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Many coastal communities around the world are threatened by a near-field (or local) tsunami that could inundate the low-lying areas in a matter of minutes after generation. The universal consensus amongst emergency agencies and academic researchers is that a safe evacuation requires an effective response, which is typically assessed by the evacuation time estimate (ETE). ETE is an integral component of community emergency evacuation planning, especially areas prone to tsunamis. This paper aims to investigate the ETE for pedestrian evacuation during a tsunami through two different approaches: (1) the deterministic Least-Cost Distance (LCD) model; and (2) the dynamic Agent-Based Model (ABM). Then, the comparison of the two models in their intrinsic characteristics, strengths and weaknesses, and its applicability was discussed based on methodology behind of the LCD model and ABM. The LCD model was conducted to generate a spatially distributed ETE map, visualizing vulnerable areas where the evacuation time would be insufficient for individuals to reach safety. The ABM investigated uncertainty during tsunami evacuations, such as population distribution, walking speed, and milling time. This paper provides insights into the differences between the LCD model and ABM in terms of methodology and application. It assists the academic researchers and emergency managers, evacuation planners, and decision makers to choose an appropriate method for modeling pedestrian evacuation during tsunami. 

Despite the increase in frequency and intensity of wildfires around the world, little research has examined households’ expectations of evacuation logistics and evacuation time estimate (ETE) components during such rapid-onset disasters. To address this gap, this study analyzes data from 152 household responses affected by the devastating 2018 wildfire in Mati, Greece where the second-deadliest wildfire of the 21st century took place. The questionnaire measured residents’ expectations of how they would respond to a future wildfire. This includes the number of vehicles they would take, their evacuation destination and route choices, and their expected evacuation preparation and travel times. Explanatory variables include risk perceptions, wildfire preparedness, wildfire experience, and demographic characteristics. The univariate results reveal some similarities to, but also some differences from, expected evacuation logistics and ETE components in other natural hazards. Moreover, correlation and regression analyses show that expected evacuation logistics and ETE components are primarily related to wildfire preparedness actions. Comparison of this study’s results with other rapid onset events such as tsunamis and hazardous material incidents, as well as longer onset events such as hurricanes, sheds light on household responses to wildfires. Emergency managers can use the similarities in results across studies to better prepare for wildfire evacuations. 

Abstract Building community resilience has become a national imperative. Substantial uncertainties in dynamic environments of emergencies and crises require real‐time information collection and dissemination based on big data analytics. These, in turn, require networked communities and cross‐sector partnerships to build lasting resilience. This viewpoint article highlights an interdisciplinary approach to building community resilience through community‐engaged research and partnerships. This perspective leverages existing community partnerships and network resources, undertakes an all‐hazard and whole‐community approach, and evaluates the use of state‐of‐the‐art information communication technologies. In doing so, it reinforces the multifaceted intergovernmental and cross‐sector networks through which resilience can be developed and sustained. 

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. 

Debris removal is a critical activity in the aftermath of natural disasters such as earthquakes and tsunamis to enable community and lifeline network recovery. This activity is hampered by logistical bottlenecks including the non-availability of equipment and inadequate capacity of temporary debris management sites (TDMS). This paper enables analysis of debris removal and lifeline repair operations quantifying recovery times for informed decision-making about equipment allocation and TDMS selection before the disaster. The developed framework was applied to the case study of a Cascadia Subduction Zone event for the coastal town of Astoria in Oregon. The proposed framework enables decision-makers with an objective means of evaluating decision alternatives both before and after disasters to analyze and improve their community’s capability of handling disaster debris. Furthermore, this framework will serve as a platform upon which interdependencies between transportation network and debris removal operations will be analyzed in the future. 

This paper presents an integrative analysis framework combining natural hazards with network mobility to provide insights on disaster preparedness and relief. In particular, this framework characterizes the impact of seismically induced landslides on network mobility to reveal the mobility changes immediately after the events and throughout the course of restoration and recovery efforts. Landslides not only undermine the structural integrity of roadways, but also deposit a significant amount of material on the road surface, usually resulting in partial or complete road closure to traffic. The highly populated Portland, Oregon, Metro is selected as a case study to demonstrate this framework given that the Pacific Northwest is highly prone to large earthquakes as part of the Cascadia Subduction Zone as well as highly susceptible to landslides given its high topographic relief and wet climate. In this case study, travel time to the west and east sides of Willamette River, which divides the Portland Metro area, shows an abrupt change in mobility. In particular, the Portland Hills region with its steep topography is identified as the most vulnerable region. Based on a temporal analysis of recovery, the majority of the network mobility is expected to be restored after 30 days. The results of this study serve as a preliminary assessment of the impact of landslides on network mobility and can facilitate decision making in emergency planning.