Modeling the large runup along a narrow segment of the Kaikoura coast, New Zealand following the November 2016 tsunami from a potential landslide

Appendix A. Supplementary data: The following is the Supplementary data to this article: Download Word document (https://ars.els-cdn.com/content/image/1-s2.0-S0029801818318286-mmc1.docx - 3MB).Copyright © 2019 The Authors. The 2016 Mw 7.8 Kaikoura earthquake and consequent tsunami have been controversial because of uncertainty over whether and where the plate interface ruptured and the incapability of the proposed source models to reproduce the near-field runup of 7 m. Existing models identify a wide range of locations for the interface rupture, from on land to offshore, and fail to reproduce runup of 7 m near Kaikoura. To generate the large tsunami peak in Kaikoura tide gauge record and the observed runup height, offshore seafloor movement is necessary, but the offshore extension of the plate-interface rupture and its type, either seismic rupture or a landslide, is uncertain. Here, we propose a submarine landslide in addition to the earthquake source, with the landslide delayed 10–20 min after the earthquake rupture. The landslide volume is 4.5–5.2 km3, located within 173.7–174.3oE (longitude) and 42.6–42.15oS (latitude). Our proposed dual tsunami source successfully reproduces near-field tide gauge records as well as observed near-field runup height of 7 m. We showed that more accurate source models of earthquakes can be achieved by considering observed runup data through runup inversions in addition to waveform inversions.Brunel Research Initiative and Enterprise Fund 2017/18 (BUL BRIEF) at Brunel University Londo

Remote sensing for natural or man-made disasters and environmental changes

Natural and man-made disasters have become an issue of growing concern throughout the world. The frequency and magnitude of disasters threatening large populations living in diverse environments, is rapidly increasing in recent years across the world due to demographic growth, inducing to urban sprawls into hazardous areas. These disasters also have far-reaching implications on sustainable development through social, economic and environmental impact. This chapter summarises three scientific contributions from relevant experiences of the British Geological Survey and the Federico II University of Naples, where remote sensing sensors have been playing a crucial role to potentially support disaster management studies in areas affected by natural hazards. The three cases are: the landslide inventory map of St. Lucia island, tsunami-induced damage along the Sendai coast (Japan) and the landslide geotechnical characterization in Papanice (Italy). For each case study we report the main issue, datasets available and results achieved. Finally, we analyse how recent developments and improved satellite and sensor technologies can support in overcoming the current limitations of using remotely sensed data in disaster management so to fully utilize the capabilities of remote sensing in disaster management and strength cooperation and collaboration between relevant stakeholders including end users

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As demonstrated at Anak Krakatau on December 22nd, 2018, tsunamis generated by volcanic flank collapse are incompletely understood and can be devastating. Here, we present the first high-resolution characterisation of both subaerial and submarine components of the collapse. Combined Synthetic Aperture Radar data and aerial photographs reveal an extensive subaerial failure that bounds pre-event deformation and volcanic products. To the southwest of the volcano, bathymetric and seismic reflection data reveal a blocky landslide deposit (0.214 ± 0.036 km3) emplaced over 1.5 km into the adjacent basin. Our findings are consistent with en-masse lateral collapse with a volume ≥0.175 km3, resolving several ambiguities in previous reconstructions. Post-collapse eruptions produced an additional ~0.3 km3 of tephra, burying the scar and landslide deposit. The event provides a model for lateral collapse scenarios at other arc-volcanic islands showing that rapid island growth can lead to large-scale failure and that even faster rebuilding can obscure pre-existing collapse

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Mass wasting processes : offshore Sumatra

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