Effects of rupture complexity on local tsunami inundation: Implications for probabilistic tsunami hazard assessment by example

Abstract

We investigated the influence of earthquake source complexity on the extent of inundation caused by the resulting tsunami. We simulated 100 scenarios with collocated sources of variable slip on the Hikurangi subduction interface in the vicinity of Hawke's Bay and Poverty Bay in New Zealand and investigated the tsunami effects on the cities of Napier and Gisborne. Rupture complexity was found to have a first-order effect on flow depth and inundation extent for local tsunami sources. The position of individual asperities in the slip distribution on the rupture interface control to some extent how severe inundation will be. However, predicting inundation extent in detail from investigating the distribution of slip on the rupture interface proves difficult. Assuming uniform slip on the rupture interface in tsunami models can underestimate the potential impact and extent of inundation. For example, simulation of an M w 8.7 to M w 8.8 earthquake with uniform slip reproduced the area that could potentially be inundated by equivalent nonuniform slip events of M w 8.4. Deaggregation, to establish the contribution of different sources with different slip distributions to the probabilistic hazard, cannot be performed based on magnitude considerations alone. We propose two predictors for inundation severity based on the offshore tsunami wavefield using the linear wave equations in an attempt to keep costly simulations of full inundation to a minimum.

Probabilistic Seismic and Tsunami Hazard Analysis Conditioned on a Megathrust Rupture of the Cascadia Subduction Zone

“…Subsequent to the 2004 event and with the more recent SZ tsunami events in Chile in 2010 and in Japan in 2011, there has been an increasing interest in developing PTHA. Studies have focused on regions throughout the Pacific Rim, including Japan (Burroughs and Tebbens, 2005;Annaka et al, 2007;Yanagisawa et al, 2007;Fukutani et al, 2015;and Goda and Song, 2016), the US Pacific Coast and Canada (Geist and Parsons, 2006;González et al, 2009;Thio and Somerville, 2009;Priest et al, 2010;Witter et al, 2013;Leonard et al, 2014;and Park and Cox, 2016), South China Sea (Liu et al, 2007;Li et al, 2016), New Zealand, and Australia (Power et al, 2007(Power et al, , 2013Burbidge et al, 2008;and Mueller et al, 2015), as well as places in Europe (Tinti et al, 2005;Grezio et al, 2010;Anita et al, 2012) and the Northwestern Indian Ocean (Thio et al, 2007;Heidarzadeh and Kijko, 2011). As explained in the study by see text footnote 1, the PTHA generally uses one of three approaches for the tsunami generation: (1) historical record approach, (2) logic-tree approach, and (3) random phase approach.…”

Tsunami evacuation plans for future megathrust earthquakes in Padang, Indonesia, considering stochastic earthquake scenarios

“…Significant risk mitigation efforts have been made in recent years in high-risk countries, such as Japan, USA, Chile, New Zealand, and Indonesia (Schlurmann et al, 2010;Wood et al, 2014;Mueller et al, 2015;Raby et al, 2015). Even though Japan is a welldeveloped country with comprehensive tsunami defense systems, the 2011 Tōhoku tsunami still caused significant damage, causing economic loss of ∼ USD 365 billion and fatalities of ∼ 20 000 people (Kazama and Noda, 2012).…”

“…For instance, the deterministic approach is more communicable to the authorities for developing post-disaster recovery and mitigation plans (McGuire, 2001). However, implementation of deterministic scenarios may oversimplify the tsunami hazards and risks, leading to imprecise mitigation plans (Griffin et al, 2015;Mueller et al, 2015). In contrast, the probabilistic approach requires the proper consideration of regional earthquake characteristics, including uncertainties in the earthquake rupture size, its focal mechanism, and the depth and spatial heterogeneity of the earthquake slip.…”

Influence of Flow Velocity on Tsunami Loss Estimation

“…For instance, spatial slip distributions of a M w 9.0 earthquake can vary significantly in terms of location and dimension of the earthquake rupture and details of earthquake slips along the rupture plane. It has been demonstrated that the uncertainty in earthquake source characterisation results in significant variability of tsunami loss in coastal regions [28][29][30][31]. In probabilistic tsunami risk analysis, several studies adopted a stochastic approach where the seismic source is characterised by magnitude and average slip [32], while probabilistic scaling relationships can be used to characterise earthquake source through multiple source parameters in a comprehensive way [33].…”