Matthew Patrick Thompson

As development expands and extreme wildfire events worsen, people and property are increasingly exposed to wildfire hazards, necessitating comprehensive and accurate community and structure risk assessment tools. A variety of modeling frameworks and indices have been applied to support community wildfire planning, but few have been evaluated against subsequent fire events, which is a key gap for advancing the science and practice of risk management. Here we evaluate the performance of the Structure Exposure Score (SES) at predicting subsequent structure exposure. Specifically we analyze a SES layer current to 2017 landscape conditions and evaluate predictive performance over the western US across 2017-2022. We describe SES and evaluate its ability to discriminate wildfire-exposed from unexposed structures relative to a random classifier using an AUC-ROC approach. Results show that the mean SES value of exposed structures was 78.95% higher than unexposed structures. We report a mean per-fire AUC of 0.83, and an overall AUC of 0.76; the lower overall AUC is attributed to challenges predicting exposure from wildlands when events transition to urban conflagrations. Burn probability alone performed similarly to SES, suggesting it is sufficient where general exposure patterns are the focus, and SES more appropriate where the magnitude of exposure matters. Ember load was also an important predictor, especially on a per-fire basis (AUC = 0.76), consistent with the known role of ember transport in driving structure exposure in WUI fire environments. This work provides a foundational benchmark for future exposure prediction and assessment.

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; differences in empirical cumulative distribution functions of BP for burned/unburned areas were statistically significant; Logarithmic Skill Scores ranged from − 0.072 to 0.389 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.

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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.

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.