSMs): Application to the 2011 Off the Pacific Coast of Tohoku Earthquake Tsunami. In: Shim, J.-S.; Chun, I., and Lim, H.S. (eds.), Proceedings from the International Coastal Symposium (ICS) 2018 (Busan, Republic of Korea). Journal of Coastal Research, Special Issue No. 85, pp. 696–700. Coconut Creek (Florida), ISSN 0749-0208. The detection of coastal damage due to the 2011 Off the Pacific Coast of Tohoku Earthquake Tsunami on the southern Sendai Coast was attempted via state-of-the-art remote sensing technology using ALOS PRISM satellite images with a spatial resolution of 2.5 m. Digital surface models (DSMs) were generated semi-automatically, and their accuracies were confirmed via comparisons with digital elevation models generated from aerial laser measurements. The differences between the DSMs prior to and after the tsunami were analyzed statistically for each type of land cover: residential districts, rice fields, coastal forests, and beaches. It was demonstrated that the damage to the coastal forests and residential districts could be detected though the damage in the low land area was difficult to be detected because of its small undulations in topography. Low pass filtering was employed to remove the jitter noise; however, the major characteristics such as damage in the coastal forests and residential areas were not changed by the filtering; that is, the major statistical characteristics could be detected without filtering. [ABSTRACT FROM AUTHOR]

The lattice Boltzmann method (LBM) has recently attracted attention as a simple computational fluid dynamics (CFD) method that does not directly solve macroscopic equations. Due to the advantage of its local scheme, the LBM is expected to efficiently achieve highly accurate and high-resolution fluid simulations. In particular, the application of the LBM to disaster simulations has been studied since the proposal of the LBM for nonlinear shallow water equations (LABSWE). However, it is well known that the LBM is prone to computational instability in high Reynolds number flows. A tsunami inundating an urban area is considered a high Reynolds number flow because of its violent flow. The numerical stability in such flows must be enhanced for the application of the LBM to real-world tsunami simulations. For this purpose, it is practical to introduce the subgrid-scale (SGS) model into the multiple-relaxation-time (MRT) model. Notably, the MRT model is computationally more expensive than the conventional lattice Bhatnagar–Gross–Krook (BGK) model. In this study, we modified the SGS model for the MRT model. We verified the calculation accuracy by focusing on grid size differences in the classical benchmark problems in high Reynolds number flows. Finally, we simulated the 2011 tsunami off the Pacific coast caused by the Tohoku earthquake. The LBM simulations reproduced the tsunami propagation waveform and inundation depth with the computational accuracy equally as good as that of the finite difference method (FDM). • The tsunami by the 2011 Great East Japan Earthquake is simulated. • The proposed model is stable in the high Reynolds number flows. • A comparative study with the finite difference method is demonstrated. [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]

Japan was hit by the tsunami generated by the greatest earthquake in the history of Japan. The authors conducted the post-tsunami field survey in Miyagi prefecture immediately after the event to measure the inundation depth and investigate damaged buildings. Most of the buildings surveyed were wooden houses and could be classified into 4 damage levels. The primary data of inundation depth and damage levels obtained from the field survey were used to create the tsunami fragility curves, which described the degree of structural damage as a function of the hydrodynamic characteristics of tsunami inundation. The developed fragility curves show that damage with more than a 50% chance of occurring is classified as minor damage, moderate damage, major damage and complete damage when the tsunami inundation depth is between 2.5-3.0 m, 3.0-4.0 m and 4.0-4.5 m and greater than 4.5 m, respectively. By comparing the developed tsunami fragility curves, wooden houses in the studied area have higher structural performance than those obtained from historical events in other areas. The new curves suggest that wooden houses will be severely damaged if the inundation depth is greater than 3 m and collapse if the depth is greater than 4 m while wooden houses from historical data can resist only 1-2 m. Moreover, wooden walls in Japan reduce the overall failure of a structural member because a wooden wall is easier to destroy (compared to brick walls in Thailand and Indonesia) and allows the tsunami to pass through, which reduces the pressure at the attacking front of the tsunami. The fragility curves are very important for the loss estimation and reconstruction plans of the city; they are also crucial for reducing the disaster damage from a future tsunami. [ABSTRACT FROM AUTHOR]

Applications of machine learning on remote sensing data appear to be endless. Its use in damage identification for early response in the aftermath of a large-scale disaster has a specific issue. The collection of training data right after a disaster is costly, time-consuming, and many times impossible. This study analyzes a possible solution to the referred issue: the collection of training data from past disaster events to calibrate a discriminant function. Then the identification of affected areas in a current disaster can be performed in near real-time. The performance of a supervised machine learning classifier to learn from training data collected from the 2018 heavy rainfall at Okayama Prefecture, Japan, and to identify floods due to the typhoon Hagibis on 12 October 2019 at eastern Japan is reported in this paper. The results show a moderate agreement with flood maps provided by local governments and public institutions, and support the assumption that previous disaster information can be used to identify a current disaster in near-real time. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of debris estimation which is one of the most important initial challenges in the wake of a disaster like the Great East Japan Earthquake and Tsunami. Reasonable estimates of the debris have to be made available to decision makers as quickly as possible. Current approaches to obtain this information are far from being optimal as they usually rely on manual interpretation of optical imagery. We have developed a novel approach for the estimation of tsunami debris pile heights and volumes for improved emergency response. The method is based on a stereo-synthetic aperture radar (stereo-SAR) approach for very high-resolution polarimetric SAR. An advanced gradient-based optical-flow estimation technique is applied for optimal image coregistration of the low-coherence non-interferometric data resulting from the illumination from opposite directions and in different polarizations. By applying model based decomposition of the coherency matrix, only the odd bounce scattering contributions are used to optimize echo time computation. The method exclusively considers the relative height differences from the top of the piles to their base to achieve a very fine resolution in height estimation. To define the base, a reference point on non-debris-covered ground surface is located adjacent to the debris pile targets by exploiting the polarimetric scattering information. The proposed technique is validated using in situ data of real tsunami debris taken on a temporary debris management site in the tsunami affected area near Sendai city, Japan. The estimated height error is smaller than 0.6 m RMSE. The good quality of derived pile heights allows for a voxel-based estimation of debris volumes with a RMSE of 1099 m 3 . Advantages of the proposed method are fast computation time, and robust height and volume estimation of debris piles without the need for pre-event data or auxiliary information like DEM, topographic maps or GCPs. [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]

