Volcanic Tsunamis Research Articles

Physical experiments of waves generated by submerged steam eruptions with applications to volcanic tsunamis.

The tsunamigenic potential of underwater volcanic eruptions is not well understood, even though eruption-generated tsunamis can be devastating. To address how erupted steam bursts from underwater volcanoes generate tsunamis, we present the experiments, using pressurized steam injected vertically into a water tank. Results over various eruption conditions identify three eruption regimes, namely, shallow-, intermediate-, and deep-water eruptions, according to the combined effects of water depths, source strengths, and source durations. The transition between shallow and intermediate eruptions is characterized by critical depths maximizing tsunami wave heights, while the transition between intermediate and deep eruptions is characterized by containment depths inhibiting surface disturbances. Water depth and source intensity are the dominant factors controlling maximum wave amplitudes, more so than aspects of jet duration and condensation. These experiments and supporting dimensional analysis improve our understanding of how underwater volcanic eruptions form tsunamis, while also providing a complete dataset for advancing tsunami generation models.

Classification of Non-Seismic Tsunami Early Warning Level Using Decision Tree Algorithm

Background: Tsunami caused by volcanic collapse are categorized as non-seismic uncommon events, unlike tsunamis caused by earthquakes, which are common events. The traditional tsunami early warning based on the seismic sensor (e.g. earthquake detectors) may not be applicable to volcanic tsunamis because they do not generate seismic waves. Consequently, these tsunamis cannot be detected in advance, and warnings cannot be issued. New methods should be explored to address these non-seismic tsunamis caused by volcanic collapse. Objective: This study explored the potential of machine learning algorithms in supporting early warning level issuing for non-seismic tsunamis, specifically volcanic tsunamis. The Anak Krakatau volcano event in Indonesia was used as a case study. Methods: This study generated a database of 160 collapse scenarios using numerical simulation as input sequences. A classification model was constructed by defining the worst tsunami elevation and its arrival time at the coast. The database was supervised by labeling the warning levels as targets. Subsequently, a decision tree algorithm was employed to classify the warning levels. Results: The results demonstrated that the classification model performs very well for the Major Tsunami, Minor Tsunami, and Tsunami classes, achieving high precision, recall, and F1-Score with very high accuracy of 98%. However, the macro average indicates uneven performance across classes, as there are instances of ‘No Warning’ in some coastal gauges. Conclusion: To improve the model performance in the ‘No Warning’ class, it is necessary to balance the dataset by including more ‘No Warning’ scenarios, which can be achieved by simulating additional scenarios involving very small-volume collapse. Additionally, exploring additional collapse parameters such as dip angle and outlier volume could contribute to developing a more robust classification model. Keywords: Machine Learning, Classification, Volcanic Tsunamis, Early Warning, Decision Tree

Physical Modeling of Tsunamis Generated by Submarine Volcanic Eruptions

AbstractSubmarine volcanic eruptions can induce major local and regional tsunami hazards through varied source mechanisms. Large‐scale experiments of tsunamis generated by submarine volcanic eruptions are conducted to study the cylindrical wave generation and propagation in a three‐dimensional wave basin. A unique volcanic tsunami generator (VTG) was deployed at the bottom of the wave basin to generate volcanic tsunamis with repeatable and controlled source parameters. The physical modeling is based on the generalized Froude number defined with the VTG velocity and the near source water depth. The pneumatically driven vertical stroke motion generates leading elevation waves in the wave basin. The tsunami waves generated by the VTG are measured with a wave gauge array. The wave maker performance is characterized by the dimensionless leading wave amplitudes and periods. The experimental data show the variations of the leading wave amplitude, period, and celerity along the radial propagation distance. The generated cylindrical waves belong to the weakly nonlinear wave regime in the near field with decaying wave amplitude along radial propagation. The attenuation rate of the leading wave exceeds the range from the linear wave theory in runs with higher Froude numbers. The dimensionless leading wave period increases with the dimensionless propagation distance due to the dispersion relation. Empirical equations for the characteristic wave parameters such as amplitudes and periods are derived. The experimental results contribute to the understanding of volcanic tsunami generation processes and may serve rapid volcanic tsunami hazard assessments as well as the advancement and validation of numerical models.

