25 research outputs found
Housing Arrangement and Location Determine the Likelihood of Housing Loss Due to Wildfire
Surging wildfires across the globe are contributing to escalating residential losses and have major social, economic, and ecological consequences. The highest losses in the U.S. occur in southern California, where nearly 1000 homes per year have been destroyed by wildfires since 2000. Wildfire risk reduction efforts focus primarily on fuel reduction and, to a lesser degree, on house characteristics and homeowner responsibility. However, the extent to which land use planning could alleviate wildfire risk has been largely missing from the debate despite large numbers of homes being placed in the most hazardous parts of the landscape. Our goal was to examine how housing location and arrangement affects the likelihood that a home will be lost when a wildfire occurs. We developed an extensive geographic dataset of structure locations, including more than 5500 structures that were destroyed or damaged by wildfire since 2001, and identified the main contributors to property loss in two extensive, fire-prone regions in southern California. The arrangement and location of structures strongly affected their susceptibility to wildfire, with property loss most likely at low to intermediate structure densities and in areas with a history of frequent fire. Rates of structure loss were higher when structures were surrounded by wildland vegetation, but were generally higher in herbaceous fuel types than in higher fuel-volume woody types. Empirically based maps developed using housing pattern and location performed better in distinguishing hazardous from non-hazardous areas than maps based on fuel distribution. The strong importance of housing arrangement and location indicate that land use planning may be a critical tool for reducing fire risk, but it will require reliable delineations of the most hazardous locations
Probabilistic fire spread forecast as a management tool in an operational setting
Background: An approach to predict fire growth in an operational setting, with the
potential to be used as a decision-support tool for fire management, is described and
evaluated. The operational use of fire behaviour models has mostly followed a deterministic
approach, however, the uncertainty associated with model predictions needs
to be quantified and included in wildfire planning and decision-making process during
fire suppression activities. We use FARSITE to simulate the growth of a large wildfire.
Probabilistic simulations of fire spread are performed, accounting for the uncertainty
of some model inputs and parameters. Deterministic simulations were performed for
comparison. We also assess the degree to which fire spread modelling and satellite
active fire data can be combined, to forecast fire spread during large wildfires events.
Results: Uncertainty was propagated through the FARSITE fire spread modelling system
by randomly defining 100 different combinations of the independent input variables
and parameters, and running the correspondent fire spread simulations in order
to produce fire spread probability maps. Simulations were initialized with the reported
ignition location and with satellite active fires. The probabilistic fire spread predictions
show great potential to be used as a fire management tool in an operational setting,
providing valuable information regarding the spatial–temporal distribution of burn
probabilities. The advantage of probabilistic over deterministic simulations is clear
when both are compared. Re-initializing simulations with satellite active fires did not
improve simulations as expected.
Conclusion: This information can be useful to anticipate the growth of wildfires
through the landscape with an associated probability of occurrence. The additional
information regarding when, where and with what probability the fire might be in the
next few hours can ultimately help minimize the negative environmental, social and
economic impacts of these firesinfo:eu-repo/semantics/publishedVersio