ABSTRACT Potential earthquake source models have been developed to estimate and assess tsunami hazards affecting Japanese coastal communities along the Sea of Japan. In contrast, a source model for a historical event presumed to have occurred in the Hokkaido region was previously proposed based on tsunami deposit distributions combined with numerical simulations. The estimated slip amount for this event exceeds those assumed in existing hazard assessments, although the maximum slip considered in such assessments is generally expected to encompass that of the largest historical earthquakes. To investigate this inconsistency, this study reevaluates the historical event using numerical tsunami simulations that incorporate uncertainties in earthquake source parameters and coastal topography. The results indicate that the slip amount required to reproduce inundation extents inferred from tsunami deposits is strongly influenced by locally elevated coastal features, whose conditions at the time of the event remain highly uncertain. When these elevated features are excluded from simulations, the required slip amount is substantially reduced but remains on the order of 10 m. These findings indicate that slip amounts associated with the historical event may have been overestimated in previous studies and that large slips on the order of 10 m are required for hazard assessments .

Traditional tsunami prediction relies on binary classification using machine learning, which lacks physical interpretability and extrapolation capability. This study introduces a novel probabilistic framework for tsunami genesis characterization using Multivariate Extreme Value Theory (MEVT) and Archimedean copula dependence modeling. We analyse an expanded dataset of 1,002 significant earthquakes [Formula: see text] drawn from the USGS/NEIC catalog spanning 1976-2022 and supplemented with moment-magnitude conversions following Scordilis (2006), yielding 391 confirmed tsunami events (39.0%). Generalized Extreme Value and Generalized Pareto distributions are fitted to the marginal parameters; negative shape parameters [Formula: see text] confirm light-tailed magnitude behavior consistent with tectonic fault-dimension constraints. Among three competing Archimedean families, the Clayton copula provides the best fit for magnitude-significance dependence ([Formula: see text] = 2.367, AIC = -725.17, AIC weight = 98.4%) with strong lower tail dependence ([Formula: see text] = 0.746, Kendall’s [Formula: see text] = 0.542), while magnitude-depth and depth-significance pairs display near-independence best described by the Frank copula. These bivariate structures are embedded in a trivariate nested Gumbel-Hougaard copula for complete joint modelling. A closed-form limit state equation is derived via the First Order Reliability Method (FORM): [Formula: see text], where inverse depth contributes 26.4% of total variance. Cross-validation yields AUC = 0.607 ± 0.038, Brier score = 0.233, and ECE = 0.031. This framework provides engineers with a physics-based, directly calculable tool for probabilistic tsunami hazard assessment (PTHA), bridging structural reliability theory with seismological practice.

An integrated framework for pluvial flood and tsunami hazard assessment at coastal sites: application to cultural heritage

Purpose This study examines household-level tsunami preparedness in three coastal villages in Sirimau District, namely, Galala, Hative Kecil and Pandan Kasturi along Ambon Bay, Indonesia, an area highly exposed to seismic and tsunami hazards. The study aims to assess preparedness conditions and identify gaps in the capacity of households to mobilize resources during tsunami emergencies. Design/methodology/approach The study employs household survey data collected from 100 respondents. A composite preparedness index adapted from the LIPI, UNESCO and ISDR framework was used to evaluate preparedness across four dimensions consisting of disaster knowledge, emergency response planning, early warning systems and resource mobilization. Pearson correlation analysis was conducted to examine the relationship between resource mobilization and other preparedness dimensions. Findings The results show that communities demonstrate relatively strong levels of disaster knowledge, emergency planning and early warning awareness, all of which fall within the ready category. However, resource mobilization remains at the almost ready level, reflecting limitations in financial preparedness, uneven participation in evacuation drills, and restricted access to transportation during emergencies. These findings reveal a mobilization gap in which households possess basic preparedness knowledge but face constraints in translating that knowledge into effective response capacity. Originality/value By highlighting the role of resource mobilization as a critical component of tsunami preparedness, this study contributes new empirical insights into disaster resilience in archipelagic coastal settings and provides practical implications for strengthening preparedness strategies in tsunami exposed communities.

