Matthew P. Thompson’s research while affiliated with US Forest Service and other places

The escalation of wildfires in the USA, coupled with rising firefighting costs and decreasing workforce capacity, underscores the critical need to evaluate the efficiency and effectiveness of containment measures. However, the existing spatial data that records the locations and types of containment measures and wildfire perimeters contains numerous errors and redundancies. This paper presents a comprehensive fireline Quality Assurance and Quality Control dataset developed from the wildland firefighting operations data reported in the National Interagency Fire Center National Incident Feature Service. This improved dataset contains reliable spatial locations for fireline built during suppression operations, the associated verified fire perimeters, and identifies where containment was success or failure for fires greater than 1000 acres from 2017–2024. The improved final dataset represents critical information that was previously unavailable for assessing the success of fireline operations and incident management resource-use efficiency. The lessons learned from analyses utilizing this dataset are critical for improving the efficiency and effectiveness of the United States wildfire management system.

Wildfire simulation is deployed extensively to support risk management, and in the US has driven billions in federal investment. Foundational to strategic risk analysis is spatial information on the likelihood of burning in a fire year, typically provided by burn probability (BP) models. The recency of BP maps is a key driver of their accuracy, especially in disturbed landscapes that have experienced changes in fire spread potential. Few published examples exist comparing BP values against subsequent fire activity, and none to our knowledge evaluate annually updated BP maps. Here, we present a novel performance evaluation of the operational wildfire simulation system FSim, confronting updated BP maps with subsequent fire activity across the state of California over a 4-year period (2020–2023). Results show strong predictive ability: across 5 equal-area BP classes, 56.7–79.8% of the burned area occurred in the top 20% of mapped area; mean (median) BP values in burned areas were 238.5-348.8% (551.4-880.7%) greater than in unburned areas; empirical cumulative distribution functions of BP for burned/unburned areas were statistically significant; mean Log Skill Scores ranged from 0.276–0.339 against two reference models. Findings indicate reliable forecast performance and useful application of up-to-date BP maps, critical to support ongoing wildfire risk mitigation.

We present a mixed integer programming model for prioritizing fuel treatments within a landscape fuel break network to maximize protection against wildfires, measured by the total fire size reduction or the sum of Wildland Urban Interface areas avoided from burning. This model uses a large dataset of simulated wildfires in a large landscape to inform fuel break treatment decisions. Its mathematical formulation is concise and computationally efficient, allowing for customization and expansion to address more complex and challenging fuel break management problems in diverse landscapes. We constructed test cases for Southern California of the United States to understand model outcomes across a wide range of fire and fuel management scenarios. Results suggest optimal fuel treatment layouts within the Southern California’s fuel break network responding to various model assumptions, which offer insights for regional fuel break planning. Comparative tests between the proposed optimization model and a rule-based simulation approach indicate that the optimization model can provide significantly better solutions within reasonable solving times, highlighting its potential to support fuel break management and planning decisions.

Extreme spread events (ESEs), often characterized by high intensity and rapid rates of spread, can overwhelm fire suppression and emergency response capacity, threaten responder and public safety, damage landscapes and communities, and result in high socioeconomic costs and losses. Advances in remote sensing and geospatial analysis provide an improved understanding of observed ESEs and their contributing factors; however, there is a need to improve anticipatory and predictive capabilities to better prepare, mitigate, and respond. Here, leveraging individual-fire day-of-arrival raster outputs from the FSim fire modeling system, we prototype and evaluate methods for the simulation and categorization of ESEs. We describe the analysis of simulation outputs on a case study landscape in Colorado, USA, summarize daily spread event characteristics, threshold and probabilistically benchmark ESEs, spatially depict ESE potential, and describe limitations, extensions, and potential applications of this work. Simulation results generally showed strong alignment with historical patterns of daily growth and the proportion of cumulative area burned in the western US and identified hotspots of high ESE potential. Continued analysis and simulation of ESEs will likely expand the horizon of uses and grow in salience as ESEs become more common.

WildEST—the Wildfire Exposure Simulation Tool—is a cloud-based software system that produces continuously variable landscape-scale spatial data representing fire weather, flame-front, and ember characteristics as well as integrated measure of risk to buildings, wildfire hazard, and suppression difficulty.

