Microzoning Tsunami Hazard by Combining Flow Depths and Arrival Times

Abstract

Tsunami hazard is typically assessed from inundation flow depths estimated from one or many earthquake scenarios. However, information about the exact time when such inundation occurs is seldom considered, yet it is crucial for pedestrian evacuation planning. Here, we propose an approach to estimating tsunami hazard by combining tsunami flow depths and arrival times to produce a nine-level, qualitative hazard scale that is translated into a simple tsunami hazard map. To do this, one of the most populated regions of the coast of Chile is considered as the sample site, using a large set of 2,800 tsunamigenic sources from earthquakes with magnitudes in the range Mw8.6−9.2, modeled from generation to inundation at high resolution. Main outcomes show great dependency of the hazard categorization on the tsunami time arrival, and less to the flow depths. Also, these results demonstrate that incorporating different sources of variability such as different earthquake magnitudes and locations as well as stochastic slip distributions is essential. Moreover, this proof-of-concept exercise clearly shows that the qualitative hybrid categorization of the tsunami hazard allows for its more effective understanding, which can be beneficial for designing mitigation strategies such as evacuation planning, and its management.

Discriminating the occurrence of inundation in tsunami early warning with one-dimensional convolutional neural networks

“…Among these, 1730 was the only one to have inundation in Valparaíso, whereas 1922 and 2015 did cause inundation in Coquimbo. However, Zamora et al 68 found that events 9.0 are also capable of inundating the Valparaíso region, depending on the characteristics of the slip distribution. Hence, to account for source variability, these synthetic earthquakes were generated within this region using the Karhoenen-Loeve Expansion following Leveque et al 69 and Melgar et al 70 , considering a domain discretization of 10 x 10 km.…”

“…Vertical colored regions distinguish between the tsunami amplitude values used to categorize the hazard 21 . Horizontal coloring distinguish between very fast, fast and late arrivals, following 68 . Percentage values on the left indicate the cumulative fraction of events as a function of time.…”

From offshore to onshore probabilistic tsunami hazard assessment via efficient Monte-Carlo sampling

“…To improve on the efficiency of synthetic catalogues, PTHAs often employ stratified-sampling by earthquake magnitude (e.g. De Risi & Goda, 2017;Williamson et al, 2020;Basili et al, 2021;Zamora et al, 2021). The Monte-Carlo accuracy of this approach is analysed below, and techniques are presented to estimate the errors both before and after high-resolution tsunami simulation.…”

“…Both stratified and stratified/importance-sampling require fixing N (M w,b ) prior to sampling, and in practice uniform N (M w,b ) are often used (e.g. De Risi & Goda, 2017; Williamson et al, 2020;Zamora et al, 2021). However the computational effort is generally related to the total number of scenarios N tot :…”

“…Our approach builds upon stratified-sampling, in which scenarios are binned by magnitude and a fixed number are randomly sampled from each bin (e.g. De Risi & Goda, 2017; Williamson et al, 2020;Basili et al, 2021;Zamora et al, 2021). This standard approach is improved by leveraging the offshore PTHA to automatically focus sampling on scenarios with larger waves near the site of interest, and on magnitude-bins that contribute most to Monte-Carlo errors.…”