The 2011 Great East Japan Earthquake generated extreme tsunami waves that entered river mouths, causing strong and long-distance propagation along the water corridors. This study reports the first empirical survey of this rare and extreme ecological impact of tsunami propagation on river communities., We sampled macroinvertebrate communities at 15 river sites on the Sendai Plain in Northeast Japan along a gradient of tsunami disturbance intensity [i.e. altitude: range = 1-29 m above sea level (a.s.l.)] two and 16 months after the earthquake. In comparison with data collected before the tsunami, we found evidence of significant reductions in taxon richness (−54% on average) and total abundance (−91%) after the tsunami in inundated river reaches (up to 25 km from the river mouth)., There were large spatial variations in local impacts, with altitudes of 7-9 m a.s.l. separating heavily damaged inundated communities from intact non-inundated communities., The degraded communities exhibited variable recovery rates extending beyond 16 months after the tsunami. Mobility trait analysis revealed a relatively rapid recovery of swimmers (e.g. Baetis), reflecting their high mobility and active drift behaviours in the recolonisation phase., We observed the rare phenomenon of 'upstream drift' of marine worms (Phyllodocidae) to freshwater rivers in three of the 10 inundated sites. These survived in fresh water for up to 14 months, suggesting that some marine-derived immigrants can persist in fresh water for extended periods. [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]

When flooding occurs, Synthetic Aperture Radar (SAR) imagery is often used to identify flood extent and the affected buildings for two reasons: (i) for early disaster response, such as rescue operations, and (ii) for flood risk analysis. Furthermore, the application of machine learning has been valuable for the identification of damaged buildings. However, the performance of machine learning depends on the number and quality of training data, which is scarce in the aftermath of a large scale disaster. To address this issue, we propose the use of fragmentary but reliable news media photographs at the time of a disaster and use them to detect the whole extent of the flooded buildings. As an experimental test, the flood occurred in the town of Mabi, Japan, in 2018 is used. Five hand-engineered features were extracted from SAR images acquired before and after the disaster. The training data were collected based on news photos. The date release of the photographs were considered to assess the potential role of news information as a source of training data. Then, a discriminant function was calibrated using the training data and the support vector machine method. We found that news information taken within 24 h of a disaster can classify flooded and nonflooded buildings with about 80% accuracy. The results were also compared with a standard unsupervised learning method and confirmed that training data generated from news media photographs improves the accuracy obtained from unsupervised classification methods. We also provide a discussion on the potential role of news media as a source of reliable information to be used as training data and other activities associated to early disaster response. [ABSTRACT FROM AUTHOR]

The rapid and accurate mapping of large-scale landslides and other mass movement disasters is crucial for prompt disaster response efforts and immediate recovery planning. As such, remote sensing information, especially from synthetic aperture radar (SAR) sensors, has significant advantages over cloud-covered optical imagery and conventional field survey campaigns. In this work, we introduced an integrated pixel-object image analysis framework for landslide recognition using SAR data. The robustness of our proposed methodology was demonstrated by mapping two different source-induced landslide events, namely, the debris flows following the torrential rainfall that fell over Hiroshima, Japan, in early July 2018 and the coseismic landslide that followed the 2018 Mw6.7 Hokkaido earthquake. For both events, only a pair of SAR images acquired before and after each disaster by the Advanced Land Observing Satellite-2 (ALOS-2) was used. Additional information, such as digital elevation model (DEM) and land cover information, was employed only to constrain the damage detected in the affected areas. We verified the accuracy of our method by comparing it with the available reference data. The detection results showed an acceptable correlation with the reference data in terms of the locations of damage. Numerical evaluations indicated that our methodology could detect landslides with an accuracy exceeding 80%. In addition, the kappa coefficients for the Hiroshima and Hokkaido events were 0.30 and 0.47, respectively. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) images have been used to map flooded areas with great success. Flooded areas are often identified by detecting changes between a pair of images recorded before and after a certain flood. During the 2018 Western Japan Floods, the change detection method generated significant misclassifications for agricultural targets. To evaluate whether such a situation could be repeated in future events, this paper examines and identifies the causes of the misclassifications. We concluded that the errors occurred because of the following. (i) The use of only a single pair of SAR images from before and after the floods. (ii) The unawareness of the dynamics of the backscattering intensity through time in agricultural areas. (iii) The effect of the wavelength on agricultural targets. Furthermore, it is highly probable that such conditions might occur in future events. Our conclusions are supported by a field survey of 35 paddy fields located within the misclassified area and the analysis of Sentinel-1 time series data. In addition, in this paper, we propose a new parameter, which we named "conditional coherence", that can be of help to overcome the referred issue. The new parameter is based on the physical mechanism of the backscattering on flooded and non-flooded agricultural targets. The performance of the conditional coherence as an input of discriminant functions to identify flooded and non-flooded agricultural targets is reported as well. [ABSTRACT FROM AUTHOR]