Pre-fire mitigation efforts that include the installation and maintenance of fuel breaks are integral to wildfire suppression in Southern California. Fuel breaks alter fire behavior and assist in fire suppression at strategic locations on the landscape. However, the combined effectiveness of fuel breaks and wildfire suppression is not well studied. Using daily firefighting personnel to proxy the quantity and diversity of potential fire suppression operations (i.e., operational complexity), we examined 15 wildfires from 2017 to 2020 in the Los Padres, Angeles, San Bernardino, and Cleveland National Forests to assess how weather and site-specific fuel break characteristics influenced wildfire containment when leveraged during suppression operations. After removing effects of fuel treatments, wildfire and aerial firefighting, we estimated that expanding fuel break width in grass-dominant systems from 10 to 100 m increased the average success rate against a heading fire from 31 % to 41 %. Likewise, recently cleared fuel breaks had higher success rates compared to poorly maintained fuel breaks in both grass (25 % to 45 %) and shrub systems (20 % to 45 %). Combined, grass and shrub systems exhibited an estimated success rate of 80 % under mild weather conditions (20th percentile) and 19 % under severe weather (80th percentile). Other significant determinants included forb and grass production, adjacent tree canopy cover and terrain. Consistent with complexity theory and previous suppression effectiveness research, our analysis showed signs of suppression effectiveness declining as firefighter personnel increased. Future work could better account for the role of suppression with improved data on firefighting resource types, actions, locations, and timing. https://authors.elsevier.com/a/1jtGp4y2D1kEi5

We produced wildfire ignition probability datasets for both human and natural ignition sources for the western and southeastern portions of the US. These datasets describe modern ignition patterns in the form of annual ignition probability maps and inform resource allocation and other land management through improved fire modeling and wildfire risk assessments.

Background The decision making process undertaken during wildfire responses is complex and prone to uncertainty. In the US, decisions federal land managers make are influenced by numerous and often competing factors. Aims To assess and validate the presence of decision factors relevant to the wildfire decision making context that were previously known and to identify those that have emerged since the US federal wildfire policy was updated in 2009. Methods Interviews were conducted across the US while wildfires were actively burning to elucidate time-of-fire decision factors. Data were coded and thematically analysed. Key results Most previously known decision factors as well as numerous emergent factors were identified. Conclusions To contextualise decision factors within the decision making process, we offer a Wildfire Decision Framework that has value for policy makers seeking to improve decision making, managers improving their process and wildfire social science researchers. Implications Managers may gain a better understanding of their decision environment and use our framework as a tool to validate their deliberations. Researchers may use these data to help explain the various pressures and influences modern land and wildfire managers experience. Policy makers and agencies may take institutional steps to align the actions of their staff with desired wildfire outcomes.

As the frequency and intensity of power line-induced wildfires increase due to climate-, human-, and infrastructure-related risk drivers, maintaining power system resilience and reducing environmental impacts become increasingly crucial. This paper presents a comprehensive methodology to assess the susceptibility, vulnerability, and risk of power line-induced wildfires for lines and nodes in an electric grid. The methodology integrates a well-established wildfire spread simulator into power flow analysis through a set of analytical steps. The proposed approach is applied to a case study using the IEEE 30-bus test system mapped on a region in the Yosemite-Ritter section of the Sierra Nevada in California. The main findings include the identification of high-risk lines and high-impact nodes and quantification of their vulnerability. These insights can inform the implementation of microgrids, virtual power plants, and distributed energy resources (DERs) to increase grid resilience and guide risk mitigation efforts such as line undergrounding, vegetation management and maintenance procedures. The proposed methodology intends to provide an effective tool for power system planners and operators to assess the risk exposure of their grid to power line-induced wildfires, enabling them to make informed decisions for allocating capital to their resilience building and risk mitigation strategies.

In wildland–urban interface areas, firefighters balance wildfire suppression and structure protection. These tasks are often performed under resource limitations, especially when many structures are at risk. To address this problem, wildland firefighters employ a process called “structure triage” to prioritize structure protection based on perceived defensibility. Using a dataset containing triage assessments of thousands of structures within the Western US, we developed a machine learning model that can improve the understanding of factors contributing to assessed structure defensibility. Our random forest models utilized variables collected by wildland firefighters, including structural characteristics and the surrounding ignition zone. The models also used landscape variables not contained within the triage dataset that captured important information about accessibility, vegetation, topography, and structure density. We achieved a high overall accuracy (77.8%) in classifying structures as defensible or non-defensible. The presence of a safety zone was the most important factor in determining structure defensibility. Road proximity, vegetation composition, and topography were also found to have high importance. In addition to improving the understanding of factors considered by wildland firefighters, communities could also gain from this information by enhancing their wildfire response plans, focusing on targeted mitigation, and improving their overall preparedness.