Isolating the Primary Drivers of Fire Risk to Structures in WUI regions in California

Isolating the Primary Drivers of Fire Risk to Structures in WUI regions in California | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Isolating the Primary Drivers of Fire Risk to Structures in WUI regions in California Michael Gollner, Maryam Zamanialaei, Daniel San Martin, Maria Theodori, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5776626/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 28 Aug, 2025 Read the published version in Nature Communications → Version 1 posted You are reading this latest preprint version Abstract The destructive impacts of Wildland-Urban Interface (WUI) fires on people, property and the environment have dramatically increased, especially in California. Critical factors influencing structure protection during wildfires, including home hardening (e.g., vents, siding, roof, eaves, window, construction year), defensible space (vegetation and surrounding features), exposure to flames and embers, and structure separation are well known but their interrelated impacts are not quantified. Here, we find that structure separation and exposure significantly influence the probability of loss, underscoring the role of large conflagrations in driving widespread destruction. Machine learning models combined with previously unavailable exposure data enhance the predictive accuracy of structure loss up to 82%. Home hardening and defensible space, especially closest to the home, are still vital and effective mitigation measures, cutting hypothetical structures losses by 52%. Our results offer data-driven actionable insights for prioritizing mitigation strategies to protect vulnerable communities and structures in the WUI. Earth and environmental sciences/Natural hazards Physical sciences/Engineering/Mechanical engineering Full Text Additional Declarations There is NO Competing Interest. Supplementary Files DriversofFireRisktoStructuresinWUIsupplemental.docx Supplemental Material Cite Share Download PDF Status: Published Journal Publication published 28 Aug, 2025 Read the published version in Nature Communications → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5776626","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":414456554,"identity":"45588263-cfe8-41e5-a966-fa146c6711e1","order_by":0,"name":"Michael 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