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Housing Arrangement, Location Determine Likelihood of Structure Loss Due to Wildfire in Southern California


Housing Arrangement, Location Determine Likelihood of Structure Loss Due to Wildfire in Southern California

LOS ANGELES – Certain housing factors, such as being located in areas with a history of frequent fire or being surrounded by wildland vegetation, can increase the risk of structure loss in the event of wildfires. This strong importance of housing arrangement and location indicate that land use planning may be a critical tool for reducing fire risk. 

In both the Santa Monica Mountains region and the San Diego region, analysis showed that the spatial arrangement and location of housing structures significantly influenced the likelihood of structure loss — whether structures were destroyed or damaged. In particular, structures were most likely to burn in areas with low to intermediate housing density, and this was the single most important determinant of property loss in San Diego.

Structure losses were also greatest in areas with a history of frequent fire, and this was the single most influential predictor of structure loss in the Santa Monica Mountains. Losses were higher when structures were surrounded by wildland vegetation, and in the Santa Monica Mountains, losses were higher in herbaceous fuel types than in woody fuel types. 

“Wildland fire is a fact of life in the Mediterranean-type climate of Southern California, and the costs are mounting from fire losses,” says USGS Director Marcia McNutt. “This report is a must read: it uses excellent scientific evidence to demonstrate where and how to plan communities to minimize the risk that homeowners will become fire victims.” 

Researchers from the Conservation Biology Institute, U.S. Geological Survey and University of Wisconsin published their findings in the journal PLoS ONE. The team analyzed public records and satellite imagery of thousands of housing structures before and after wildfires — some 5,500 fire-affected structures out of 36,399 structures in Ventura and Los Angeles counties and 687,869 structures in San Diego County. 

“Traditionally, fire risk management efforts have focused on the wildlands side, such as treatments to stop wildfires from reaching the urban environment, or attempting to suppress fire altogether.  But a another key aspect of fire risk has been missing from the discussion — rethinking where we put homes in the first place,” says Jon Keeley, fire ecologist at the USGS Western Ecological Research Center and study coauthor. “This study provides evidence for a completely new way to think about fire risk management.” 

Researchers also addressed the need for maps in the new study, by testing traditionally used maps — based on the assumption that fuel distribution is the primary determinant of hazard — against fire hazard maps developed using a combination of factors that included housing location and spatial arrangement. The fuel-based maps correctly identified general patterns of fire hazard across the state. However, at the regional scale, fuel-based maps did not predict structure loss as well as maps that incorporated housing location and spatial arrangement factors. 

The study is the first major product of the USGS Wildfire Risk Scenario Project, a federal-state-local partnership exploring wildfire hazard risks along the “wildland-urban interface” of Southern California suburbia — where housing communities meet the natural chaparral. 

Organized by Keeley, an international team of fire ecologists, geographers and economists is examining 1) the factors that determine when and where fires start; 2) how fires reach housing from the wildland; 3) the factors that lead to home and structure loss once fires reach an urban area; and 4) which fire management and wildfire factors lead to biodiversity change and natural resource impact. 

“The new study pinpoints the factors that determine where houses are most likely to be exposed to fire and where they’re likely to burn once fire reaches a community,” says Alexandra Syphard, an ecologist with the Conservation Biology Institute and the lead author of the study.

While Syphard and colleagues still need to analyze and model the overall picture of fire risk across the wildland-urban interface and in the context of regional and local conditions, they say this initial study has already provided important revelations. 

“For example, there’s the popular notion that if you clear-cut woody, chaparral vegetation around a home, that should provide some sort of fire barrier,” says Syphard. “Yet in the Santa Monica Mountains, we found that homes are more likely to be lost if they are surrounded by grassy, herbaceous vegetation — precisely the type of plants that move in after you clear-cut native chaparral plants.” 

“Wildfires are unpreventable, natural phenomena in the chaparral landscapes of Southern California,” says Keeley. “We can’t control the seasons or the weather, but are there factors that urban planners and hazard managers can control to minimize the risk of housing damage from fires? That’s the key question, and this is our first detailed look at what those factors might be.” 

Jon Keeley will be among the panelists at The Mediterranean City conference on climate change adaptation, on Monday, June 25, in Los Angeles. 

The Southern California Wildfire Risk Scenario project is a collaborative partnership of USGS Western Ecological Research center, USGS Western Geographic Science Center, Conservation Biology Institute and University of Wollongong, Australia. It is funded by USGS. Fire Scenario research findings will be shared with the National Park Service, USDA Forest Service, California state and local governments and industry partners.

Housing Location or Arrangement Factor Risk of Structure loss:
San Diego Region
Risk of Structure loss:
Los Angeles/Ventura Region
Housing structures arranged in small, isolated clusters, characterized by low to intermediate housing density and fewer roads Structure loss more likely Structure loss more likely
Housing structures located near the edge of a development Structure loss more likely Structure loss more likely
Housing structures  located on steep slopes Structure loss more likely Structure loss more likely
Housing structure located close to the coast

Structure loss less likely

Structure loss more likely
Housing structure located in areas with high historic fire frequency

Structure loss non-linearly related

Structure loss more likely
Housing structures surrounded by wildland vegetation rather than by urban or impervious areas Structure loss more likely Structure loss more likely

Table: Housing location and spatial arrangement factors that determine property loss from wildfires.

USGS Newsroom


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Parameter Value Description
Magnitude mb The magnitude for the event.
Longitude ° East Decimal degrees longitude. Negative values for western longitudes.
Latitude ° North Decimal degrees latitude. Negative values for southern latitudes.
Depth km Depth of the event in kilometers.
Place Textual description of named geographic region near to the event. This may be a city name, or a Flinn-Engdahl Region name.
Time 1970-01-01 00:00:00 Time when the event occurred. UTC/GMT
Updated 1970-01-01 00:00:00 Time when the event was most recently updated. UTC/GMT
Timezone offset Timezone offset from UTC in minutes at the event epicenter.
Felt The total number of felt reports
CDI The maximum reported intensity for the event.
MMI The maximum estimated instrumental intensity for the event.
Alert Level The alert level from the PAGER earthquake impact scale. Green, Yellow, Orange or Red.
Review Status Indicates whether the event has been reviewed by a human.
Tsunami This flag is set to "1" for large events in oceanic regions and "0" otherwise. The existence or value of this flag does not indicate if a tsunami actually did or will exist.
SIG A number describing how significant the event is. Larger numbers indicate a more significant event.
Network The ID of a data contributor. Identifies the network considered to be the preferred source of information for this event.
Sources A comma-separated list of network contributors.
Number of Stations Used The total number of Number of seismic stations which reported P- and S-arrival times for this earthquake.
Horizontal Distance Horizontal distance from the epicenter to the nearest station (in degrees).
Root Mean Square sec The root-mean-square (RMS) travel time residual, in sec, using all weights.
Azimuthal Gap The largest azimuthal gap between azimuthally adjacent stations (in degrees).
Magnitude Type The method or algorithm used to calculate the preferred magnitude for the event.
Event Type Type of seismic event.
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