Microzoning tsunami hazard by combining flow depths and arrival times

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.The authors acknowledge SHOA’s support to this work, particularly making available nautical charts and coastal zone plans used to generate high resolution terrain models. PC, NZ and AG were partially funded by ANID through its grants ANID/FONDAP/15110017 (CIGIDEN), and ANID/FONDEF/ID19I10048. PAC also thanks ANID PIA/APOYO AFB180002. MC acknowledges the support from ‘Millennium Nucleus CYCLO: The Seismic Cycle Along Subduction Zones’ and Chile’s Fondo Nacional de Desarrollo Científico y Tecnológico, FONDECYT Projects N°1190258 and N° 1181479. The authors also acknowledge C. Sippl for discussion about asperities offshore Valparaíso and corrections to the manuscript. The authors thankfully acknowledges the computer resources at CTE-POWER and the technical support provided by BSC (RES-AECT-2020-2-0001), and the EDANYA Group at Málaga university for providing the HySEA code. We acknowledge D. Melgar for making available the MudPy code at: https://github.com/dmelgarm/MudPy. J. León is acknowledge for sharing the evacuation results. We are very grateful to the editor S. Lorito and the comments of K. Goda, A. Armigliato and W. Power, for a thoughtful and insightful discussion that helped us to greatly improve this work. NZ has received funding from the STARS programme part of the Marie Skłodowska-Curie grant agreement H2020-MSCA-COFUND-2016-754433.Peer ReviewedPostprint (published version