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Free-surface flow problems occur in numerous disaster simulations, such as tsunami inland penetration in urban areas. Simulation models for these problems must be non-hydrostatic, three-dimensional and highly resolved because of the strong non-linearity and higher-order physical phenomena. However, three-dimensional large-scale tsunami simulations based on conventional computational fluid dynamics (CFD) solving the Navier-Stokes equations are challenging due to the pressure Poisson equation in incompressible Navier-Stokes fluid modeling. The lattice Boltzmann method (LBM) is an alternative simulation tool that is attracting attention as a fully explicit and efficient approach. The LBM does not have to iteratively solve the pressure Poisson equation and is therefore considered to have an advantage over other methods when executing high-performance three-dimensional tsunami simulations. In the current study, we developed a fully explicit free-surface model using the LBM in which the more advanced multiple-relaxation-time (MRT) collision model is used, along with the piecewise linear interface calculation (PLIC) approach. Through classic dam-break problems, we validated the appropriate parameter settings, including the weak compressibility, for tsunami simulations. The benchmark tests showed that our model accurately simulates the three-dimensional dam-break flows and controls the compressibility drop in the second-order value of the Mach number. [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]

As confirmed by the extreme tsunami events over the last decade (the 2004 Indian Ocean, 2010 Chile and 2011 Japan tsunami events), mitigation measures and effective evacuation planning are needed to reduce disaster risks. Modeling tsunami evacuations is an alternative means to analyze evacuation plans and possible scenarios of evacuees' behaviors. In this paper, practical applications of an agent-based tsunami evacuation model are presented to demonstrate the contributions that agent-based modeling has added to tsunami evacuation simulations and tsunami mitigation efforts. A brief review of previous agent-based evacuation models in the literature is given to highlight recent progress in agent-based methods. Finally, challenges are noted for bridging gaps between geoscience and social science within the agent-based approach for modeling tsunami evacuations. [ABSTRACT FROM AUTHOR]

SUMMARY Characteristics of the tsunami decay processes are examined following the definition and discussion of the major terms involved: 'moving root mean squared amplitude', 'tsunami coda' and 'non-dimensional tsunami amplitude' (NDA). Tsunami waveform data observed at tidal stations in Japan during two Kuril Island earthquake tsunamis in 2006 and 2007 are analysed. The tsunami codas of these events showed the following characteristics. (1) The decay process determined by the tsunami coda of the 2006 tsunami implies that the decay time constant at a tidal station depends on which ocean the station is facing. (2) Fluctuations of the NDAs in the tsunami codas are stochastically approximated by either the Rayleigh distribution or the normal distribution and the distributions of the NDAs at most tidal stations are similar. (3) Differences in the decay processes between these two tsunami events were small with regard to the averaged decay time constants and the distributions of the NDAs. These findings may be the first step towards improved forecasting of the tsunami decay process. [ABSTRACT FROM AUTHOR]

Abstract: Using an integrated approach including satellite imagery analysis, field measurements, and numerical modeling, we investigated the damage to mangroves caused by the 2004 Indian Ocean tsunami at Pakarang Cape in Pang Nga Province, Thailand. Comparing pre- and post-tsunami satellite imagery of the study area, we found that approximately 70% of the mangrove forest was destroyed by the tsunami. Based on field observations, we found that the survival rate of mangroves increased with increasing stem diameter. Specifically, we found that 72% of Rhizophora trees with a 25–30cm stem diameter survived the tsunami impact, whereas only 19% with a 15–20cm stem diameter survived. We simulated the 2004 Indian Ocean tsunami using the nonlinear shallow-water wave theory to reproduce the tsunami inundation flow and investigated the bending moment acting on the mangrove trees. Results of the numerical model showed that the tsunami inundated areas along the mangrove creeks, and its current velocity reached 5.0ms−1. Based on the field measurements and numerical results, we proposed a fragility function for mangroves, which is the relationship between the probability of damage and the bending stress caused by the maximum bending moment. We refined the numerical model to include the damage probability of mangrove forests using the obtained fragility function to investigate the tsunami reduction effect of mangrove forest. Under simple numerical conditions related to the mangrove forest, ground level, and incident wave, the model showed that a mangrove forest of Rhizophora sp. with a density of 0.2treesm−2 and a stem diameter of 15cm in a 400m wide area can reduce the tsunami inundation depth by 30% when the incident wave is assumed to have a 3.0m inundation depth and a wave period of 30min at the shoreline. However, 50% of the mangrove forest is destroyed by a 4.5m tsunami inundation depth, and most of the mangrove forest is destroyed by a tsunami inundation depth greater than 6m. The reduction effect of tsunami inundation depth decreased when the tsunami inundation depth exceeded 3m, and was mostly lost when the tsunami inundation depth exceeded 6m. [Copyright &y& Elsevier]