Megathrusts host Earth's largest earthquakes. Understanding the physical conditions controlling their rupture dynamics is critical for assessing seismic and tsunami hazards. These earthquakes often display complex rupture dynamics, exemplified by the 2011 Tohoku-Oki earthquake, which exhibited multiple rupture episodes, depth-dependent seismic radiation, and substantial tsunamigenic slip near the trench. However, how such complexity arises from preexisting physical conditions remains uncertain. Here, we demonstrate that the observed rupture complexity of the Tohoku-Oki earthquake can spontaneously and self-consistently emerge, driven by rapid coseismic frictional restrengthening and data-informed fault heterogeneity. We use an ensemble of 3D dynamic rupture simulations to identify that mixed downdip pulse-like and updip crack-like rupture are driven by dynamic stress redistribution with episodic rupture reactivation. By featuring low fault strength compared to its dynamic stress drop, a preferred model can consistently reproduce the observed complex depth-dependent propagation speeds, multiple rupture fronts as imaged by back-projection, and large tsunamigenic slip at the trench. Our findings demonstrate that preexisting fault heterogeneity conjointly with dynamic frictional weakening and restrengthening drives seemingly unexpected megathrust rupture complexity, highlighting the need to include dynamic effects into physics-based seismic and tsunami hazard assessments of future earthquakes.

Tsunamis pose a major hazard, exaggerated by large floating debris within the flow. However, understanding of how finer sediment (sand, silt and clay) influences the tsunami flow remains incomplete, introducing uncertainty in predictive models of their impact. Typically, a turbulent and dilute tsunami wave is assumed, but the differing conditions at the flow front have yet to be quantified. Here, videos of the 2011 Tōhoku-oki tsunami in the Sendai Plain, Japan, were analysed to document trends in the properties of the tsunami flow front. Results reveal rapid temporal and abrupt spatial changes in velocity and the development of a steep gradient to the flow front (∼25–59°). Deposits reveal the tsunami flow had a high mud content and caused almost continuous erosion for at least 2 km inland. This evidence shows that a highly cohesive flow with a dense debritic head formed in the mid-shore region, transforming from an initially turbulent flow through the entrainment of cohesive material. The altered hydrodynamics and the greater force exerted by a dense debritic head highlight the need to incorporate debritic heads into tsunami hazard assessments on mud-rich coastlines, where the hazard will be enhanced.

Abstract. Landslide-Tsurrogate v1.0 is an open-source Python and MATLAB tool that helps scientists quickly estimate the tsunami hazards generated by submarine landslides. Instead of running thousands of heavy deterministic numerical simulations, the software builds surrogate models that reproduce the main results with a fraction of the computational cost. The method relies on a mathematical approach called generalized polynomial chaos expansion, which efficiently explores how uncertain landslide parameters affect tsunami generation. Users can perform sensitivity analyses, identify the most influential parameters, and quantify the variability of possible outcomes. The tool includes a Jupyter Notebook User Manual and interactive MATLAB and Jupyter Notebook interfaces, making it easy to understand the methodology, set up the surrogate simulations and visualize the results. The Landslide-Tsurrogate v1.0 model's performance is demonstrated through a real-world test case involving five zones in Mayotte (France). For this application, the surrogate models achieve convergence with only 135 deterministic simulations per zone and produce probabilistic results in less than 2 s within the user-friendly interfaces used on a basic laptop, demonstrating the computational efficiency of the approach. Beyond this example, the framework can be applied to any coastal region prone to submarine landslides. By combining physical modeling, statistical analysis, and user-friendly design, Landslide-Tsurrogate v1.0 enables faster and more transparent probabilistic tsunami hazard assessments.

