Coastal landslides in Palu Bay during 2018 Sulawesi earthquake and tsunami

Liu, Philip L.‐F.; Higuera, Pablo; Husrin, Semeidi; Prasetya, Gegar; Prihantono, Joko; Diastomo, Hanif; Pryambodo, Dino Gunawan; Susmoro, Harjo

doi:10.1007/s10346-020-01417-3

Cited by 66 publications

(96 citation statements)

References 24 publications

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“…They estimated the widths of the sharp crescent waves to be 343 and 461 m, and the elevations (trough-to-crest) to be 24.1 m and 28.9 m at locations LS-D and -E, respectively ( Figure 1B). Based on the boat Supplementary Video S39, they estimated that at location -F*, the splash of the outgoing wave was 28.4 m high, and the elevation of the unbroken outgoing wave traveling toward the Boat location was 8.2 m. These estimated wave heights are all much greater than the initial values, on the order of 2-10 m, predicted by Liu et al (2020) using semiempirical methods for the landslide tsunami waves generated at these locations.…”

Section: Video Analysis Overviewmentioning

confidence: 93%

“…Here, for the first time, we demonstrate that to explain the tsunami observations in Palu Bay requires simultaneously modeling both coseismic and landslide sources. We simulate the three coseismic sources discussed above (Jamelot et al, 2019;Socquet et al, 2019;Ulrich et al, 2019), the mapped (rather than hypothetical) landslides (Liu et al, 2020;Takagi et al, 2019), and dual source combinations of these, using numerical models that include frequency dispersion effects (Shi et al, 2012;Ma et al, 2012). We show that dispersion affects the shorter wavelength landslide tsunamis propagating into the deeper waters in the center of Palu Bay.…”

Section: Figure 1 | (A)mentioning

confidence: 99%

“…Their landslide parameters and corresponding wave generation, however, were hypothetical and selected to match observations. To date, only Liu et al (2020) modeled landslide tsunamis from mapped landslide locations, but they: 1) did not model the tsunami generation from the landslides directly, using instead semi-empirical sources, and 2) did not simulate an additional earthquake mechanism. Finally all the landslide modeling studies to date simulated tsunami propagation with a non-dispersive model, which, as we will show, affected results.…”

Section: Figure 1 | (A)mentioning

confidence: 99%

“…200 m resolution grid with two nested 10 m grids in palu city and pantoloan, derived from DEMNAS and BATNAS USGS (2018) and their own (hybrid source with nine subfaults parameterized from satellite data), using Okada (1985) and Tanioka and Satake (1996) (Sepúlveda et al, 2020). Here, as noted by Carvajal et al (2019), Sepúlveda et al (2020), and Liu et al (2020), the tide gauge is located in shallow water inside a harbor basin protected by a slotted seawall/dock (see Supplementary Figure S1 in supplementary material), which is not represented in the available bathymetric data nor in our model grids. While not affecting tide measurements, harbor structures may cause seiching and affect tsunami wave dynamics by modifying their elevation through reflection or dissipation; later in time ( > 650 s), the record may have been affected by waves reflected from the other side of the bay.…”

Section: Tide Gauge Datamentioning

confidence: 99%

“…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%

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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: Video Analysis Overviewmentioning

confidence: 93%

Section: Figure 1 | (A)mentioning

confidence: 99%

Section: Figure 1 | (A)mentioning

confidence: 99%

Section: Tide Gauge Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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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Tsunami Induced by the Strike-Slip Fault of the 2018 Palu Earthquake (Mw=7.5), Sulawesi Island, Indonesia

Satake

Watada

et al. 2020

Preprint

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after an M w 7.5 earthquake. A moment tensor solution obtained by the U.S. Geological Survey (USGS) from W phase waveforms suggests strike-slip faulting with a high dip angle (∼67°) at a shallow depth (∼13 km). The estimated nodal plane strikes approximately north-south (N10°W) with a rake angle of −17°, implying the presence of some normal faulting component.The Palu-Koro fault (Figure 1), the plate boundary between the Makassar block and the North Sula block, is thought to be responsible for the Palu earthquake. Global Navigation Satellite System monitoring shows that the relative velocity on the Palu-Koro fault zone accommodates 42 mm/yr of complex transtensive

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A tsunami generated by a strike-slip event: constraints from GPS and SAR data on the 2018 Palu earthquake

Simons

Broerse

Shen

et al. 2022

Preprint

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A devastating tsunami struck Palu Bay in the wake of the 28 September 2018 M w = 7.5 Palu earthquake (Sulawesi, Indonesia). With a predominantly strike-slip mechanism, the question remains whether this unexpected tsunami was generated by the earthquake itself, or rather by earthquake-induced landslides. In this study we examine the tsunami potential of the co-seismic deformation. To this end, we present a novel geodetic data set of Global Positioning System and multiple Synthetic Aperture Radar-derived displacement fields to estimate a 3D co-seismic surface deformation field. The data reveal a number of fault bends, conforming to our interpretation of the tectonic setting as a transtensional basin. Using a Bayesian framework, we provide robust finite fault solutions of the co-seismic slip distribution, incorporating several scenarios of tectonically feasible fault orientations below the bay. These finite fault scenarios involve large co-seismic uplift (>2 m) below the bay due to thrusting on a restraining fault bend that connects the offshore continuation of two parallel onshore fault segments. With the co-seismic displacement estimates as input we simulate a number of tsunami cases. For most locations for which video-derived tsunami waveforms are available our models provide a qualitative fit to leading wave arrival times and polarity. The modeled tsunamis explain most of the observed runup. We conclude that co-seismic deformation was the main driver behind the tsunami that followed the Palu earthquake. Our unique geodetic data set constrains vertical motions of the sea floor, and sheds new light on the tsunamigenesis of strike-slip faults in transtensional basins.

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Coastal landslides in Palu Bay during 2018 Sulawesi earthquake and tsunami

Cited by 66 publications

References 24 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

Tsunami Induced by the Strike-Slip Fault of the 2018 Palu Earthquake (Mw=7.5), Sulawesi Island, Indonesia

A tsunami generated by a strike-slip event: constraints from GPS and SAR data on the 2018 Palu earthquake

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