A coupled atmosphere-ocean source mechanism was a predictor of the 2022 Tonga volcanic tsunami

Volcanic tsunamis pose significant threats to nearby coastal communities. Despite extensive research, the mechanism behind tsunami generation remains unclear, and the ability to forecast a destructive tsunami has proven elusive. Here we present findings from the 2022 Tonga volcanic incident, showing that the leading air-pressure wave holds promise as a key predictor of tsunami behavior. We constructed an integrated atmosphere-ocean model to explain the underlying mechanism and validated it with various data, including satellite altimetry measurements. Contrary to prior hypotheses, our results reveal that: (1) the eruption process governs both the air-pressure and tsunami dynamics; and (2) the resulting crater volume controls the volcanic ejecta that produces the air-pressure waves, while the corresponding mass loss in the ocean triggers influxes of water into the crater, generating the tsunami. This study unveils a coupled atmosphere-ocean source mechanism in generating volcanic tsunamis and advocates for incorporating air-pressure sensors into early warning systems.

Tsunami Waves off the Southeastern Coast of Sakhalin Island Caused by the Eruption of the Hunga–Tonga–Hunga–Ha’apai Volcano on 15 January 2022 According to Observations by Bottom Devices of Waves

The observation of waves (sea level oscillations) in the coastal water area of the southeastern coast of Sakhalin Island was carried out from November 2021 to May 2022 using bottom-mounted high-precision ARW 14 K devices with one second discreteness. The analysis of the obtained time series revealed the presence of anomalous waves in the range of tsunami waves in January 2022. It is shown that the waves detected on 15 January 2022 with periods of 15 minutes–4 hours, significantly standing out above the background, are associated with volcanic tsunami waves. Spectral analysis of the time series showed that the maximum in the spectra of sea level oscillations found after the volcanic eruption is close to the period of the first mode of the shelf seiche. This indicates that incoming tsunami waves excite shelf seiches in the coastal zone and contribute to the generation of longshore and transverse seiches into Mordvinov Bay. Time series analysis made it possible to establish that the spectral density of sea level oscillations for the time interval of the beginning of the eruption of the Hunga–Tonga–Hunga–Ha’apai volcano from 21 December 2021 to 11 January 2022 significantly differs from the spectral density for the time of the Plinian volcanic eruption. An analysis of the statistical characteristics of sea level oscillations performed on two–day series in the range of periods 1 min–4 h for background waves and tsunamis showed an increase in the rms by 3.5 times for a time series containing tsunami waves. It has been established that for background time series containing tsunami waves, the coefficients of asymmetry and kurtosis differ little from“Gaussia” values. This indicates that the arrival of tsunami waves from distant sources does not significantly disrupt the stationarity of the wave field in the observation area.

A Review of Tsunamis Generated by Volcanoes (TGV) Source Mechanism, Modelling, Monitoring and Warning Systems

Tsunamis generated by volcanic eruptions have risen to prominence since the December 2018 tsunami generated by the flank collapse of Anak Krakatau during a moderate eruption and then the global tsunami generated by the explosive eruption of the Hunga volcano in the Tongan Archipelago in January 2022. Both events cause fatalities and highlight the lack in tsunami warning systems to detect and warn for tsunamis induced by volcanic mechanisms. Following the Hunga Tonga—Hunga Ha’apai eruption and tsunami, an ad hoc working group on Tsunamis Generated by Volcanoes was formed by the Intergovernmental Oceanographic Commission of UNESCO. Volcanic tsunamis differ from seismic tsunamis in that there are a wide range of source mechanisms that can generate the tsunamis waves and this makes understanding, modelling and monitoring volcanic tsunamis much more difficult than seismic tsunamis. This paper provides a review of both the mechanisms behind volcanic tsunamis and the variety of modelling techniques that can be used to simulate their effects for tsunami hazard assessment and forecasting. It gives an example of a volcanic tsunami risk assessment undertaken for Stromboli, outlines the requirement of volcanic monitoring to warn for tsunami hazard and provides examples of volcanic tsunami warning systems in Italy, the Hawaiian Island (USA), Tonga and Indonesia. The paper finishes by highlighting the need for implementing monitoring and warning systems for volcanic tsunamis for locations with submarine volcanoes or near-shore volcanoes which could potentially generate tsunamis.

