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

The simulation of urban-scale evacuation scenarios: Swinley forest fire

Forest fires are an annual occurrence in many parts of the world causing evacuation of nearby residential areas and industrial facilities. The frequent occurrence of these events deems it necessary to develop appropriate evacuation plans for areas that are susceptible to forest fires. A well-established and well-validated evacuation model, buildingEXODUS, has been extended to model large scale urban/rural evacuations by including the road network and open spaces (e.g. parks, green spaces and town squares)along with buildings. The evacuation simulation results have been coupled with the results of a forest fire spread model and applied to the Swinley forest fire. Four evacuation procedures differing in the routes taken by the pedestrians were simulated and analysed providing key evacuation statistics such as time to reach the assembly location, the distance travelled and congestion experienced by the agents. In addition, the safety margins associated with using each evacuation route are identified. This is the time available between the safe passage of the pedestrians through the route and the route being considered no longer safe for pedestrian use. A key finding of this work is the importance of formulating evacuation procedures in response to wildfires by providing occupants timely evacuation notice and appropriate choice of routes to keep them at a safe distance from the fire even at the cost of taking longer evacuation routes

Analyzing evacuation decisions using multi-attribute utility theory (MAUT)

Emergency managers are faced with critical evacuation decisions. These decisions must balance conflicting objectives as well as high levels of uncertainty. Multi-Attribute Utility Theory (MAUT) provides a framework through which objective trade-offs can be analyzed to make optimal evacuation decisions. This paper is the result of data gathered during the European Commission Project, Evacuation Responsiveness by Government Organizations (ERGO) and outlines a preliminary decision model for the evacuation decision. The illustrative model identifies levels of risk at which point evacuation actions should be taken by emergency managers in a storm surge scenario with forecasts at 12 and 9 hour intervals. The results illustrate how differences in forecast precision affect the optimal evacuation decision. Additional uses for this decision model are also discussed along with improvements to the model through future ERGO data-gathering

Master of Science

thesisDuring rapid-onset disasters, timely dissemination of warning information to the public is crucial. Official emergency information channels are often slow, leaving the public to monitor social media websites for more timely updates. Examining Twitter communications, or tweets, sent during the 2012 Waldo Canyon Fire, this research seeks to determine what level of descriptive information is sent through Twitter during a wildfire, whether or not that information can inform other users of changes in fire activity, and how the spatial and temporal information within a tweet can be used in conjunction with geographic information systems (GIS) to determine fire location and activity. This research utilized geotagged tweets and viewshed analysis in GIS as a means of determining what portions of the wildfire are visible from each Twitter user. These visible areas, or viewsheds, were then overlapped with viewsheds from other users to generate shared viewsheds. Both individual and shared viewsheds were compared to the area of new fire growth to determine if burning areas could be more confidently identified by considering different user perspectives. The shared viewshed method showed that while increasing the number of observations does result in a decrease in shared visible area, the portion of the shared viewshed that falls within the fire boundary significantly increases. Many groupings, iv which were compiled based on time sent and ranged in size from two to eight tweets, could see more than 20% of the fire. This research found that there is the potential for users to inform one another of changes in fire activity that may not be visible from different points of view. The addition of viewshed analysis adds another layer of valuable information to the tweets and could be useful if done in real-time, especially during events occurring at a smaller scale

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

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 WATER BALANCE AND SEDIMENT YIELD ANALYSIS MODEL FOR THE LOPEZ LAKE RESERVOIR

Lopez Lake Reservoir is the primary source of potable water for the Cities of Arroyo Grande, Grover Beach, Pismo Beach, and to the Community Service Districts of Oceano and Avila Beach. In this study, a water balance and sediment yield analysis model was developed for the reservoir’s watershed. The model was used to estimate evaporation from the lake and to examine the effects of a wildfire on the reservoir. Evaporation and wildfire are dependent on variables that change on a spatial and temporal scale, making modeling challenging. The County of San Luis Obispo uses pan coefficients to estimate evapotranspiration losses from the reservoir. In this study, a water balance model was developed using a watershed model known as Soil and Water Assessment Tool, SWAT. Evaporation loss from the lake was calculated using the inflows simulated by the model, and other fluxes (e.g., water released for consumption to Arroyo Grande Creek, precipitation) that were obtained from the County of San Luis Obispo. The evaporation values estimated by the pan coefficient model were significantly higher than the water balance and the Penman-Monteith predictions. The Penman-Monteith method estimates seem more reasonable for the lake. SWAT was also used to simulate effects of a wildfire on sediment inflow and sediment yield into the reservoir for a year after a simulated fire. Results showed that sediment inflow rates increased by a factor of 3 following the simulated wildfire. Lopez Lake Reservoir’s capacity would be significantly affected by a wildfire. To improve the evaporation estimates it is recommended that the County of San Luis Obispo install streamflow gauges to measure the inflow into the reservoir. Using the streamflow gauges the reservoir evaporation could be calculated using the water balance method. Adding climate gauges at the reservoir would increase the accuracy of the Penman-Monteith method. Sediment gauges in the watershed would provide a calibration data source for the model as well as data collection points in the event of an actual wildfire

Probability-Based Wildfire Risk Measure for Decision-Making

Wildfire is a natural element of many ecosystems as well as a natural disaster to be prevented. Climate and land usage changes have increased the number and size of wildfires in the last few decades. In this situation, governments must be able to manage wildfire, and a risk measure can be crucial to evaluate any preventive action and to support decision-making. In this paper, a risk measure based on ignition and spread probabilities is developed modeling a forest landscape as an interconnected system of homogeneous sectors. The measure is defined as the expected value of losses due to fire, based on the probabilities of each sector burning. An efficient method based on Bayesian networks to compute the probability of fire in each sector is provided. The risk measure is suitable to support decision-making to compare preventive actions and to choose the best alternatives reducing the risk of a network. The paper is divided into three parts. First, we present the theoretical framework on which the risk measure is based, outlining some necessary properties of the fire probabilistic model as well as discussing the definition of the event ‘fire’. In the second part, we show how to avoid topological restrictions in the network and produce a computable and comprehensible wildfire risk measure. Finally, an illustrative case example is included