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

Evaluating Human Driving Behavior and Traffic Operation Conditions during Wildfire Evacuation Using Connected Vehicles Data

With climate change and the resulting rise in temperatures, wildfire risk is increasing all over the world, particularly in the western United States, and the communities in wildland-urban interface (WUI) areas are at the greatest risk of fire. Understanding the driving behavior of individuals during evacuating fire-affected WUI areas is important because the evacuees may encounter difficult driving conditions and traffic congestions due to proximity to flammable vegetation and limited exit routes. Existing studies lack empirical data on evacuee driving behavior and traffic operation conditions during a wildfire evacuation. This study used two distinct connected vehicles (CV) datasets that contain lane-level precision historical vehicle trajectory and driving events datasets to investigate the traffic delays and driving behavior of individuals during historical wildfire evacuation events. The results of the study showed that the CV-datasets are a valuable source to accurately evaluate human driving behavior and calculate traffic delays in wildfire-caused evacuations

A review of wildland fire spread modelling, 1990-present 3: Mathematical analogues and simulation models

In recent years, advances in computational power and spatial data analysis (GIS, remote sensing, etc) have led to an increase in attempts to model the spread and behvaiour of wildland fires across the landscape. This series of review papers endeavours to critically and comprehensively review all types of surface fire spread models developed since 1990. This paper reviews models of a simulation or mathematical analogue nature. Most simulation models are implementations of existing empirical or quasi-empirical models and their primary function is to convert these generally one dimensional models to two dimensions and then propagate a fire perimeter across a modelled landscape. Mathematical analogue models are those that are based on some mathematical conceit (rather than a physical representation of fire spread) that coincidentally simulates the spread of fire. Other papers in the series review models of an physical or quasi-physical nature and empirical or quasi-empirical nature. Many models are extensions or refinements of models developed before 1990. Where this is the case, these models are also discussed but much less comprehensively.Comment: 20 pages + 9 pages references + 1 page figures. Submitted to the International Journal of Wildland Fir

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

Scientific knowledge and scientific uncertainty in bushfire and flood risk mitigation: literature review

EXECUTIVE SUMMARY The Scientific Diversity, Scientific Uncertainty and Risk Mitigation Policy and Planning (RMPP) project aims to investigate the diversity and uncertainty of bushfire and flood science, and its contribution to risk mitigation policy and planning. The project investigates how policy makers, practitioners, courts, inquiries and the community differentiate, understand and use scientific knowledge in relation to bushfire and flood risk. It uses qualitative social science methods and case studies to analyse how diverse types of knowledge are ordered and judged as salient, credible and authoritative, and the pragmatic meaning this holds for emergency management across the PPRR spectrum. This research report is the second literature review of the RMPP project and was written before any of the case studies had been completed. It synthesises approximately 250 academic sources on bushfire and flood risk science, including research on hazard modelling, prescribed burning, hydrological engineering, development planning, meteorology, climatology and evacuation planning. The report also incorporates theoretical insights from the fields of risk studies and science and technology studies (STS), as well as indicative research regarding the public understandings of science, risk communication and deliberative planning. This report outlines the key scientific practices (methods and knowledge) and scientific uncertainties in bushfire and flood risk mitigation in Australia. Scientific uncertainties are those ‘known unknowns’ and ‘unknown unknowns’ that emerge from the development and utilisation of scientific knowledge. Risk mitigation involves those processes through which agencies attempt to limit the vulnerability of assets and values to a given hazard. The focus of this report is the uncertainties encountered and managed by risk mitigation professionals in regards to these two hazards, though literature regarding natural sciences and the scientific method more generally are also included where appropriate. It is important to note that while this report excludes professional experience and local knowledge from its consideration of uncertainties and knowledge, these are also very important aspects of risk mitigation which will be addressed in the RMPP project’s case studies. Key findings of this report include: Risk and scientific knowledge are both constructed categories, indicating that attempts to understand any individual instance of risk or scientific knowledge should be understood in light of the social, political, economic, and ecological context in which they emerge. Uncertainty is a necessary element of scientific methods, and as such risk mitigation practitioners and researchers alike should seek to ‘embrace uncertainty’ (Moore et al., 2005) as part of navigating bushfire and flood risk mitigation

