Submarine landslide source models consistent with multiple tsunami records of the 2018 Palu tsunami, Sulawesi, Indonesia

Nakata, Kenji; Katsumata, Akio; Muhari, Abdul

doi:10.1186/s40623-020-01169-3

Cited by 51 publications

(57 citation statements)

References 36 publications

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“…Although there are now many tsunami simulations of the 2018 Palu event (e.g., Heidarzadeh et al, 2019;Takagi et al, 2019;Carvajal et al, 2019;Pakoksung et al, 2019;Gusman et al, 2019;Jamelot et al, 2019;Ulrich et al, 2019;Goda et al, 2019;Nakata et al, 2020;Sepúlveda et al, 2020;Liu et al, 2020, see summary of studies characteristics in Table 1), the tsunami mechanism, earthquake, coastal landslides, or both in combination, is still uncertain. In addition, whereas many published models simulate some, or even most, recorded runups around the Bay, the mechanisms are often ad hoc and do not reproduce the timing of tsunami waves from eyewitness accounts or the video evidence.…”

Section: Introductionmentioning

confidence: 99%

New High-Resolution Modeling of the 2018 Palu Tsunami, Based on Supershear Earthquake Mechanisms and Mapped Coastal Landslides, Supports a Dual Source

2021

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The Mw 7.5 earthquake that struck Central Sulawesi, Indonesia, on September 28, 2018, was rapidly followed by coastal landslides and destructive tsunami waves within Palu Bay. Here, we present new tsunami modeling that supports a dual source mechanism from the supershear strike-slip earthquake and coastal landslides. Up until now the tsunami mechanism: earthquake, coastal landslides, or a combination of both, has remained controversial, because published research has been inconclusive; with some studies explaining most observations from the earthquake and others the landslides. Major challenges are the numerous different earthquake source models used in tsunami modeling, and that landslide mechanisms have been hypothetical. Here, we simulate tsunami generation using three published earthquake models, alone and in combination with seven coastal landslides identified in earlier work and confirmed by field and bathymetric evidence which, from video evidence, produced significant waves. To generate and propagate the tsunamis, we use a combination of two wave models, the 3D non-hydrostatic model NHWAVE and the 2D Boussinesq model FUNWAVE-TVD. Both models are nonlinear and address the physics of wave frequency dispersion critical in modeling tsunamis from landslides, which here, in NHWAVE are modeled as granular material. Our combined, earthquake and coastal landslide, simulations recreate all observed tsunami runups, except those in the southeast of Palu Bay where they were most elevated (10.5 m), as well as observations made in video recordings and at the Pantoloan Port tide gauge located within Palu Bay. With regard to the timing of tsunami impact on the coast, results from the dual landslide/earthquake sources, particularly those using the supershear earthquake models are in good agreement with reconstructed time series at most locations. Our new work shows that an additional tsunami mechanism is also necessary to explain the elevated tsunami observations in the southeast of Palu Bay. Using partial information from bathymetric surveys in this area we show that an additional, submarine landslide here, when simulated with the other coastal slides, and the supershear earthquake mechanism better explains the observations. This supports the need for future marine geology work in this area.

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Section: Introductionmentioning

confidence: 99%

New High-Resolution Modeling of the 2018 Palu Tsunami, Based on Supershear Earthquake Mechanisms and Mapped Coastal Landslides, Supports a Dual Source

2021

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“…In the case of the Palu tsunami, the submarine landslide source was implemented by using a combination of models, namely, Titan2D (Pitman et al 2003;Patra et al 2005; Titan2D 2016) and JAGURS (Baba et al 2017), with a multi-landslide location in the bay. The volume of the multi-landslide source varied from 0.02 to 0.07 km 3 among six different locations (Nakata et al 2020). In addition, the TUNAMI-N2 model was applied to model the submarine landslide-induced tsunami in Palu Bay by Pakoksung et al (2019), who proposed that the main source of the landslide that generated the 2018 Palu tsunami was in the northern part of the bay.…”

Section: Introductionmentioning

confidence: 99%

Global Optimization of a Numerical Two-layer Model Using Observed Data: A Case Study of the 2018 Sunda Strait Tsunami