Understanding paleo-earthquakes in the Kuril Trench based on Late-Holocene tsunami deposits in the distal region from wave sources, northern Hidaka, Hokkaido, Japan.

Geological evidence, such as tsunami deposits, is crucial for studying the largest rupture zone of the Kuril Trench in Hokkaido, Japan, due to its poor historical record. Although 17th-century tsunami deposits are widely distributed across Hokkaido, the presence of multiple wave sources during that period, including the collapse of Mt. Komagatake, complicates the correlation with their wave sources. Understanding the regional distribution of these tsunami deposits can provide valuable data to estimate the magnitude of megathrust earthquakes in the Kuril Trench. The northern part of Hidaka, Hokkaido, where tsunamis from multiple wave sources are expected to overlap, is distant from the Kuril Trench. To clarify the depositional history of tsunami deposits in such distal areas, evaluating the influence of the depositional environments on the event layer preservation becomes even more critical. We conducted field surveys in Kabari, located in the northern Hidaka region, identifying three sand layers from the 10th to the 17th century and two layers dating beyond 2.3 thousand years ago. The depositional ages of most sand layers potentially correlate with tsunami deposits resulting from the Kuril Trench earthquakes. Utilizing reconstructed paleo-sea level data, we estimated that most sand layers reached approximately 2 m in height. However, it is noteworthy that the latest sand layer from the 17th century exhibited an unusual distribution, more than 3 m in height. This suggests a different wave source as the Mt. Komagatake collapse. The discovery of multiple sand layers and their distributions is crucial to constraining the maximum magnitude of giant earthquakes in the Kuril Trench and understanding the volcanic tsunami events related to Mt. Komagatake.

A Review of Historical Volcanic Tsunamis: A New Scheme for a Volcanic Tsunami Monitoring System

Tsunami monitoring and early warning systems are mainly established to deal with seismogenic tsunamis generated by sudden seafloor fault displacement. However, a global tsunami triggered by the 2022 Tonga volcanic eruption promoted the need for tsunami early warning and hazard mitigation of non-seismogenic tsunamis in coastal countries. This paper studied the spatiotemporal distribution characteristics of historical volcanic tsunamis and summarized high-risk areas of volcanic tsunamis. The circum southwestern Pacific volcanic zone, including the Sunda volcanic belt and the Indo-Australian plate, is a concentrated area of active volcanoes and major volcanic tsunamis. In addition, the challenges associated with adapting seismogenic tsunami techniques for use in the context of volcanic tsunamis were elucidated. At the same time, based on historical records and post-disaster surveys, typical historical volcanic tsunami events and involved mechanisms were summarized. The results show that a majority of volcanic tsunamis may involve multiple generation mechanisms, and some mechanisms show geographical distribution characteristics. The complexity of volcanic tsunami mechanisms poses challenges to tsunami early warning by measuring tsunami sources to evaluate the possible extent of impact, or using numerical modeling to simulate the process of a tsunami. Therefore, a concise overview of the lessons learned and the current status of early warning systems for volcanic tsunamis was provided. Finally, a conceptual scheme of monitoring systems for volcanic tsunamis based on historical volcanoes, real-time volcanic eruption information and sea level data, as well as remote sensing images, was presented.