A new analytical solution of tsunami which is obliquely incident on a continental slope is derived based on the linear shallow water wave theory. The model of a continental slope consists of a finite constant slope and a bottom of uniform depth and has a vertical wall at the shoreline. Because of the existence of the vertical wall, another function can be taken as one of the fundamental solutions on a slope in addition to the confluent hypergeometric function M. Based on the solution obtained, the characteristics of tsunami amplification on a continental slope are discussed in terms of the conditions of the incidence. The magnitude of the amplification can be predicted with several parameters of wave incidence, such as, the incident angle and the relative incident-wave length to the topography. Especially, when a tsunami is incident almost parallel to the slope, we found that there exists distinctive amplifications for particular parameters characterizing the incident wave. The effect of the existence of the vertical wall is also discussed. The characteristics of amplification also depend much on the position of the vertical wall. [ABSTRACT FROM AUTHOR]

Fundamental characteristics of tsunamis propagating over an oceanic ridge are investigated analytically based on the linear shallow water wave theory. The oceanic ridge model used has a triangular shape which is formed by the combination of a constant slope and a bottom of uniform depth. Wave pattern of on-ridge tsunamis varies depending on the obliquity of incident waves. Especially, standing waves are formed when the incident wave angle to the ridge exceeds the critical angle. Then no transmitted waves exist because of the evanescent waves. The present study is applied to the 1996 Irian Jaya tsunami which propagated over the South-Honshu ridge. The characteristics of on-ridge tsunamis observed at Chichijima and Hachijojima, which are close to the South-Honshu ridge, can be partly explained by using the solution derived in the present study. [ABSTRACT FROM AUTHOR]

Tsunamis are rare events compared with the other natural disasters, but once it happens, it can be extremely devastating to the coastal communities. Extensive inland penetration of tsunamis may cause the difficulties of understanding its impact in the aftermath of its generation. Therefore the social needs to technologies of detecting the wide impact of great tsunamis have been increased. Recent advances of remote sensing and technologies of image analysis meet the above needs and lead to more rapid and efficient understanding of tsunami affected areas. This paper provides a review of how remote sensing methods have developed to contribute to post-tsunami disaster response. The evaluations in the performances of the remote sensing methods are discussed according to the needs of tsunami disaster response with future perspective. [ABSTRACT FROM AUTHOR]

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]

On 28 September 2018, a large earthquake of magnitude 7.5 occurred at 72 km away from the city of Palu, Central Sulawesi, Indonesia. Following the main shock, a series of tsunami attacked the eastern coasts of central Sulawesi and the severe damages were reported along the coast of Palu Bay.In the aftermath of the event, since the rupture was occurred along Palu-Koro strike-slip fault, the tsunami impact was expected to be relatively low compared with the other past tsunami events caused by the thrust fault rupture. However, the reported damage, more than 70,000 houses were reported to be damaged, was much more severe than expected because of the combination of strong ground motion, landslides, mudflows and tsunamis.After the event occurred, as a part of the Indonesian government's reconnaissance, we conducted a post-tsunami field survey to identify the tsunami impact. The survey was conducted on 21 and 22 October, 2018, and aimed to measure the extent of tsunami inland penetration with RTK-GPS, flow depths and to inspect the structural damage. The survey also aimed to collect the ground truth information for satellite remote sensing and survivor video analysis to understand how the tsunami attacked and devastated the coastal areas of central Palu.From the survey at the central Palu, we found that the major tsunami impact was concentrated within about 200 m inland from the shoreline. The spatial distribution of tsunami flow depths was scattered in the narrow range of tsunami inundation zone of the central Palu coast; 1-3m flow depths in average, 6 m the maximum at the central Palu.Some survivor videos provided important information on tsunami attack, especially on the time series of the first and second tsunami attacks. From the video analysis, we found that the second tsunami hit Palu Grand Mall with the splash height of almost 8 m above the sea level, and the difference of 1st. and 2nd. tsunami attack was likely to be 2 minutes. This implies that these tsunamis were generated by a phenomenon with short time scale or different sources. [ABSTRACT FROM AUTHOR]

Tsunami fragility functions (TFF) are statistical models that relate a tsunami intensity measure to a given building damage state, expressed as cumulative probability. Advances in computational and data retrieval speeds, coupled with novel deep learning applications to disaster science, have shifted research focus away from statistical estimators. TFFs offer a "disaster signature" with comparative value, though these models are seldom applied to generate damage estimates. With applicability in mind, we challenge this notion and investigate a portion of TFF literature, selecting three TFFs and two application methodologies to generate a building damage estimation baseline. Further, we propose a simple machine learning method, trained on physical parameters inspired by, but expanded beyond, TFF intensity measures. We test these three methods on the 2011 Ishinomaki dataset after the Great East Japan Earthquake and Tsunami in both binary and multi-class cases. We explore: (1) the quality of building damage estimation using TFF application methods; (2) whether TFF can generalize to out-of-domain building damage datasets; (3) a novel machine learning approach to perform the same task. Our findings suggest that: both TFF methods and our model have the potential to achieve good binary results; TFF methods struggle with multiple classes and out-of-domain tasks, while our proposed method appears to generalize better. [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]