Extreme hazards such as earthquake and ensuing tsunamis can pose significant threats to offshore infrastructures, among which bridges are particularly vulnerable due to their locations. Accurate assessment of bridge performance under such events is crucial to enhance structural safety. In this study, the fragility method was employed to evaluate bridge capability against combined hazard effects, with three variables introduced to capture multi-hazard intensity. The vector-valued method was used to quantify bivariate tsunami intensities, with different fragility functions compared in their fitting capability. A new fragility form was proposed for earthquake-tsunami scenarios, with the system-level fragility also examined via multiple bridge components. A case study was conducted to compare the effectiveness of various functions to isolated bridges. The component-level fragility shows an inconsistent development with increasing seismic magnitudes but consistent trends with tsunami intensity. The comparison analysis implies the highest fitness of log-sum model, while the proposed method yields consistent outcomes despite the unified factor. System-level fragility results indicate that isolated bridges have notable vulnerability due to multi-component contributions. Further, the expected damage ratio was assessed and shows notable sensitivity to spectral acceleration and relative wave height, as opposed to the limited influences from water depths. This study provides preliminary guidance for estimating the seismic-tsunami fragility of isolated bridges using complex intensity sets. • Three-variable intensity measure was introduced for random sampling and fragility analysis. • Bivariate tsunami intensity was used for the joint effects of water depth and wave height. • Vector-valued functions were compared in terms of seismic-tsunami fragility fitting capability. • System-level fragility was assessed by combining SOLM with multi-mode failures of girders. • Log-sum model has the best fitting performance in seismic-tsunami hazard fragility.

Abstract Machine learning is emerging as a promising strategy for modeling tsunami inundation at reduced computational cost. To enable probabilistic outputs that capture emulator uncertainty, we employ an ensemble of stochastic encoder‐decoder emulators with dropout‐based stochastic forward pass. Training and testing is conducted on an extensive Mediterranean Sea tsunami simulation data set comprising multiple earthquake source regions with variable magnitude, location, and focal mechanism, including near‐field events with local coseismic deformation. For the two target sites Catania and Siracusa in Sicily, we achieve good performance for relatively sparse training sets of only a few hundred events. We demonstrate the potential of our emulators to accurately model inundation from a range of tsunami sources needed to perform probabilistic tsunami hazard assessment (PTHA), reducing the computational cost of PTHA by a factor of 30–100, and estimating the uncertainty introduced by the emulator. Comparing probabilistic hazard maps and curves from our emulation with those from the full simulation ensemble and importance sampling Monte Carlo approach, we demonstrate the efficacy of emulators in capturing the spatial distribution and magnitude of inundation needed in overland tsunami hazard assessment.

Spatial assessment of coastal community resilience to volcanic tsunami hazards: evidence from the 2018 Anak Krakatau eruption in South Lampung, Indonesia

The Shumagin Gap, a creeping segment of the Alaska subduction zone characterized by tsunamigenic structures, experienced a deep rupture during the July 2020 M7.8 earthquake. However, shallow slip behavior and the upper boundary of the rupture remain poorly understood. Here we utilize controlled-source electromagnetic data to image subsurface electrical resistivity, investigating the role of fluids in modulating megathrust locking state within the Shumagin Gap. Results reveal pronounced trench-normal heterogeneity in electrical resistivity both along the shallow plate interface and within the overriding plate, showing fluid presence but low overall porosity at the interface. An observed conductive channel extending into the overriding plate may facilitate upward fluid drainage. Our findings suggest that the volumes of fluids and inferred pore pressures are not sufficient to explain megathrust creep at the Shumagin Gap. Rather, the intricate interplay between heterogeneous structure and fluid distribution contributes to the region’s seismogenic behavior and tsunami hazards, particularly in the shallow portion of the megathrust.