Development of early warning system for tsunamis accompanied by collapse of Anak Krakatau volcano, Indonesia

Present tsunami warning systems have been specialized for earthquake-generated tsunamis, but rapidly evaluating the tsunamis caused by volcanic eruptions and/or volcanic sector collapses remains a challenge. In this study, we applied a numerical model to the 2018 Anak Krakatau tsunami event, which was generated by the sector collapse, investigated a tsunami prediction skill by the model, and developed a real-time forecasting method based on a pre-computed database for future tsunamis accompanied by such eruption of Anak Krakatau. The database stores spatiotemporal changes in water surface level and flux, which are simulated under various collapse scenarios, for confined areas in the vicinity of potential source. The areas also cover the locations of observation stations that are virtually placed on uninhabited island surrounding the source area. During an actual volcanic tsunami event, a tsunami is expected to be observed at the observation stations. For real-time tsunami forecasting, the most suitable scenarios to reproduce the observed waveforms are searched quickly in the database. The precomputed results under the identified scenarios are further provided as input for rapid tsunami propagation simulation. Therefore, an effective real-time forecasting can be conducted to densely populated coastal areas located at a considerable distance from the source, such as the coasts of Java and Sumatra. The forecasting performance was examined by applying the method for three hypothetical collapse scenarios assuming different sliding directions. We demonstrated that the tsunamis along the coasts were successfully forecasted. Moreover, we showed that the combination of a pre-computed database and the existence of observation stations near the source area was able to produce appropriate tsunami forecasting for the coastal area even in a volcanic event.

Are eruptions reliable precursors to marine volcano collapses?

Volcanoes are sources of numerous threats including lava flows, pyroclastic flows, ash dispersal and landslides or sector collapses. In addition to these commonly known volcanic hazards, volcano-induced tsunamis can occur in the marine environment, introducing a major hazard that can affect populations located far away from the volcanoes. Existing tsunami warning systems generally do not account for volcano-generated tsunamis, due to the multiple source mechanisms that can cause such tsunamis, a limited understanding of precursory signals for these events, and the need for local detection rather than remote sensing. Among these source mechanisms of volcanic tsunamis, sector and lateral collapses are at the high risk-low frequency extreme of risk matrices. Marine volcanoes grow in specific environments, with factors like marine clays, constant full saturation, sediment transport and remobilization, interaction with ocean dynamics, and sea level changes that may impact edifice stability in distinct ways. The majority of historically documented marine volcano collapses occurred at erupting volcanoes, suggesting that eruptions could serve as a remotely detectable warning signal for collapses. However, careful examination of temporal sequences of these examples reveals that collapses do not always follow eruptions. Consequently, there is a need for identifying other, more robust precursors to volcano collapse, in particular in the marine environment, where the consequences of collapses may be widespread.

Physical modeling of spikes during the volcanic tsunami generation

Tsunamis generated by underwater volcanic eruptions are physically modeled in a large three-dimensional wave basin. A unique pneumatic volcanic tsunami generator (VTG) was deployed at the bottom of the wave basin to generate volcanic tsunamis with repeatable source parameters under controlled physical conditions. The volcanic Froude number defined with the VTG eruption velocity and water depth allows to physically model real-world events from slow mud-volcanoes to explosive eruptions. The VTG generates radial N-waves with prescribed vertical stroke motions in the wave basin. Initial three-dimensional water surfaces are reconstructed for the daylighting scenarios. Smooth dome shapes are observed during the submarine volcanic eruption and tsunami wave generation, which is followed by a trough formation at the source. A concentric vertical spike is observed for a specific range of water depths, which is generated by superposition of an inward propagating circular bore on top of the wave generator. The spike can be clustered with different ranges of a dimensionless VTG parameter. With an increasing dimensionless parameter, the spike pattern transitions through three distinct categories: smooth spike, rough spike, and splash spike. The dimensionless spike height and the dimensionless vertical velocity of the spike tip are dependent on the dimensionless VTG parameters. The maximum values of the dimensionless spike height and spike tip velocity are observed in the rough spike regime among all tested experimental scenarios.