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]

Numerical simulation of tsunami is an effective method to reproduce what has occurred in the past and to predict future events for many tsunami-related research issues including warning systems. However, some real phenomena have not been fully integrated into numerical simulations for transoceanic tsunamis such as fault dynamics of rupture velocity, ocean currents, and the initial sea level. Considering the 2004 Indian Ocean tsunami event, this study evaluates the consequences of rupture velocity. Subsequently, numerical experiments were conducted to normalize the effects as represented by non-dimensional parameters. The rupture velocity, ocean current, and initial sea level were simplified to be uniform and common among simulations. Results of the experiment show that the sea depth along the propagation direction, distance from the tsunami source, rupture velocity, and initial sea level impart some considerable effects on a tsunami's arrival time and wave height. Nevertheless, ocean currents have almost no importance for the arrival time or wave height of oceanic propagation of tsunamis. [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]

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 fragility function shows the building damage likelihood based on a demand parameter such as the tsunami flow depth. While empirical fragility functions have been developed by using postdisaster damage survey data, regions with no recent tsunami damage cannot develop these estimations. Alternatively, analytical fragility functions could be developed to fill this gap. Along the Colombian Pacific coast, several tsunami events have occurred; however, the aftermath resulting from the structure physical damage was not recorded. Fortunately, other regions of the have collected tsunami damage information, which has been analyzed and represented in terms of empirical fragility functions. This paper provides a comparative study of empirical and analytical tsunami fragility functions. It is focused on the analytical fragility functions developed for typical structures in Tumaco, Colombia and on empirical fragility curves taken from damage survey data of recent tsunami events. The differences in the assessments of the damage produced by the two methods in the case of a two-story reinforced concrete building in Tumaco is discussed herein. As a result of the comparison, it was shown that the structures in Tumaco have higher collapse probabilities at lower flow depths than structures in other places along the Pacific and Indian Oceans. [ABSTRACT FROM AUTHOR]

We developed tsunami fragility functions using three sources of damage data from the 2018 Sulawesi tsunami at Palu Bay in Indonesia obtained from (i) field survey data (FS), (ii) a visual interpretation of optical satellite images (VI), and (iii) a machine learning and remote sensing approach utilized on multisensor and multitemporal satellite images (MLRS). Tsunami fragility functions are cumulative distribution functions that express the probability of a structure reaching or exceeding a particular damage state in response to a specific tsunami intensity measure, in this case obtained from the interpolation of multiple surveyed points of tsunami flow depth. We observed that the FS approach led to a more consistent function than that of the VI and MLRS methods. In particular, an initial damage probability observed at zero inundation depth in the latter two methods revealed the effects of misclassifications on tsunami fragility functions derived from VI data; however, it also highlighted the remarkable advantages of MLRS methods. The reasons and insights used to overcome such limitations are discussed together with the pros and cons of each method. The results show that the tsunami damage observed in the 2018 Sulawesi event in Indonesia, expressed in the fragility function developed herein, is similar in shape to the function developed after the 1993 Hokkaido Nansei-oki tsunami, albeit with a slightly lower damage probability between zero-to-five-meter inundation depths. On the other hand, in comparison with the fragility function developed after the 2004 Indian Ocean tsunami in Banda Aceh, the characteristics of Palu structures exhibit higher fragility in response to tsunamis. The two-meter inundation depth exhibited nearly 20% probability of damage in the case of Banda Aceh, while the probability of damage was close to 70% at the same depth in Palu. [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]

This paper presents post-tsunami survey results describing the impacts of the 2011 East Japan tsunami in Yos Sudarso Bay, Papua, Indonesia. Although the far-field tsunami had a height of 0.8 m measured at a tide gauge inside the small U-shaped bay, it severely damaged four villages scattered along the bay. Detailed numerical analysis was carried out to explain the damages. We used a well-verified source model in the near- and far-field cases to model the propagation of the tsunami southward in the Pacific until reaching Papua Island, Indonesia. The numerical analyses demonstrate two causes of damage in the bay: the maximum tsunami velocity (3.5 m s–1) floated the houses in the villages of Tobati and Enggros and caused them to collapse, and water trapped by the hook-shaped peninsula amplified the tsunami height at the Holtekamp and Hanurata. [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]