Tsunamis from large subduction earthquakes pose severe coastal hazards, yet their genesis near the trench remains poorly constrained by land-based seismic geodetic data and distant deep-water sensors. Following the 29 July 2025 magnitude 8.8 Kamchatka earthquake, the NASA/CNES Surface Water and Ocean Topography (SWOT) satellite captured a distinct train of short-wavelength tsunami waves, which we link to near-trench tsunamigenesis. Sensitivity analyses of earthquake slip indicated tsunamigenesis within 10 kilometers of the trench, an inference not attainable from land seismology and geodesy or sparse deep-water seafloor pressure records alone. These results provide the first high-resolution, two-dimensional spaceborne observation directly linking the measured dispersive tsunami wavefield to near-trench tsunamigenesis, extending earlier model- and gauge-based inferences. They establish SWOT as a constraint on source processes, with implications for tsunami hazard science and subduction-zone geodynamics.

​Tsunamis pose severe and time-critical risks to densely populated coastal cities, where limited warning times and infrastructure constraints demand carefully coordinated evacuation strategies. This study develops an integrated, risk-aware optimization framework that jointly considers vertical and horizontal sheltering options together with mixed pedestrian-vehicular evacuation dynamics. The proposed mixed-integer second-order cone programming (MISOCP) model simultaneously determines vertical shelter location, evacuee assignment, road-use designation for pedestrians and vehicles, and route selection under congestion, capacity, and budget constraints. Vehicle travel times incorporate congestion effects through a convex flow-dependent function, while pedestrian routing ensures convergent and conflict free evacuation paths. A risk-minimization objective accounts for tsunami hazard levels, inundation conditions, and spatial exposure, prioritizing safer evacuation routes over purely time-based approaches. The model is applied to İstanbul’s Büyükçekmece district, one of the areas most vulnerable to tsunami impact following a major Marmara Sea earthquake. Using real geographic, demographic, and infrastructure data, we evaluate multiple budget and demand scenarios to examine the model’s sensitivity to shelter investment decisions and varying proportions of pedestrian and vehicular evacuees. Results show that opening strategically located vertical shelters sub stantially reduces total evacuation risk and alleviates congestion in critical zones. Increasing vertical shelter investment provides diminishing returns beyond a moderate budget level, indicating the existence of an efficient investment thresh old. Scenario analysis further reveals that mixed-mode evacuations can generate significant congestion on shared road segments; the model mitigates these effects through modal separation and speed adjustments. Overall, the proposed framework offers robust, context-aware decision support for tsunami-prone urban regions.

The southern coast of Trenggalek District is prone to tsunami disasters, because it is located north of the megathrust zone. This study aims to map the level of tsunami inundation risk in The Trenggalek District, especially of the coast of Watulimo Subdistrict. This mapping was done with the help of Model Builder in ArcGIS software, using scenarios of tsunami wave heights on the shore of 1 m, 2 m, 5 m, 15 m, 27 m, and 30 m. The risk map of tsunami inundation was obtained by combining the tsunami hazard map with the vulnerability map. The study results show that the area of tsunami inundation at a high-altitude scenario with wave heights of 1 m has low, medium, and high-risk levels covering 0.254 km², 0.240 km², and 0.032 km², respectively. In the 27 m scenario, which is the worst-case scenario according to Meteorology, Climatology, and Geophysics Agency (BMKG), the areas of inundation at low, medium, and high-risk levels reach 23.032 km², 16.471 km², and 7.904 km², respectively. In this 27 m scenario, four villages in Watulimo Subdistrict are almost entirely inundated by the tsunami. The results of this study are expected to be used as the material for tsunami disaster mitigation in the Trenggalek District.

