On the landslide tsunami uncertainty and hazard

Abstract

Landslides are the second most frequent tsunami source worldwide. However, their complex and diverse nature of origin combined with their infrequent event records make prognostic modelling challenging. In this paper, we present a probabilistic framework for analysing uncertainties emerging from the landslide source process. This probabilistic framework employs event trees and is used to conduct tsunami uncertainty analysis as well as probabilistic tsunami hazard analysis (PTHA). An example study is presented for the Lyngen fjord in Norway. This application uses a mix of empirical landslide data combined with expert judgement to come up with probability maps for tsunami inundation. Based on this study, it is concluded that the present landslide tsunami hazard analysis is largely driven by epistemic uncertainties. These epistemic uncertainties can be incorporated in the probabilistic framework. Conducting a literature analysis, we further show examples of how landslide and tsunami data can be used to better constrain landslide uncertainties, combined with statistical and numerical analysis methods. We discuss how these methods, combined with the probabilistic framework, can be used to improve landslide tsunami hazard analysis in the future.

Probabilistic Tsunami Hazard and Risk Analysis: A Review of Research Gaps

“…Landslide tsunami PTHA (LPTHA) was introduced less than a decade ago (Geist and Lynett, 2014). Its application is often similar to SPTHA (e.g., ten Brink et al, 2006;Lane et al, 2016), but can also be based on geotechnical interpretations with a strong emphasis on expert judgment (e.g., Grilli et al, 2009;Hermanns et al, 2013;Løvholt et al, 2020). Salamon and Di Manna (2019) derive empirical scaling relations for landslides triggered by onshore earthquakes.…”

“…This can be done using landslide run-out information alone (e.g., Salmanidou et al, 2017), which consequently yields broad epistemic uncertainties in LPTHA. By using tsunami information, such uncertainties can be drastically reduced (e.g., Gylfadóttir et al, 2017;Kim et al, 2019;Løvholt et al, 2020). In practice, however, very few landslide tsunami data are available.…”

Tsunami risk management for crustal earthquakes and non-seismic sources in Italy

“…Among all tsunamigenic earthquakes, the "tsunami earthquakes" deserve particular attention since they generate larger tsunamis than expected given their seismic moment magnitude, and both their mechanical process and frequency of occurrence are not completely understood [15]. Atypical sources also include all the other non-seismic tsunamigenic sources like sub-aerial near-coast and submarine landslides, a wide range of volcanic phenomena occurring both during eruptions or in non-eruptive periods (pyroclastic flows-fast currents of hot gas and volcanic materials, submarine explosions, caldera collapses, and flank instability are the most important ones), and meteotsunamis-tsunamis generated by atmospheric pressure disturbances (e.g., [16][17][18][19][20][21][22]).…”

“…Also this field is indeed a field of intense research activity [11,31,54,63,[90][91][92], and it is particularly challenging when extending to the large variety of atypical tsunami sources (e.g. [11,17,32,54]).…”

“…This is often the case also for PTHA, because the subduction earthquakes may be thought to dominate the hazard either at the global (e.g., [93]) or at the local scale at specific places (e.g., [94]). Tsunamis generated by atypical sources are usually more local and probably less frequent than subduction zone events worldwide, and their effective hazard is more difficult to constrain, and thus it is often neglected [11,17]. Also, their forecasting in near-real-time for early warning purposes is rather challenging and not yet a well-established practice, particularly as far as non-seismic sources are concerned.…”