Wildland Urban Interface
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Colorado State University engineers have developed a model that can predict how wildfire will impact a community, down to which buildings will burn. They say predicting damage to the built environment is essential to developing fire mitigation strategies and steps for recovery.
Conference website.
Coastal Quest, in partnership with Ventura County Wildfire Collaborative, is proud to present the first California Wildfire Conference. This three-day exchange will bring together a diverse community of wildfire practitioners to focus on understanding, preventing, and recovering from wildfires. The conference will be held from October 24-26, 2023, in Ventura County at the Ronald Reagan Presidential Library.
The Community Wildfire Mitigation Best Practices training is designed to increase the skills of the professional mitigation practitioner and individuals who run mitigation programs. Participation in the 9-week virtual course is not guaranteed as we endeavor to provide training to individuals who lead or have responsibility for community wildfire mitigation programs. Acceptance is on a per person basis and is not transferable to another person. If there are multiple people from an organization signing up please ensure they sign up individually.
Webinar registration.
This webinar will provide an overview of the major changes in CO-WRA, including modification of Scott and Burgan (2005) standard fire behavior fuel models to better reflect fuel types in Colorado, incorporating LiDAR to produce higher spatial resolution data products, and advanced wildland-urban interface risk analysis. Presenters will explain how these datasets and information can be used to: (1) increase public awareness about wildfire risk; (2) support wildfire risk reduction efforts, decision-making, and research from state to local scales; (3) identify high priority areas; (4) assist in the development of Community Wildfire Protection Plans (CWPPs) and other hazard mitigation plans; and (5) complement forest stewardship and forest management plans.
Webinar recording.
This webinar will provide an overview of the major changes in CO-WRA, including modification of Scott and Burgan (2005) standard fire behavior fuel models to better reflect fuel types in Colorado, incorporating LiDAR to produce higher spatial resolution data products, and advanced wildland-urban interface risk analysis. Presenters will explain how these datasets and information can be used to: (1) increase public awareness about wildfire risk; (2) support wildfire risk reduction efforts, decision-making, and research from state to local scales; (3) identify high priority areas; (4) assist in the development of Community Wildfire Protection Plans (CWPPs) and other hazard mitigation plans; and (5) complement forest stewardship and forest management plans.
After a brief review, presenters will explore discussions and questions from participants to address technical issues.
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This research is a case study of one community, located in Washington State, that is located on unprotected lands. Semi-structured, in-depth interviews were conducted with 32 participants who live in the study area. Participants were asked questions to assess their level of knowledge about unprotected lands and to determine their preferences regarding the introduction of formalized fire protection. Over the course of the field work, data was also gathered pertaining to participants’ capacity to adapt to wildfire and the social characteristics that are present within the community that could impact their ability to ‘live with wildfire.’ We discovered that a large proportion of participants were unaware that they had no formalized fire protection and displayed significant lack of knowledge about unprotected lands. Those participants, however, shared social characteristics with the participants that were aware of their level of fire protection that promote a sense of collective self-sufficiency and a rejection of outside interference. Those participants who were aware of the unprotected lands situation did profess a need for some type of additional fire protection for their community, but in general, participants favored managing wildfire risk on their own.
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Our modeling approach identifies spatial and temporal patterns of wildfire potential and risk, which is critical information to guide decision-making. Because the drivers behind risk shift over time, strategies to mitigate risk may need to account for multiple drivers simultaneously.
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We found the probability of WUI exposure from an active fire had close relationships with several explanatory variables including wind gust velocity, suppression difficulty, control potential, fireline arrangement, road densities, WUI block sizes, and the distance between WUI and the fire’s front. We found that the most important predictor variables influencing WUI exposure probability were gust, fireline arrangement, and distance from a fire ignition location to a WUI. We found that random forest models can achieve reasonable accuracy in estimating WUI fire exposure probabilities.
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Here, we outline barriers and opportunities in the next generation of fire science and provide guidance for investment in future research. We synthesize insights needed to better address the long-standing challenges of innovation across disciplines to (i) promote coordinated research efforts; (ii) embrace different ways of knowing and knowledge generation; (iii) promote exploration of fundamental science; (iv) capitalize on the “firehose” of data for societal benefit; and (v) integrate human and natural systems into models across multiple scales. Fire science is thus at a critical transitional moment. We need to shift from observation and modeled representations of varying components of climate, people, vegetation, and fire to more integrative and predictive approaches that support pathways toward mitigating and adapting to our increasingly flammable world, including the utilization of fire for human safety and benefit. Only through overcoming institutional silos and accessing knowledge across diverse communities can we effectively undertake research that improves outcomes in our more fiery future.
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This article outlines an approach for understanding the ways that local social context influences differential community adaptation to wildfire risk. I explain how my approach drew from Wilkinson’s interactional theory of community during various stages of its evolution and describe a series of advancements developed while extending the theory to promote collective action for wildfire. Extensions of Wilkinson’s work include organizing a range of adaptive capacity characteristics that help document differential community capacity for wildfire adaptation, introduction of “community archetypes” that reflect patterns of key adaptive capacity characteristics across cases, and development of fire adaptation “pathways” – combinations of policies, actions, and programs tailored to a range of community conditions.