Abstract Tsunami deposits offer valuable insights into the frequency, magnitude, and potential sources of historical and paleo‐tsunamis. However, previous studies have often overlooked the interaction between tsunamis and tides in sediment simulations, leaving a significant gap in evaluating tsunami‐induced inundation and sediment transport under tidal conditions. In this study, we investigated the impact of tides on tsunami‐induced sediment transport associated with potential earthquakes (∼Mw 9.0) from the Manila Subduction Zone (MSZ) in the South China Sea (SCS). Using the forward model COMCOT‐SED, simulations were conducted at a reported tsunami deposit site in the Qing'ao Embayment on Nan'ao Island. The experiments include scenarios incorporating time‐varying tidal effects at four different phases: high tide, low tide, flood tide, and ebb tide. Our findings reveal that tsunami‐tide interactions at different tidal phases significantly affect nearshore tsunami wave height, and nonlinear effects are most pronounced during the ebb tide scenario. The tidal level is the primary factor controlling onshore inundation and sediment distribution, while tidal current velocity and direction have a secondary influence. These interactions primarily influence sediment deposition and erosion processes by altering water depth and flow velocity, affecting volume flux and sediment transport rate during the tsunami flooding process. The results underscore the importance of considering the effects of tidal phases and sea level variation on tsunami inundation and deposition onshore when interpreting historical and paleo‐tsunamis with tsunami deposits. This study provides a quantitative understanding of tidal levels and current influences on tsunami hazard assessments and paleo‐tsunami research, offering critical insights for future studies.

ABSTRACT The Guyana Basin developed in an evolving tectonic setting, but a lack of subsurface data has limited understanding of its tectonic and sedimentary evolution through time. This study uses extensive, newly available 2D seismic reflection and exploration well data to develop a regional tectonostratigraphic framework, to reconstruct sediment accumulation and constrain sediment routing to the basin through time. Five megasequences (MS‐0 to MS‐4; oldest to youngest) are defined, each representing a distinct phase of basin fill. MS‐0 corresponds to the pre‐ and syn‐rift phases of the Central Atlantic rift, marked by folded volcano‐sedimentary units. MS‐1 (Middle Jurassic–Aptian) represents the post‐rift phase of the Central Atlantic, characterised by low sedimentation rates and isolated depocenters likely fed by nearby continental sources from the Guyana margin, and possibly adjacent conjugate margins in North America and Africa. MS‐2 (Aptian–Albian) represents the Equatorial Atlantic syn‐rift and transform phase, marked by the development of the Equatorial Atlantic Fracture Zone (EAFZ) and the emergence of a southeastern slope depocenter. MS‐3 (Albian–Middle Miocene) corresponds to the Equatorial Atlantic passive margin phase of the basin, with sediment transported through large canyon systems, likely indicating increased riverine flux from the Guyana margin. MS‐4 (Upper Miocene–Recent) reflects ongoing passive margin sedimentation and the development of a fold‐and‐thrust belt in the northwestern basin due to the collision between the Caribbean and South American plates. About 41% of the total sediments of the Guyana Basin were deposited in the last ~11.6 Myr, driven by a ~40 MTa −1 sediment flux primarily from the Amazon River–Guiana Current system. This order of magnitude increase in sedimentation rate in the Miocene corresponds with the onset of widespread mass transport deposits, indicating that rapid sedimentation and disequilibrium compaction preconditioned the continental slope for repeated failure. This has important implications for slope stability, with large‐volume submarine landslides forming a potentially significant landslide and tsunami hazard for subsea infrastructure and coastal populations.

Understanding near-trench interseismic deformation at subduction zones is crucial for assessing the hazards of giant earthquakes, especially trench-breaking tsunamigenic earthquakes, such as the 2011 Tohoku earthquake. However, interseismic locking preceding such trench-breaking tsunamigenic events remain poorly constrained due to the limited sensitivity of onshore geodetic observations. Here, we present seafloor geodetic observations along the southwestern Kuril trench, a region with the potential for large coseismic ruptures reaching the trench, as inferred from historical tsunami records. Our results show high slip-deficit rates near the trench during 2019–2024 and suggest that a slip deficit of 20.5–30.0 m may have accumulated over the past ~400 years, considering the long-term low seismic activity in this region. These findings imply future recurrence of a megathrust earthquake (Mw ~8.8) with rupture reaching the Kuril trench. Furthermore, they highlight the importance of seafloor geodetic monitoring for seismic and tsunami hazard assessment at subduction zones. Seafloor geodetic monitoring suggests the southwestern Kuril trench offshore Hokkaido, Japan, has accumulated substantial slip deficits through shallow plate interface coupling, which could indicate potential for a rare megathrust earthquake