First Open-Coast HF Radar Observations of a 2-Phase Volcanic Tsunami, Tonga 2022

We describe results from coastal radar systems that observed anomalous current flows generated by the volcanic eruption in the Tongan archipelago on 15 January 2022 UTC, reporting the first radar detection of a volcanic tsunami. The eruption caused small tsunamis along the western U.S. Coast, generating some damage in a few harbors. The highest tsunami signal in U.S. tide gauge data from the California coast occurred at Arena Cove, with significant heights detected at Port San Luis and Crescent City. We analyze correlated wave orbital velocity detections by High Frequency (HF) radars along the coast between Gerstle Cove and Santa Barbara. Signals observed by the radars indicate that the event had two phases, each with its own distinct genesis: an initial weak surface disturbance, most likely generated by the wave of atmospheric pressure that moved outward from the blast source at just below the speed of sound, followed by a stronger disturbance that arrived approximately 3.5 h later, matching the arrival time for a wave moving entirely through the water from the volcano to the U.S. West Coast. We conclude that this phase consists of a conventional water wave tsunami and weaker waves generated by the pulse. We also report the detection of a small pulse-generated event off the west coast of Florida. Radar observations are compared with water level measurements at nearby tide gauges and a DART buoy, and with observations of barometric pressure. We point out that a Proudman near-resonance at the Tonga Trench is unlikely to explain the second phase observations. Comparison with tide gauge signals at San Francisco, generated by the Krakatoa eruption in 1883, support our conclusions.

Global water level variability observed after the Hunga Tonga-Hunga Ha'apai volcanic tsunami of 2022

Abstract. The eruption of the Hunga Tonga-Hunga Ha'apai volcano on 15 January 2022 provided a rare opportunity to understand global tsunami impacts of explosive volcanism and to evaluate future hazards, including dangers from “volcanic meteotsunamis” (VMTs) induced by the atmospheric shock waves that followed the eruption. The propagation of the volcanic and marine tsunamis was analyzed using globally distributed 1 min measurements of air pressure and water level (WL) (from both tide gauges and deep-water buoys). The marine tsunami propagated primarily throughout the Pacific, reaching nearly 2 m at some locations, though most Pacific locations recorded maximums lower than 1 m. However, the VMT resulting from the atmospheric shock wave arrived before the marine tsunami and propagated globally, producing water level perturbations in the Indian Ocean, the Mediterranean, and the Caribbean. The resulting water level response of many Pacific Rim gauges was amplified, likely related to wave interaction with bathymetry. The meteotsunami repeatedly boosted tsunami wave energy as it circled the planet several times. In some locations, the VMT was amplified by as much as 35-fold relative to the inverse barometer due to near-Proudman resonance and topographic effects. Thus, a meteotsunami from a larger eruption (such as the Krakatoa eruption of 1883) could yield atmospheric pressure changes of 10 to 30 mb, yielding a 3–10 m near-field tsunami that would occur in advance of (usually) larger marine tsunami waves, posing additional hazards to local populations. Present tsunami warning systems do not consider this threat.

How did the Tonga volcanic tsunami on January 15, 2022, affect Chinese coasts?

How did the Tonga volcanic tsunami on January 15, 2022, affect Chinese coasts?

Two Volcanic Tsunami Events Caused by Trapdoor Faulting at a Submerged Caldera Near Curtis and Cheeseman Islands in the Kermadec Arc

AbstractTwo submarine earthquakes (Mw 5.8) occurred near volcanic islands, Curtis and Cheeseman, in the Kermadec Arc in 2009 and 2017. Following both earthquakes, similar tsunamis with wave heights of about a meter, larger than expected from their moderate seismic magnitudes, were observed by coastal tide gauges. We investigate the source mechanism for both earthquakes by analyzing tsunami and seismic data of the 2017 event. Tsunami waveform analysis indicates that the earthquake uplifted a submerged caldera around the islands. Combined analysis of tsunami and seismic data suggests that trapdoor faulting, or sudden intra‐caldera fault slip interacted with magma reservoir deformation, occurred due to magma overpressure, possibly in association with caldera resurgence. The earthquake scaling relationship for trapdoor faulting events at three calderas deviates from that for regular earthquakes, possibly due to the fault‐reservoir interaction in calderas.

