On the landslide tsunami uncertainty and hazard
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Finn Løvholt I Sylfest Glimsdal I Carl B. Harbitz
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. Keywords Tsunamis . Uncertainty . Landslide dynamics . Hazard analysis . PTHA Introduction Landslides are the second most frequent tsunami source worldwide (Harbitz et al. 2014a; Tappin 2010; Yavari-Ramshe and Ataie-Ashtiani 2016). Their importance is undisputed, most recently demonstrated by the 2018 Anak Krakatoa volcano flank collapse (Grilli et al. 2019) causing several hundred fatalities. Additional examples of fatal landslide tsunamis comprise among others the 1959 Vajont event (Crosta et al. 2016), the 1998 Papua Guinea tsunami (Tappin et al. 2008) and the 1792 Mount Unzen tsunami (Sassa et al. 2016). Moreover, several subaerial landslide-generated waves have induced up to 150-m run-up heights during the last two decades (George et al. 2017; Gylfadóttir et al. 2017; Paris et al. 2019; Sepúlveda and Serey 2009; Tinti et al. 2005). Further examples can be found in the review of (Harbitz et al. 2014a). Landslide tsunamis have the potential to produce larger, but often more local (Okal and Synolakis 2004) tsunamis than earthquakes, and they are also more complex and display a more diverse nature (Løvholt et al. 2015). Therefore, prognostic analysis of landslide tsunamis is far less developed than for earthquakes, and hence, we have a more limited understanding of landslide tsunami hazards (Harbitz et al. 2014a; Geist and Parsons 2014). A first reason for this is due to lack of knowledge related to the landslide occurrence frequency, with just a few limited records of comprehensive landslide statistics covering landslide volumes across several orders of magnitude (Blikra et al. 2005; Geist and ten Brink 2019; Lane et al. 2016; Urgeles and Camerlenghi 2013). Hence, the stat
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