A new generation of tsunami inundation maps of Chilean cities: tsunami source database and probabilistic hazard analysis

ABSTRACT

Tsunami inundation maps are crucial for understanding the impact of tsunamis and planning mitigation measures. Our research focuses on creating a database of stochastic tsunami scenarios along the Chilean subduction zone and probabilistic inundation maps for 11 coastal cities. We divided the Chile-Perú subduction zone into four seismic segments based on historical seismicity. Stochastic rupture scenarios, ranging from 8.0 to 9.6 magnitudes, were generated using the Karhunen-Loeve expansion. The Stochastic Reduced Order Model (SROM) helped select representative tsunami scenarios for each segment and magnitude bin. We then used the NEOWAVE model to simulate these scenarios to an inundation level, creating probabilistic tsunami maps for various return periods. Our findings reveal that local geography significantly influences tsunami inundation, with some areas facing high inundation risks while others experience minimal impacts. As a result, a uniform planning and design criterion across the entire country is not advisable; site-specific studies are necessary. These probabilistic scenarios can provide tailored solutions for different Chilean coastal cities, enhancing their resilience. Additionally, this research marks the first comprehensive probabilistic tsunami hazard analysis for the Chilean coast, considering multiple seismic sources, marking a crucial step toward full tsunami risk assessment for coastal communities.

KEYWORDS:

• Tsunami
• Chile
• probabilistic hazard assessment
• numerical simulation
• inundation maps

Acknowledgments

This research was partially funded by ANID/FONDECYT grants 1210496 and 1210540. It was also partially supported by the supercomputing infrastructure of the NLHPC (ECM-02). R.A. also thanks the Research Center for Integrated Disaster Risk Management: ANID/1523A0009 FONDAP 2023 and the UCSC Office of Research for funding by means of FAA2023. I.S. would like to acknowledge the support from the SEED funding of San Diego State University. The authors extend their gratitude to Oneska Peña y Lillo and Claudio Astroza for making the tsunami maps in kmz format and Dr. Juan Gonzalez for helping with the K-L implementation. Finally, special thanks to Forestal Arauco S.A. by means of Fernando Bustamante, for providing lidar data of Constitución, Coronel and Corral.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

Tsunami source database and inundation maps are available at https://data.mendeley.com/datasets/b85kvbm8ht/2

Additional information

Funding

The work was supported by the Agencia Nacional de Investigación y Desarrollo [1210496]; Agencia Nacional de Investigación y Desarrollo [1210540]; San Diego State University [SEED funding].