Review of California Wildfire Evacuations from 2017 to 2019

Between 2017 and 2019, California experienced a series of devastating wildfires that together led over one million people to be ordered to evacuate. Due to the speed of many of these wildfires, residents across California found themselves in challenging evacuation situations, often at night and with little time to escape. These evacuations placed considerable stress on public resources and infrastructure for both transportation and sheltering. In the face of these clear challenges, transportation and emergency management agencies across California have widely varying levels of preparedness for major disasters, and nearly all agencies do not have the public resources to adequately and swiftly evacuate all populations in danger. To holistically address these challenges and bolster current disaster and evacuation planning, preparedness, and response in California, we summarize the evacuations of eleven major wildfires in California between 2017 and 2019 and offer a cross-comparison to highlight key similarities and differences. We present results of new empirical data we collected via an online survey of individuals impacted by: 1) the 2017 October Northern California Wildfires (n=79), 2) the 2017 December Southern California Wildfires (n=226), and 3) the 2018 Carr Wildfire (n=284). These data reveal the decision-making of individuals in these wildfires including choices related to evacuating or staying, departure timing, route, sheltering, destination, transportation mode, and reentry timing. We also present results related to communication and messaging, non-evacuee behavior, and opinion of government response. Using the summarized case studies and empirical evidence, we present a series of recommendations for agencies to prepare for, respond to, and recover from wildfires

Doctor of Philosophy

dissertationWildfire is a common hazard in the western U.S. that can cause significant loss of life and property. When a fire approaches a community and becomes a threat to the residents, emergency managers need to take into account both fire behavior and the expected response of the threatened population to warnings before they issue protective action recommendations to the residents at risk. In wildfire evacuation practices, incident commanders use prominent geographic features (e.g., rivers, roads, and ridgelines) as trigger points, such that when a fire crosses a feature, the selected protective action recommendation will be issued to the residents at risk. This dissertation examines the dynamics of evacuation timing by coupling wildfire spread modeling, trigger modeling, reverse geocoding, and traffic simulation to model wildfire evacuation as a coupled human-environmental system. This dissertation is composed of three manuscripts. In the first manuscript, wildfire simulation and household-level trigger modeling are coupled to stage evacuation warnings. This work presents a bottom-up approach to constructing evacuation warning zones and is characterized by fine-grain, data-driven spatial modeling. The results in this work will help improve our understanding and representation of the spatiotemporal dynamics in wildfire evacuation timing and warnings. The second manuscript integrates trigger modeling and reverse geocoding to extract and select prominent geographic features along the boundary of a trigger buffer. A case study using a global gazetteer GeoNames demonstrates the potential value of the proposed method in facilitating communications in real-world evacuation practice. This work also sheds light on using reverse geocoding in other environmental modeling applications. The third manuscript explores the spatiotemporal dynamics behind evacuation timing by coupling fire and traffic simulation models. The proposed method sets wildfire evacuation triggers based on the estimated evacuation times using agent-based traffic simulation and could be potentially used in evacuation planning. In summary, this dissertation enriches existing trigger modeling approaches by coupling fire simulation, reverse geocoding, and traffic simulation. A framework for modeling wildfire evacuation as a coupled human-environmental system using triggers is proposed. Moreover, this dissertation also attempts to advocate and promote open science in wildfire evacuation modeling by using open data and software tools in different phases of modeling and simulation

Doctor of Philosophy

dissertationWildfire is a common hazard in the western U.S. that can cause significant loss of life and property. When a fire approaches a community and becomes a threat to the residents, emergency managers need to take into account both fire behavior and the expected response of the threatened population to warnings before they issue protective action recommendations to the residents at risk. In wildfire evacuation practices, incident commanders use prominent geographic features (e.g., rivers, roads, and ridgelines) as trigger points, such that when a fire crosses a feature, the selected protective action recommendation will be issued to the residents at risk. This dissertation examines the dynamics of evacuation timing by coupling wildfire spread modeling, trigger modeling, reverse geocoding, and traffic simulation to model wildfire evacuation as a coupled human-environmental system. This dissertation is composed of three manuscripts. In the first manuscript, wildfire simulation and household-level trigger modeling are coupled to stage evacuation warnings. This work presents a bottom-up approach to constructing evacuation warning zones and is characterized by fine-grain, data-driven spatial modeling. The results in this work will help improve our understanding and representation of the spatiotemporal dynamics in wildfire evacuation timing and warnings. The second manuscript integrates trigger modeling and reverse geocoding to extract and select prominent geographic features along the boundary of a trigger buffer. A case study using a global gazetteer GeoNames demonstrates the potential value of the proposed method in facilitating communications in real-world evacuation practice. This work also sheds light on using reverse geocoding in other environmental modeling applications. The third manuscript explores the spatiotemporal dynamics behind evacuation timing by coupling fire and traffic simulation models. The proposed method sets wildfire evacuation triggers based on the estimated evacuation times using agent-based traffic simulation and could be potentially used in evacuation planning. In summary, this dissertation enriches existing trigger modeling approaches by coupling fire simulation, reverse geocoding, and traffic simulation. A framework for modeling wildfire evacuation as a coupled human-environmental system using triggers is proposed. Moreover, this dissertation also attempts to advocate and promote open science in wildfire evacuation modeling by using open data and software tools in different phases of modeling and simulation

