Abstract Palermo is a populous city situated on the northern coast of Sicily, bordered by the Tyrrhenian Sea. This central part of the Mediterranean Sea features dramatic bathymetry, numerous subaqueous landslides and is also the epicentre to many subaqueous earthquakes. As such, the region is an area prone to tsunamis. This investigation uses the Cornell Multi-Grid Coupled Tsunami (COMCOT) tsunami modelling package to simulate five near-field landslides, and five near-field earthquakes regarded as worst-case credible scenarios for Palermo. The seismic simulations produced waves on a very small scale, the largest being c. 5 cm at its maximum height, and none of the earthquake-generated tsunami waves produced any measurable inundation. The landslide simulations produced larger waves ranging from 1.9 to 12 m in maximum height, two of which resulted in inundation in areas surrounding the Port of Palermo. Sensitivity analysis identified that fault width and dislocation as well as landslide-specific gravity did have significant influence over maximum wave height, inundation and maximum run-up wave height. There are methodological issues limiting the extent to which this study forms a comprehensive tsunami hazard assessment of Palermo, such as gaps in bathymetric data, computational restrictions and lack of a probabilistic element. These issues are counteracted by the fact that this study does serve as a robust first step in identifying that landslides in the region may produce larger tsunami waves than earthquakes, and that the directionality of mass movement is critical in landslide-driven tsunami propagation in the southern Tyrrhenian region.

Abstract Landslides are common in aquatic settings worldwide, from lakes and coastal environments to the deep sea. Fast-moving, large-volume landslides can potentially trigger destructive tsunamis. Landslides damage and disrupt global communication links and other critical marine infrastructure. Landslide deposits act as foci for localized, but important, deep-seafloor biological communities. Under burial, landslide deposits play an important role in a successful petroleum system. While the broad importance of understanding subaqueous landslide processes is evident, a number of important scientific questions have yet to receive the needed attention. Collecting quantitative data is a critical step to addressing questions surrounding subaqueous landslides. Quantitative metrics of subaqueous landslides are routinely recorded, but which ones, and how they are defined, depends on the end-user focus. Differences in focus can inhibit communication of knowledge between communities, and complicate comparative analysis. This study outlines an approach specifically for consistent measurement of subaqueous landslide morphometrics to be used in the design of a broader, global open-source, peer-curated database. Examples from different settings illustrate how the approach can be applied, as well as the difficulties encountered when analysing different landslides and data types. Standardizing data collection for subaqueous landslides should result in more accurate geohazard predictions and resource estimation.

Abstract For New Zealand, a country straddling the Pacific ‘Ring of Fire’, effective mitigation of the risks posed by tsunamis is an urgent priority. Mitigation measures include evacuation mapping, land-use planning and engineering of tsunami resilient buildings and infrastructure; but for these to be effective, a quantitative estimate of the tsunami hazard is needed. For this purpose we present the New Zealand Probabilistic Tsunami Hazard Model (NZPTHM). The model uses a Monte Carlo method for sampling from the geophysical parameters that constrain the magnitude–frequency distributions of the earthquake sources that can cause tsunamis affecting New Zealand. The sampled parameters are used to construct synthetic catalogues of the source events and the subsequent tsunami heights. Processing of these synthetic catalogues produces hazard curves, describing maximum tsunami height as a function of return period, which include ‘error bars’ (confidence intervals) as determined by the Monte Carlo model. Most practical mitigation measures require inundation modelling, and for this purpose we propose using de-aggregation, a process by which a small set of scenarios can be extracted from the NZPTHM for the purpose of detailed inundation modelling.