Scenario-based modelling of waves generated by sublacustrine explosive eruptions at Lake Taupō, New Zealand

Abstract. Volcanogenic tsunami and wave hazard remains less understood than that of other tsunami sources. Volcanoes can generate waves in a multitude of ways, including subaqueous explosions. Recent events, including a highly explosive eruption at Hunga Tonga-Hunga Ha'apai and subsequent tsunami in January 2022, have reinforced the necessity to explore and quantify volcanic tsunami sources. We utilise a non-hydrostatic multilayer numerical method to simulate 20 scenarios of sublacustrine explosive eruptions under Lake Taupō, New Zealand, across five locations and four eruption sizes. Waves propagate around the entire lake within 15 min, and there is a minimum explosive size required to generate significant waves (positive amplitudes incident on foreshore of > 1 m) from the impulsive displacement of water from the eruption itself. This minimum size corresponds to a mass eruption rate of 5.8×107 kg s−1, or VEI 5 equivalent. Inundation is mapped across five built areas and becomes significant near shore when considering only the two largest sizes, above VEI 5, which preferentially impact areas of low-gradient slope. In addition, novel hydrographic output is produced showing the impact of incident waves on the Waikato River inlet draining the lake and is potentially useful for future structural impact analysis. Waves generated from these explosive source types are highly dispersive, resulting in hazard rapidly diminishing with distance from the source. With improved computational efficiency, a probabilistic study could be formulated and other, potentially more significant, volcanic source mechanisms should be investigated.

Ice tongue calving in Antarctica triggered by the Hunga Tonga volcanic tsunami, January 2022

Ice tongue calving in Antarctica triggered by the Hunga Tonga volcanic tsunami, January 2022

The characteristics of the 2022 Tonga volcanic tsunami in the Pacific Ocean

Abstract. On 15 January 2022, an exceptional eruption of the Hunga Tonga–Hunga Ha'apai volcano generated atmospheric and tsunami waves that were widely observed in the oceans globally, gaining remarkable attention from scientists in related fields. The tsunamigenic mechanism of this rare event remains enigmatic due to its complexity and lack of direct underwater observations. Here, to explore the tsunamigenic mechanisms of this volcanic tsunami event and its hydrodynamic processes in the Pacific Ocean, we conduct statistical analysis and spectral analysis of the tsunami recordings at 116 coastal gauges and 38 deep-ocean buoys across the Pacific Ocean. Combined with the constraints of some representative barometers, we obtain the plausible tsunamigenic origins of the volcano activity. We identify four distinct tsunami wave components generated by air–sea coupling and seafloor crustal deformation. Those tsunami components are differentiated by their different propagating speeds or period bands. The first-arriving tsunami component with an ∼ 80–100 min period was from shock waves spreading at a velocity of ∼ 1000 m s−1 in the vicinity of the eruption. The second component with extraordinary tsunami amplitude in the deep ocean was from Lamb waves. The Lamb wave with a ∼ 30–40 min period radically propagated outward from the eruption site with spatially decreasing propagation velocities from ∼ 340 to ∼ 315m s−1. The third component with a ∼ 10–30 min period was probably from some atmospheric-gravity-wave modes propagating faster than 200 m s−1 but slower than Lamb waves. The last component with a ∼ 3–5 min period originated from partial caldera collapse with dimension of ∼ 0.8–1.8 km. Surprisingly, the 2022 Tonga volcanic tsunami produced long oscillation in the Pacific Ocean which is comparable with that of the 2011 Tohoku tsunami. We point out that the long oscillation is associated not only with the resonance effect with the atmospheric acoustic-gravity waves but more importantly with their interactions with local bathymetry. This rare event also calls for more attention to the tsunami hazards produced by an atypical tsunamigenic source, e.g. volcanic eruption.

