Integrated wildfire risk management: Measuring risk perceptions, simulating fire severity maps, and visualizing fire risk in the California Wildland-Urban Interface
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Integrated wildfire risk management: Measuring risk perceptions, simulating fire severity maps, and visualizing fire risk in the California Wildland-Urban Interface

Abstract

The escalating frequency and severity of wildfires in California have precipitated substantial economic losses and social strains, underscoring the imperative to comprehend the dimensions of wildfire management. This dissertation amalgamates three pivotal research endeavors focusing on different dimensions of wildfire risk: perceptions among wildland-urban interface residents, predictions of wildfire burn severity, and visualization of uncertainty. The surge in wildfires, coupled with the increasing population moving to Wildland-Urban Interface (WUI) areas, highlights the urgency of understanding both the physical and human dimensions of wildfire risk management. While various management practices involving communities have emerged as favored solutions, barriers to their implementation persist. Understanding public attitudes and perceptions regarding these practices is essential for successful fire management efforts.Furthermore, the warming climate and increasing fuel loads due to fire exclusion, compounded by climate change and drought, have led to more frequent, extensive, and severe wildfires. Burn severity, a metric that measures the ecological impact of fire on vegetation, is crucial for post-fire management. The Composite Burn Index (CBI) emerges as a preferred method of characterizing fire effects due to its comprehensive approach, offering a systematic and visually intuitive estimation of ecological impacts following a fire. Effective wildfire severity and risk information to stakeholders is paramount for enhancing understanding and promoting resilience. However, conveying complex information poses significant challenges. Visualization techniques play a vital role in conveying risk information and aiding comprehension of complex wildfire-related information. This research introduces a scalable visualization model for a use in predicting and managing the complex dynamics of wildfire occurrences. This dissertation advances our understanding of wildfire management by elucidating the complex interplay between public perceptions, burn severity estimation, and risk visualization. By integrating social perspectives with empirical modeling and visualization techniques, it offers multifaceted approach to addressing the challenges posed by wildfires in California. The insights garnered from this dissertation are crucial for informing policy decisions, guiding mitigation efforts, and fostering community resilience in the face of escalating wildfire threats.

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