Modelling the impact of wildfire smoke on driving speed

Traffic models can be used to study evacuation scenarios during wildland-urban interface fires and identify the ability of a community to reach a safe place. In those scenarios, wildfire smoke can reduce visibility conditions on the road. This can have serious implications on the evacuation effectiveness since drivers would reduce their speed in relation to the optical density on the road. To date, there is no traffic model which explicitly represents the impact of reduced visibility conditions on traffic evacuation flow. This paper makes use of an experimental dataset collected in a virtual reality environment to calibrate two widely used macroscopic traffic models (the Lighthill-Whitham-Richards and the Van Aerde models) in order to account for the impact of reduced visibility conditions on driving speed. An application of the calibrated traffic model considering the impact of smoke has been performed using the WUI-NITY platform, an open multi-physics platform which includes wildfire spread, pedestrian response and traffic modelling. A dedicated verification test has been developed and performed considering different values of optical densities of smoke and traffic densities to ensure the model has been implemented correctly in WUI-NITY. A case study that demonstrates the applicability of the model to real life scenarios was also implemented, based on data from an evacuation drill. This paper shows that the presence of smoke on the road can significantly decrease movement speed and increase evacuation times thus highlighting the need for inclusion of this factor in traffic evacuation models applied for wildland-urban interface fire scenarios

Evacuation modelling for wildland-urban interface fires in touristic areas

This technical note presents a brief overview of the models available for the simulation of fire evacuation at the wildland-urban interface in touristic areas. Depending on the scale of the scenarios under consideration and the evacuation mode considered, models are split into macroscopic vs microscopic tools and 1) pedestrian models, 2) traffic models, 3) coupled evacuation models, 4) modelling unconventional evacuation modes. The key findings of this review are: 1) When pedestrian movement is the main mode of evacuation transport, the scale of the analysis will have a strong impact on the choice of the most appropriate modelling approach although at building scale and not very large area size, the use of microscopic modelling based on a continuous approach seems to be a suitable method. 2) When multiple modes of transport are considered (e.g., pedestrian and traffic), the modeller should make a call into modelling explicitly or implicitly the pedestrian response and movement layer, 3) most evacuation models are currently not able to model explicitly unconventional means of evacuations such as displacement via sea or air. The scenario complexity and the uncertainty in the available input will affect the choice of modellers to represent evacuation modelling layers (e.g., pedestrian response, pedestrian movement, and traffic movement) and their interaction with the wildfire explicitly or implicitly

An open multi-physics framework for modelling wildland-urban interface fire evacuations

Fire evacuations at wildland-urban interfaces (WUI) pose a serious challenge to the emergency services, and are a global issue affecting thousands of communities around the world. This paper presents a multi-physics framework for the simulation of evacuation in WUI wildfire incidents, including three main modelling layers: wildfire, pedestrians, and traffic. Currently, these layers have been mostly modelled in isolation and there is no comprehensive model which accounts for their integration. The key features needed for system integration are identified, namely: consistent level of refinement of each layer (i.e. spatial and temporal scales) and their application (e.g. evacuation planning or emergency response), and complete data exchange. Timelines of WUI fire events are analysed using an approach similar to building fire engineering (available vs. required safe egress times for WUI fires, i.e. WASET/WRSET). The proposed framework allows for a paradigm shift from current wildfire risk assessment and mapping tools towards dynamic fire vulnerability mapping. This is the assessment of spatial and temporal vulnerabilities based on the wildfire threat evolution along with variables related to the infrastructure, population and network characteristics. This framework allows for the integration of the three main modelling layers affecting WUI fire evacuation and aims at improving the safety of WUI communities by minimising the consequences of wildfire evacuations

Progress Report 1: Resilience and Adaptation to Climatic Extreme Wildfires (RACE Wildfires)

This is the first progress report of the international project funded by the National Research Council of Canada called Resilience and Adaptation to Climatic Extreme Wildfires (RACE Wildfires). In this first phase, the research performed included two main tasks: 1) the development of a sub-model for the representation of the impact of reduced visibility conditions on driving speed and 2) the development of a conceptual model for the study of the impact of the pandemic on shelter availability and destination choice. An experimental dataset collected in a virtual reality environment has been used to develop a sub-model for macroscopic traffic models considering the impact of reduced visibility conditions on driving speed. An application of a calibrated traffic model considering the impact of smoke has been performed using the WUI-NITY platform, an open multi-physics platform which includes wildfire spread, pedestrian response and traffic modelling. Verification testing has been performed as well. A conceptual framework for the development of a destination choice model to be applied in wildfire scenarios has also been developed

A review of traffic models for wildland-urban interface wildfire evacuation

Recent years have seen an increased prevalence of wildfires, some of which has spread into the wildland-urban interface and lead to large-scale evacuations. Large-scale evacuations gives rise to both logistical and traffic related issues. To aid in the planning and execution of such evacuations reliable modelling tools to simulate evacuation traffic are needed. Today no traffic model exists which is dedicated only to simulate wildfire evacuation in the wildland/urban interface. The aim of this thesis is to identify benchmark characteristics needed in such a model and review 12 existing models, both traffic models and evacuation models, and their potential usefulness in WUI wildfire scenarios. The thesis concludes that some models can be tuned to represent aspects of a WUI fire evacuation and that future research should focus on integrating traffic modelling with modelling of fire/smoke spread and pedestrian movement

e-Sanctuary: open multi-physics framework for modelling wildfire urban evacuation

The number of evacuees worldwide during wildfire keep rising, year after year. Fire evacuations at the wildland-urban interfaces (WUI) pose a serious challenge to fire and emergency services and are a global issue affecting thousands of communities around the world. But to date, there is a lack of comprehensive tools able to inform, train or aid the evacuation response and the decision making in case of wildfire. The present work describes a novel framework for modelling wildfire urban evacuations. The framework is based on multi-physics simulations that can quantify the evacuation performance. The work argues that an integrated approached requires considering and integrating all three important components of WUI evacuation, namely: fire spread, pedestrian movement, and traffic movement. The report includes a systematic review of each model component, and the key features needed for the integration into a comprehensive toolkit