Change detection between images is a procedure used in many applications of remote sensing data. Among these applications, the identification of damaged infrastructures in urban areas due to a large-scale disaster is a task that is crucial for distributing relief, quantifying losses, and rescue purposes. A crucial consideration for change detection is that the images must be co-registered precisely to avoid errors resulting from misalignments. An essential consideration is that some large-magnitude earthquakes produce very complex distortions of the ground surface; therefore, a pair of images recorded before and after a particular earthquake cannot be co-registered accurately. In this study, we intend to identify changes between images that are not co-registered. The proposed procedure is based on the use of phase correlation, which shows different patterns in changed and non-changed areas. A careful study of the properties of phase correlation suggests that it is robust against misalignments between images. However, previous studies showed that, in areas with no-changes, the signal power in the phase correlation is not concentrated in a single component, but rather in several components. Thus, we study the performance of the ℓ 1 -regularized logistic regression classifier to identify the relevant components of phase correlation and learn to detect non-changed and changes areas. An empirical evaluation consisting of identifying the changes between pre-event and post-event images corresponding to the 2018 Sulawesi Indonesia earthquake-tsunami was performed for this purpose. Pairs of visible and near-infrared (VNIR) spectral bands of medium-resolution were used to compute the phase correlation to set feature space. The phase correlation-based feature space consisted of 484 features. We evaluate the proposed procedure using a damage inventory performed from visual inspection of optical images of 0.5-m resolution. A third-party provided the referred inventory. Because of the limitation of medium-resolution imagery, the different damage levels in the damage inventory were merged into a binary class: "changed" and "non-changed". The results demonstrate that the proposed procedure efficiently reproduced 85 ± 6% of the damage inventory. Furthermore, our results identified tsunami-affected areas that were not previously identified by visual inspection. • Phase correlation and sparse model to identify changes in urban areas • The procedure does not require image registration. • Designed for areas with very complex ground deformation due to earthquakes • The procedure achieved an averaged overall accuracy of 85%. [ABSTRACT FROM AUTHOR]

• Tsunami Analytical fragility curves for pre-code buildings were developed. • An integrated tool has been developed to construct probabilistic fragility curves. • Structural damage of buildings was accounted for the structural capacity curve. From the last large seismic movements generated in the Pacific Ocean, it has been demonstrated that tsunamis have a high potential of destruction of buildings on the coasts. In that sense, although along the Pacific Colombian coast several earthquakes that have generated tsunami were reported during the last century, there are no local data to quantify the physical damage that future events could produce on nearby cities. For the purpose of establishing tools to increase the risk management capabilities along the Colombian-Ecuadorian Pacific Coast, this paper summarizes a research aimed to describe a new integrated methodology to obtain fragility curves for local buildings at risk of a tsunami. This task was accomplished using computational nonlinear structural analysis, combined with a Monte Carlo statistical algorithm to obtain numerical probabilistic fragility curves. The structural capacity of buildings was obtained from nonlinear structural simulations using the finite element method, to calculate later the damage of each building. Two different damage models to estimate the probability of damage were tested in order to evaluate the one that better represents the structural behavior of buildings, in which the tsunami was considered in the structural model as forces scattered to different heights, thus representing different flow depths. As a result, a methodology was developed to calculate theoretical probabilistic fragility curves for the typical structures found in the Colombian Pacific coast, although this methodology could be expanded to any structural system located in any region around the world. As an illustrative example, the methodology proposed in this research was applied to a typical reinforced concrete building, obtained from a previous work in which all the Tumaco city buildings were characterized according to their structural features. [ABSTRACT FROM AUTHOR]

The fine resolution of synthetic aperture radar (SAR) images enables the rapid detection of severely damaged areas in the case of natural disasters. Developing an optimal model for detecting damage in multitemporal SAR intensity images has been a focus of research. Recent studies have shown that computing changes over a moving window that clusters neighboring pixels is effective in identifying damaged buildings. Unfortunately, classifying tsunami-induced building damage into detailed damage classes remains a challenge. The purpose of this paper is to present a novel multiclass classification model that considers a high-dimensional feature space derived from several sizes of pixel windows and to provide guidance on how to define a multiclass classification scheme for detecting tsunami-induced damage. The proposed model uses a support vector machine (SVM) to determine the parameters of the discriminant function. The generalization ability of the model was tested on the field survey of the 2011 Great East Japan Earthquake and Tsunami and on a pair of TerraSAR-X images. The results show that the combination of different sizes of pixel windows has better performance for multiclass classification using SAR images. In addition, we discuss the limitations and potential use of multiclass building damage classification based on performance and various classification schemes. Notably, our findings suggest that the detectable classes for tsunami damage appear to differ from the detectable classes for earthquake damage. For earthquake damage, it is well known that a lower damage grade can rarely be distinguished in SAR images. However, such a damage grade is apparently easy to identify from tsunami-induced damage grades in SAR images. Taking this characteristic into consideration, we have successfully defined a detectable three-class classification scheme. [ABSTRACT FROM AUTHOR]