Abstract Hachijo Island, part of the Izu-Ogasawara Islands, is situated near major plate boundaries, including the Nankai Trough, Sagami Trough, and Izu-Ogasawara Trench, making it highly susceptible to tsunamis triggered by earthquakes in these regions. Despite this vulnerability, limited tsunami research has been conducted in the area. This study aimed to uncover the tsunami history of Hachijo Island through geological surveys and numerical modeling, providing insights into potential tsunami sources. The field investigation at west coast revealed an event layer characterized by rounded gravels which is comparable to previously recognized tsunami deposits in this area. Radiocarbon dating results indicate that these deposits formed after the seventeenth century. Based on the dating results together with sedimentary features such as cracks, the 1703 Genroku earthquake and tsunami is more likely because geological evidence fits the historical descriptions. Numerical simulations for the 1703 Genroku tsunami were conducted to evaluate the fault source responsible for the tsunami deposits. The runup height observed at west coast could not be reproduced using a previously proposed fault model. This research developed several revised fault models by changing parameters to reflect their influence on Hachijo Island. Among these, one model successfully reproduced the impact of tsunamis on both Hachijo Island and surrounding regions. The geological records of Hachijo Island provide key constraints for tsunami source modeling and highlight the importance of remote island data in tsunami hazard assessments. This study improves our understanding of tsunami risks in regions and provides a refined model to guide future research and disaster mitigation strategies.

On 22 December 2018, a devastating tsunami struck Sunda Strait, Indonesia without warning. This incident claimed many lives, particularly in the Pandeglang Regency. Therefore, it is necessary to conduct a tsunami reconstruction to analyze the tsunami risk, which can later be used as a basis for tsunami hazard mitigation. The tsunami reconstruction was carried out using numerical simulations considering seven landslide scenarios for calibration and two inundation model scenarios for comparison. In this study, we introduced a detailed inundation modeling and local risk assessment achieved by incorporating land use data from the national land cover map. The model results were validated against observed water surface elevation and field survey data of inundation heights. It was noted that the best scenario has a landslide volume of 0.326 km 3 . Among the two inundation model scenarios, it was found that the scenario with spatially varying Manning values was the best model with inundation heights accuracy of 95.39%. From the simulation results, the area with the highest tsunami wave of approximately 7 m was Tanjung Lesung. This result aligns with the tsunami risk analysis, suggesting that Tanjung Lesung was the most extensively affected area by the tsunami, with a total affected area of 3.225 km 2 and a risk level 1 area of 0.494 km 2 . This study highlights the importance of varying spatial characteristics of land use in the tsunami inundation modeling and risk analysis that is often neglected, which can be extended to other areas surrounding the Anak Krakatau.

Tsunami hazards pose persistent threats to low-lying coastal settlements in Indonesia, where physical exposure and social vulnerability often intersect. This study integrates tsunami inundation modelling using the Cornell Multi-grid Coupled Tsunami (COMCOT) model with a community preparedness assessment to develop a comprehensive understanding of tsunami risk in Tanjung Benoa, Bali, Indonesia. The COMCOT simulation, based on a potential Mw 8.5 earthquake scenario south of Bali, indicates a maximum inundation depth of up to 14 m, where the tsunami waves are projected to traverse the Tanjung Benoa peninsula, with the first tsunami arrival being expected within 24 min after rupture. A social survey involving 327 household heads across six neighborhoods was conducted using the Tsunami Ready Community framework (UNESCO–IOC) to evaluate awareness, preparedness, and response capacities. The overall Preparedness Index (PI) reached 78, categorized as “Ready”, indicating moderate readiness but uneven distribution across neighborhoods. This integrated approach highlights that physical modelling alone is insufficient to capture real tsunami risk without incorporating social preparedness dimensions. The study provides actionable insights for local disaster management authorities and supports the strengthening of the UNESCO–IOC Tsunami Ready Community indicators in Tanjung Benoa. The framework demonstrated here can serve as a replicable model for other coastal communities pursuing sustainable and data-driven tsunami resilience strategies.