The variability in obtaining estimates of tsunami inundation and runup on a near‐real‐time tsunami hazard assessment setting is evaluated. To this end, 19 different source models of the Maule Earthquake were considered as if they represented the best available knowledge an early tsunami warning system could consider. Results show that large variability can be observed in both coseismic deformation and tsunami variables such as inundated area and maximum runup. This suggests that using single source model solutions might not be appropriate unless categorical thresholds are used. Nevertheless, the tsunami forecast obtained from aggregating all source models is in good agreement with observed quantities, suggesting that the development of seismic source inversion techniques in a Bayesian framework or generating stochastic finite fault models from a reference inversion solution could be a viable way of dealing with epistemic uncertainties in the framework of nearly‐real‐time tsunami hazard mapping.

We propose a method for defining the optimal locations of a network of tsunameters in view of near real-time tsunami forecasting using sea surface data assimilation in the near and middle fields, just outside of the source region. The method requires first the application of the Empirical Orthogonal Functions (EOF) analysis to identify the potential initial locations, followed by an optimization heuristics that minimizes a cost-benefit function to narrow down the number of stations. We apply the method to a synthetic case of the 2015 M_w8.4 Illapel Chile earthquake, and show that it is possible to obtain an accurate tsunami forecast for wave heights at near coastal points, not too close to the source, from assimilating data from 3 tsunameters during 14 minutes, but with a minimum average time lag of nearly 5 minutes between simulated and forecasted waveforms. Additional tests, show that the time lag is reduced for tsunami sources that are located just outside of the area covered by the tsunameter network. The latter suggests that sea surface data assimilation from a sparse network of stations could be a strong complement for the fastest tsunami early warning systems based on pre-modeled seismic scenarios.

On 8 July 1730, a great earthquake struck metropolitan Chile, causing extensive damage 1000 km along the country and focused in Valparaíso. Due to the date of occurrence of this event, large uncertainties about the earthquake’s magnitude have been discussed among the scientific community, and the earthquake and tsunami have remained unknown for most of the population. The purpose of this paper is to describe joint efforts undertaken by organizations, academia, and authorities to rescue the forgotten memory of an event that occurred almost three centuries ago and that may be repeated in the near future. In line with the Sendai Framework, we focus on one of the four priorities for action, which is to understand disaster risk, with the premise that the memory activation and raising awareness can save lives in the future. We designed outreach strategies to communicate this knowledge to the community in a participatory way. The latter involves scientific talks, earthquake simulators, tsunami projection mapping on relief scaled models (mock-up), and a public debate including the participation of academia, politicians, authorities, and the local community. The emulation of such activities and the constant work of regional and national authorities, academia, and non-governmental organizations dealing with risk mitigation encourage involving the community to build safer cities against the tsunami hazard.

The occurrence of mega-tsunamis over the last couple of decades has greatly increased the efforts of the research community and practitioners to work hand in hand to reduce risks from these highly destructive threats. Protecting the population through evacuation is the best alternative for avoiding loss of life in the wake of the occurrence of a tsunamigenic earthquake. Therefore, guaranteeing the proper state of evacuation routes is very important to ensuring appropriate movement to the safe zones. This study carries out a detailed analysis of possible evacuation scenarios, considering the actual state of the escape routes of Iquique, a Chilean city prone to tsunamis, with a substantial number of urban micro-scale vulnerabilities, i.e., elements that obstruct or complicate pedestrian flow. The quantification of the delay in evacuation processes resulting from the presence of urban micro-vulnerabilities is carried out through micro-scale agent-based model (ABM) simulations. In addition, these results are integrated with high-resolution tsunami inundation simulations, allowing for an estimation of the potential number of people that the tsunami may reach under different scenarios by emulating the dynamics and behavior of the population and the decision-making regarding the starting time of the evacuation.