Fire

The number of communities exposed to and affected by wildfire, particularly in the Wildland Urban Interface, is increasing, and both losses from and prevention of wildfire entail substantial economic costs. However, little is known about post-wildfire response by communities after structures are lost. Our goal was to characterize patterns and rates of rebuilding and new development after wildfires across the conterminous United States. We analyzed all wildfires that occurred across the conterminous United States from 2000 to 2005. We mapped 38,440 structures prior to fires, out of which 3,604 were burned, and 39,120 structures after fires, out of which 2,403 were new development and 1,881 were rebuilt. Nationally, rebuilding rates were low; only 25% of burned homes were rebuilt within five years, but rates were higher in the West, the South, and in Kansas. New development rates inside fire perimeters were similar to development rates in surrounding areas unaffected by fire. As a result, the number of structures within the fire perimeters was higher within 5 years of the fire than before, indicating that people want to live in wildland areas and are either willing to face the risks or not aware of them.

National-scale analyses of ?re occurrence are needed to prioritize ?re policy and management activities across the United States. However, the drivers of national-scale patterns of ?re occurrence are not well understood, and how the relative importance of human or biophysical factors varies across the country is unclear. Our research goal was to model the drivers of ?re occurrence within ecoregions across the conterminous United States. We used generalized linear models to compare the relative in?uence of human, vegetation, climate, and topographic variables on ?re occurrence in the United States, as measured by MODIS active ?re detections collected between 2000 and 2006. We constructed models for all ?res and for large ?res only and generated predictive maps to quantify ?re occurrence probabilities. Areas with high ?re occurrence probabilities were widespread in the Southeast, and localized in the Mountain West, particularly in southern California, Arizona, and New Mexico. Probabilities for large-?re occurrence were generally lower, but hot spots existed in the western and southcentral United States The probability of ?re occurrence is a critical component of ?re risk assessments, in addition to vegetation type, ?re behavior, and the values at risk. Many of the hot spots we identi?ed have extensive development in the wildland-urban interface and are near large metropolitan areas. Our results demonstrated that human variables were important predictors of both all ?res and large ?res and frequently exhibited nonlinear relationships. However, vegetation, climate, and topography were also signi?cant variables in most ecoregions. If recent housing growth trends and ?re occurrence patterns continue, these areas will continue to challenge policies and management efforts seeking to balance the risks generated by wild?res with the ecological bene?ts of ?re.

Wildfire ignition distribution models are powerful tools for predicting the probability of ignitions across broad areas, and identifying their drivers. Several approaches have been used for ignition-distribution modelling, yet the performance of different model types has not been compared. This is unfortunate, given that conceptually similar species-distribution models exhibit pronounced differences among model types. Therefore, our goal was to compare the predictive performance, variable importance and the spatial patterns of predicted ignition-probabilities of three ignition-distribution model types: one parametric, statistical model (Generalised Linear Models, GLM) and two machine-learning algorithms (Random Forests and Maximum Entropy, Maxent). We parameterised the models using 16 years of ignitions data and environmental data for the Huron-Manistee National Forest in Michigan, USA. Random Forests and Maxent had slightly better prediction accuracies than did GLM, but model fit was similar for all three. Variables related to human population and development were the best predictors of wildfire ignition locations in all models (although variable rankings differed slightly), along with elevation. However, despite similar model performance and variables, the map of ignition probabilities generated by Maxent was markedly different from those of the two other models. We thus suggest that when accurate predictions are desired, the outcomes of different model types should be compared, or alternatively combined, to produce ensemble predictions.

The wildland urban interface (WUI) delineates the areas where wildland fire hazard most directly impacts human communities and threatens lives and property, and where houses exert the strongest influence on the natural environment. Housing data are a major problem for WUI mapping. When housing data are zonal, the concept of a WUI neighborhood can be captured easily in a density measure, but variations in zone (census block) size and shape introduce bias. Other housing data are points, so zonal issues are avoided, but the neighborhood character of the WUI is lost if houses are evaluated individually. Our goal was to develop a consistent method to map the WUI that is able to determine where neighborhoods (or clusters of houses) exist, using just housing location and wildland fuel data. We used structure and vegetation maps and a moving window analysis, with various window sizes representing neighborhood sizes, to calculate the neighborhood density of both houses and wildland vegetation. Mapping four distinct areas (in WI, MI, CA and CO) the method resulted in amounts of WUI comparable to those of zonal mapping, but with greater precision. We conclude that this hybrid method is a useful alternative to zonal mapping from the neighborhood to the landscape scale, and results in maps that are better suited to operational fire management (e.g., fuels reduction) needs, while maintaining consistency with conceptual and U.S. policy-specific WUI definitions.

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.