Thepurposeoftheproposedresearchistohelpadvanceindustryknowledgeandunderstandingofholisticparcel-,neighborhood-andcommunity-levelwildfirerisktobetterinformmitigationplanning, policies, programs,…

The University of California, Santa Barbara (UCSB) was awarded a $551,580 Cooperative Agreement from the Department of the Interior's Bureau of Land Management Idaho Region under the Joint Fire Science Program (CFDA 15.232) on September 24, 2024. The purpose of this research project is to advance industry knowledge and understanding of holistic parcel-, neighborhood-, and community-level wildfire risk to better inform mitigation planning, policies, programs, and regulatory efforts in the wildland-urban interface (WUI). The key products to be delivered include:

1. Results-oriented, multi-scale factor enhancements to existing parcel-level WUI scoring tools;
2. Comprehensive, multi-scale neighborhood and community-scale WUI risk scoring tools; and
3. Probabilistic structure damage-loss potential models substantiated by assessments of real-world performance.

UCSB will utilize a combination of quantitative, semi-quantitative, and field data collection methods to understand, evaluate, and design improvements to current tools and methodologies. UCSB has also awarded a $169,920 sub-grant to Jensen Hughes, Inc. to support tasks such as literature review, fire service engagement, geospatial database development, retrospective analysis, and spatial analytics.

Thepurposeoftheproposedresearchistohelpadvanceindustryknowledgeandunderstandingofholisticparcel-,neighborhood-andcommunity-levelwildfirerisktobetterinformmitigationplanning, policies, programs, andregulatoryeffortsinthewui.theaimistodevelop(1)results-oriented, multi-scalefactorenhancementstoexistingparcel-levelwuiscoringtool(s) (2)comprehensive, multi-scaleneighborhoodandcommunity-scalewuiriskscoringtools and(3)probabilisticstructuredamage-losspotentialmodelsthataresubstantiatedbyassessmentsofreal-worldperformance.thiswillbeachievedusingacombinationofquantitativemethods(e.g.,geospatialstatisticalanalysis, machinelearning),semi-quantitativemethods(e.g.,retrospectiveanalysis, expertpanelreview),andfielddatacollectiontounderstand, evaluateanddesignimprovementstocurrenttoolsandmethodologies.