Fire

Following the loss of homes to wildfire, when risk has been made apparent, homeowners must decide whethe to rebuild, and choose materials and vegetation, while local governments guide recovery and rebuilding. As wildfires ar smaller and more localised than other disasters, it is unclear if recovery after wildfire results in policy change and adaptation decreasing assets at risk, or if recovery encourages reinvestment in hazard-prone areas. We studied three wildfires on th Colorado Front Range from 2010 to 2012 that each destroyed over 150 homes, describing policy response and characterisin the built environment after wildfire. In each location, we found some adaptation, through better-mitigated homes an stronger building and vegetation mitigation standards, but also extensive reinvestment in hazard-prone environments, wit governmental support. Despite suggestions that disaster can lead to substantial policy change and elevate the role of land-us planning, we saw only modest reforms: local governments did not revise land-use regulations; a statewide task forc considered but did not require standards for building and vegetation mitigation; and only one jurisdiction strengthened it building and vegetation mitigation standards. Experiences in Colorado suggest that time after wildfire either does no provide extensive opportunities for adaptation in the built environment, or that these opportunities are easily missed.

Context Wildfires destroy thousands of building every year in the wildland urban interface. However fire typically only destroys a fraction of the building within a given fire perimeter, suggesting more coul be done to mitigate risk if we understood how t configure residential landscapes so that both peopl and buildings could survive fire Objectives Our goal was to understand the relativ importance of vegetation, topography and spatia arrangement of buildings on building loss, within th fire’s landscape context Methods We analyzed two fires: one in San Diego CA and another in Boulder, CO. We analyzed Googl Earth historical imagery to digitize buildings expose to the fires, a geographic information system t measure some of the explanatory variables, an FRAGSTATS to quantify landscape metrics. Usin logistic regression we conducted an exhaustive mode search to select the best models Results The type of variables that were importan varied across communities. We found complex spatia effects and no single model explained building los everywhere, but topography and the spatial arrangemen of buildings explained most of the variability i building losses. Vegetation connectivity was mor important than vegetation type Conclusions Location and spatial arrangement o buildings affect which buildings burn in a wildfire which is important for urban planning, building siting landscape design of future development, and to target fire prevention, fuel reduction, and homeowner educatio efforts in existing communities. Landscap context of buildings and communities is an importan aspect of building loss, and if taken into consideration could help communities adapt to fire.

Wildfire management now emphasizes fire-adapted communities that coexist wit wildfires, although it is unclear how communities will progress to this goal. Hazard research suggests that response to wildfire—specifically, rebuilding after fire—ma be a crucial opportunity for homeowner and community adaptation. We explor rebuilding after the 2010 Fourmile Canyon Fire from Boulder, CO, that destroye 165 homes, to better understand individual and community adaptation after wildfire We examined changes in perception of fire risk and structural characteristics an vegetation mitigation of rebuilt homes, to examine how homes, homeowners, an communities changed after fire. We found evidence that adaptation is occurring as well as evidence that it is not. Overall, rebuilding was slow. More than 3 2 year after the fire, only 30% of those who had lost homes had rebuilt. Postfire rebuildin will only change a fraction of homes, but it is a critical process to understand

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.

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.

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.

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.