Pakoksung

Suppasri

Muhari³

et al. 2020

Preprint

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Following the eruption of Anak Krakatoa, aconsiderable landslide occurred on the southwestern part of the volcano and, upon entering the sea, generated a large tsunami within the Sunda Strait, Indonesia, on 22 December 2018. This tsunami traveled ~5 km across the strait basin and inundated the shorelines of Sumatra and Java with a vertical runupreaching 13 m. Following the event, observed field data, GPS measurements of the inundation, and multibeam echo soundings of the bathymetry within the strait were collected and publicly provided. Using this dataset, numerical modeling of the tsunami was conducted using the two-layer (soil and water) TUNAMI-N2 model based on a combination of landslidesources and bathymetry data. The two-layer model was implemented to nest the grid system using a finest grid size of 20 m. To constrain the unknown landslide parameters, the differential evolution (DE) global optimization algorithm was applied, which resulted in a parameter set that minimized the deviation from the measured bathymetry after the event. The DE global optimization procedure was effective at determining the landslide parameters for the model with the minimum deviation from the measured seafloor. The lowest deviation from the measured bathymetry was obtained for the best-fitting parameters:a maximum landslide thickness of 301.2 m and a landslide time of 10.8 minutes. The landslide volume of 0.182 km3 estimated by the best-fitting parameters shows that the tsunami flow depth could have reached 3-10 m along the shore with a K value of0.89, although thesimulated flow depths were underestimatedin comparison with the observation data. According to the waveforms, the general wave pattern was well reproduced at tide gauges during the event. A large number of objective function evaluations were necessary to locate the minimum with the DE procedure to fix the grid cell size to 20 m;this limited the accuracy of the obtained parameter values for the two-layer model. Moreover, considering the generalizations in the modeling of landslide movements, the impact landslide time and thickness must be carefully calculated to obtain a suitable accuracy.

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“…The tsunamis are mostly generated by large earthquakes with minor occurrence of volcanic eruptions. Apart from the 2018 submarine landslide-generated tsunami in Sulawesi (Nakata et al, 2020), there is no other record of tsunami generated by submarine landslide in this region. A statistical study of historical tsunamis in the Philippines by Nakamura (1978) showed that the southern Mindanao area which includes the Sulu and Cotabato Trenches has the highest probability of tsunami occurrence, once in 10 years with waves reaching 7.8 m in height.…”

Section: Historical Tsunamis Around Sabahmentioning

confidence: 99%

Assessment Of Tsunami Hazard In Sabah – Level Of Threat, Constraints And Future Work

Tongkul¹,

Roslee²,

Daud³

2020

BGSM

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The coastal areas of Sabah are exposed to far-field earthquake-induced tsunamis that could be generated along the trenches of Manila, Negros, Sulu, Cotabato, Sangihe and North Sulawesi. Tsunami simulation models from these trenches indicated that tsunami waves can reach the coast of Sabah between 40 and 120 minutes with tsunami wave heights reaching up to 3 m near the coast. The level of tsunami threat is high in southeast Sabah due to its narrow continental shelf and proximity to tsunami source in the North Sulawesi Trench. The level of tsunami threat is moderate in north and east Sabah due to their proximity to tsunami source in the Sulu Trench. The level of tsunami threat is low in west Sabah due to its distant location to tsunami source from the Manila Trench. While tsunamis cannot be prevented, its impact on human life and property can be reduced through proper assessment of its threat using tsunami simulation models. Unfortunately, constraints remain in producing a reliable tsunami inundation models due to the lack of high-resolution topography and bathymetry data in Sabah and surrounding seas. It would be helpful if such data can be acquired by the relevant government agencies, at least first, in high threat-level areas, such as Tawau and Semporna districts. In order to properly plan mitigation measures tsunami risk mapping should be intensified in high threat-level areas. The locations of settlements (including water villages), population concentrations, types of buildings and houses, road system, drainage system, harbours, jetties and vegetations (including mangroves) need to be mapped in great detail. Based on the detailed tsunami risk map, targeted vulnerable communities could be given continuous and intensive education and awareness on basic tsunami science and tsunami hazard preparedness.

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Submarine landslide source models consistent with multiple tsunami records of the 2018 Palu tsunami, Sulawesi, Indonesia

Cited by 51 publications

References 36 publications

New High-Resolution Modeling of the 2018 Palu Tsunami, Based on Supershear Earthquake Mechanisms and Mapped Coastal Landslides, Supports a Dual Source

New High-Resolution Modeling of the 2018 Palu Tsunami, Based on Supershear Earthquake Mechanisms and Mapped Coastal Landslides, Supports a Dual Source

Global Optimization of a Numerical Two-layer Model Using Observed Data: A Case Study of the 2018 Sunda Strait Tsunami

Assessment Of Tsunami Hazard In Sabah – Level Of Threat, Constraints And Future Work

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