Data Fusion for Decision Support

This thesis demonstrates the utility of fusing data from multiple sources, including remote sensing data, in a Geographic Information System (GIS) for decision support by designing a new method of assessing wildfire risk in the wilderness urban interface (WUI) to facilitate better informed land management decisions and reduce mission impacts of wildfires on the military. Information from remote sensing systems has been used for decades to support decisions. Today, data are time and location tagged, making it possible to correlate and fuse disparate sources in a GIS, from which data can be stored, analyzed, and the resulting information shared. The GIS, relating data based on spatial attributes, has become an ideal fusion platform and decision support tool. In demonstration, decades of work in fire science were put to work, applying the Fire Susceptibility Index (FSI) on a new, 30 m scale with Landsat 8 data. Eight data sources were fused in a GIS to identify high-risk patches of wildland by calculating the FSI and preparing it for meaningful analysis and sharing. The initial results, qualitatively validated with wildfire behavior basics, appear promising, providing a view of fire danger in the landscape not seen in the current state of practice

Integrated graph measures reveal survival likelihood for buildings in wildfre events

Wildfre events have resulted in unprecedented social and economic losses worldwide in the last few years. Most studies on reducing wildfre risk to communities focused on modeling wildfre behavior in the wildland to aid in developing fuel reduction and fre suppression strategies. However, minimizing losses in communities and managing risk requires a holistic approach to understanding wildfre behavior that fully integrates the wildland’s characteristics and the built environment’s features. This complete integration is particularly critical for intermixed communities where the wildland and the built environment coalesce. Community-level wildfre behavior that captures the interaction between the wildland and the built environment, which is necessary for predicting structural damage, has not received sufcient attention. Predicting damage to the built environment is essential in understanding and developing fre mitigation strategies to make communities more resilient to wildfre events. In this study, we use integrated concepts from graph theory to establish a relative vulnerability metric capable of quantifying the survival likelihood of individual buildings within a wildfre-afected region. We test the framework by emulating the damage observed in the historic 2018 Camp Fire and the 2020 Glass Fire. We propose two formulations based on graph centralities to evaluate the vulnerability of buildings relative to each other. We then utilize the relative vulnerability values to determine the damage state of individual buildings. Based on a one-to-one comparison of the calculated and observed damages, the maximum predicted building survival accuracy for the two formulations ranged from 58 − 64% for the historical wildfres tested. From the results, we observe that the modifed random walk formulation can better identify nodes that lie at the extremes on the vulnerability scale. In contrast, the modifed degree formulation provides better predictions for nodes with mid-range vulnerability values

TEMPORAL CHANGES TO FIRE RISK IN DISPARATE WILDLAND URBAN INTERFACE COMMUNITIES

Since 1990, thirteen fires over 100,000 acres in size have burned in California seven of which were recorded to be some of the most destructive wildfires of all time (California Department of Forestry & Fire Protection 2013). To aid the development of policy that reduces the destruction caused by wildfires, it is important to evaluate how risk changes through time in communities that are expanding into fire-prone areas. The objective of this study is to discover how the likelihood of structural loss is changing in WUI as newer; more fire resilient structures replace older structures on the edges of the WUI. Geographical Information Systems and remote sensing techniques were used to observe changes in urbanization, structural materials, housing density and defensible space over time in the communities of Rancho Santa Fe, Ramona and Julian in San Diego County. Fire Risk ratings were calculated using the equation Fire Risk= Hazard – Mitigation. Mitigation scores for each structure were informed using a binary logistic regression of variables influencing home loss in the Witch Creek Fire. Fire Risk Ratings were given to the 11,747 structures in the three communities for the years 2005, 2009, 2010 and 2012. The study found that the initial 0-1.5m zone around the home is the most critical for defensible space. In this zone, increased tree cover increases the odds of structure loss by over double that of grass cover. In Rancho Santa Fe and Julian, the majority of very high risk homes were located in high income communities despite moderate mitigation due to very high fire hazard levels. In Ramona most very high fire risk homes were located in lower income areas due to poor mitigation levels. Rancho Santa Fe and Julian decreased their fire risk over the 7 year study period with improved mitigation, Rancho Santa Fe improved the most (1.7% decrease in Very High and High risk homes). The proportion of very high fire risk homes increased in Ramona by .5% over the 7 year study period. Development on the outskirts of the WUI could increase the risk of the overall community if proper construction standards are not met and defensible space is not implemented. If fire resistant communities are constructed and maintained to high standards of defensible space, they could potentially provide a buffer for older high fire risk homes