Data Assimilation Using High‐Frequency Radar for Tsunami Early Warning: A Case Study of the 2022 Tonga Volcanic Tsunami

AbstractHigh‐frequency (HF) radar monitors the sea surface current velocity and provides information for tsunami early warning. SeaSondeR, an HF ocean radar system in the eastern Tsugaru Strait, Japan, measured the tsunami‐induced current velocity during the 2022 Tonga volcanic tsunami. As an air‐coupled tsunami, the generating mechanism was complex, making it difficult to predict coastal tsunamis using traditional early warning methods. We adopted the tsunami data assimilation (DA) approach, which reconstructs the tsunami wavefield using offshore data and does not require source information, to forecast the coastal tsunami waveforms. Observations from the HF radar and offshore bottom pressure gauges (OBPGs) were used as the input for tsunami DA. The assimilation process started at 09:00 (UTC, hereafter) and forecasts were made at 14:00 and 15:00. The surface current velocity recorded by the HF radar reached the maximum (∼0.25 m/s) at 13:00, which corresponded to a negative phase of ∼2 cm sea level variation observed by OBPGs. We compared the forecasted waveforms with the observed waveforms at Hakodate and Shimokita tide gauges. The assimilation results obtained using HF radar showed a better performance in tsunami forecasting than those using OBPG in this case study. At 14:00, the forecasting accuracy indices were 91% and 67% for the next 2 and 6 hr, respectively. At 15:00, it was 63% and 70% for the next 2 and 6 hr, respectively. We suggest that HF radar could be a good supplement to OBPG for tsunami early warning purposes.

Tsunami Runup and Inundation in Tonga from the January 2022 Eruption of Hunga Volcano

On January 15th, 2022, at approximately 4:47 pm local time (0347 UTC), several weeks of heightened activity at the Hunga volcano 65 km northwest of Tongatapu, culminated in an 11-h long violent eruption which generated a significant near-field tsunami. Although the Kingdom of Tonga lies astride a large and tsunamigenic subduction zone, it has relatively few records of significant tsunami. Assessment activities took place both remotely and locally. Between March and June 2022, a field team quantified tsunami runup and inundation on the main populated islands Tongatapu and Eua, along with several smaller islands to the north, including the Ha’apai Group. Peak tsunami heights were ~ 19 m in western Tongatapu, ~ 20 m on south-eastern Nomuka Iki island and ~ 20 m on southern Tofua, located ~ 65 km S and E and 90 km N from Hunga volcano, respectively. In western Tongatapu, the largest tsunami surge overtopped a 13–15 m-high ridge along the narrow Hihifo peninsula in several locations. Analysis of tide gauge records from Nukualofa (which lag western Tongatapu arrivals by ~ 18–20 min), suggest that initial tsunami surges were generated prior to the largest volcanic explosions at ~ 0415 UTC. Further waves were generated by ~ 0426 UTC explosions that were accompanied by air-pressure waves. Efforts to model this event are unable to reproduce the timing of the large tsunami wave that toppled a weather station and communication tower on a 13 m-high ridge on western Tongatapu after 0500 UTC. Smaller tsunami waves continued until ~ 0900, coincident with a second energetic phase of eruption, and noted by eyewitnesses on Tungua and Mango Islands. Despite an extreme level of destruction caused by this tsunami, the death toll was extraordinarily low (4 victims). Interviews with witnesses and analysis of videos posted on social media suggest that this can be attributed to the arrival of smaller ‘pre tsunami’ waves that prompted evacuations, heightened tsunami awareness due to tsunami activity and advisories on the day before, the absence of tourists and ongoing tsunami education efforts since the 2009 Niuatoputapu, Tonga tsunami. This event highlights an unexpectedly great hazard from volcanic tsunami worldwide, which in Tonga’s case overprints an already extreme level of tectonic tsunami hazard. Education and outreach efforts should continue to emphasize the ‘natural warning signs’ of strong ground shaking and unusual wave and current action, and the importance of self-evacuation from coastal areas of low-lying islands. The stories of survival from this event can be used as global best practice for personal survival strategies from future tsunami.