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This study couples FN‐curves with Agent‐based Modeling and Simulation (ABMS) to assess risk for tsunamis with various recurrence intervals . By considering both expected number of casualties and the likelihood of tsunami events, multiple series of simulations and in‐depth analyses determine (1) how vertical evacuation structure (VES) placement impacts mortality rate; (2) what the best evacuation strategies VES locations are; and (3) where evacuees are likely to be caught by tsunami waves. The results from utilizing FN‐curves to conduct disaggregative analyses based on six tsunami scenarios indicate that choosing one tsunami scenario or averaging the risk of different scenarios may not fully articulate VES impacts due to the “levee effect,” which potentially leads to false positives. Findings show that placing VESs close to shorelines saves nearby at‐risk populations, but also results in two risk increasing phenomena: “exposure to risk” (i.e., evacuees being attracted to high risk roads by a VES when evacuating) and “blind zones” (i.e., locations near a VES where evacuees increase their risk by evacuating to that VES). When limited to one VES, placement near a population's centroid results in the lowest mortality rate. More than one VES may lower mortality rate further if VESs are spreading out according to community's topography. In addition to the analysis of tsunamis, the approach of coupling FN‐curves with ABMS can be used by local authorities and engineers to determine tailored hard‐adaptive measures and evacuation strategies, which helps to avoid maladaptive actions in different hazardous events.

 

Abstract. Previous tsunami evacuation simulations have mostly been based on arbitrary assumptions or inputs adapted from non-emergency situations, but a few studies have used empirical behavior data. This study bridges this gap by integrating empirical decision data from surveys on local evacuation expectations and evacuation drills into an agent-based model of evacuation behavior for two Cascadia subduction zone (CSZ) communities that would be inundated within 20–40 min after a CSZ earthquake. The model also considers the impacts of liquefaction and landslides from the earthquake on tsunami evacuation. Furthermore, we integrate the slope-speed component from least-cost distance to build the simulation model that better represents the complex nature of evacuations. The simulation results indicate that milling time and the evacuation participation rate have significant nonlinear impacts on tsunami mortality estimates. When people walk faster than 1 m s−1, evacuation by foot is more effective because it avoids traffic congestion when driving. We also find that evacuation results are more sensitive to walking speed, milling time, evacuation participation, and choosing the closest safe location than to other behavioral variables. Minimum tsunami mortality results from maximizing the evacuation participation rate, minimizing milling time, and choosing the closest safe destination outside of the inundation zone. This study's comparison of the agent-based model and the beat-the-wave (BtW) model finds consistency between the two models' results. By integrating the natural system, built environment, and social system, this interdisciplinary model incorporates substantial aspects of the real world into the multi-hazard agent-based platform. This model provides a unique opportunity for local authorities to prioritize their resources for hazard education, community disaster preparedness, and resilience plans. 

This study analyzes 488 household residents’ responses to the 2018 Indonesia M7.5 earthquake and tsunami. Comparison of this event with past earthquake and tsunami events, such as the 2009 Samoa (M8.1), 2011 Christchurch (M6.3), and 2011 Tohoku (M9.0) events, identifies commonalities and differences among people’s responses to these events. The results show that many Palu respondents failed to recognize strong earthquake ground motion as an environmental cue to a tsunami, but this was partially offset by an informal peer warning network. Most of the warnings only mentioned one of the six recommended message elements—the tsunami hazard. However, this brief message might have been adequate for many people if they could infer the certainty, severity, and immediacy of the threat, and appropriate evacuation modes, routes, and destinations. Unlike two comparison cases, some Palu respondents actually began their evacuation later than they expected the tsunami to strike. This might be due to spending too much time milling (seeking additional information, relaying warnings, reuniting families, and preparing to evacuate)—given the tsunami’s extremely rapid onset. This finding underscores the need for coastal emergency managers to promote evacuation preparedness for near-field tsunamis in which households pack Grab and Go kits in advance, warn others while evacuating, and plan in advance where to reunite household members who must evacuate separately.

 

This paper presents the use of tsunami evacuation drills within a coastal community in the Cascadia Subduction Zone (CSZ) to better understand evacuation behaviors and thus to improve tsunami evacuation preparedness and resilience. Evacuees’ spatial trajectory data were collected by Global Navigation Satellite System (GNSS) embedded in mobile devices. Based on the empirical trajectory data, probability functions were employed to model people’s walking speed during the evacuation drills. An Evacuation Hiking Function (EHF) was established to depict the speed–slope relationship and to inform evacuation modeling and planning. The regression analysis showed that evacuees’ speed was significantly negatively associated with slope, time spent during evacuation, rough terrain surface, walking at night, and distance to destination. We also demonstrated the impacts of milling time on mortality rate based on participants’ empirical evacuation behaviors and a state-of-the-art CSZ tsunami inundation model. Post-drill surveys revealed the importance of the drill as an educational and assessment tool. The results of this study can be used for public education, evacuation plan assessment, and evacuation simulation models. The drill procedures, designs, and the use of technology in data collection provide evidence-driven solutions to tsunami preparedness and inspire the use of drills in other types of natural disasters such as wildfires, hurricanes, volcanoes, and flooding.