Wildland–urban interface explained

The wildland–urban interface (WUI) is a zone of transition between wilderness (unoccupied land) and land developed by human activity – an area where a built environment meets or intermingles with a natural environment. Human settlements in the WUI are at a greater risk of catastrophic wildfire.

Definitions

In the United States, the wildland–urban interface (WUI) has two definitions. The US Forest Service defines the wildland–urban interface qualitatively as a place where "humans and their development meet or intermix with wildland fuel."[1] Communities that are within 0.5miles of the zone are included. A quantitative definition is provided by the Federal Register, which defines WUI areas as those containing at least one housing unit per 40acres.

The Federal Register definition splits the WUI into two categories based on vegetation density:

Growth

Human development has increasingly encroached into the wildland–urban interface.

Population shifts

The WUI was the fastest-growing land use type in the United States between 1990 and 2010. Factors include geographic population shifts, expansion of cities and suburbs into wildlands, and vegetative growth into formerly unvegetated land. The primary cause has been migration. Of new WUI areas, 97% were the result of new housing. In the United States there are population shifts towards the WUIs in the West and South; increasing nationally by 18 percent per decade, covering 6 million additional homes between 1990 and 2000 which in 2013 was 32 percent of habitable structures. Globally, WUI growth includes regions such as Argentina, France, South Africa, Australia, and regions around the Mediterranean sea.[3] [4] Going forward it is expected the WUI will continue to expand; an anticipated amenity-seeking migration of retiring baby-boomers to smaller communities with lower costs of living close to scenic and recreational natural resources will contribute to WUI growth. Climate change is also driving population shifts into the WUI as well as changes in wildlife composition.[5] [6] [7]

Ecological effects

Housing growth in WUI regions can displace and fragment native vegetation. The introduction of non-native species by humans through landscaping can change the wildlife composition of interface regions. Pets can kill large quantities of wildlife.[8]

Forest fragmentation is another impact of WUI growth, which can lead to unintended ecological consequences. For instance, increased forest fragmentation can lead to an increase in the prevalence of Lyme disease.[9] White-footed mice, a primary host of the Lyme tick, thrive in fragmented habitats.[10]

Increased urbanisation has a variety of effects on plant life. Depending on the influences that are present, some plant traits like woodiness and height may increase while many other traits either show mixe responses or are not well studied.[11]

Additionally, disease vectors in isolated patches can undergo genetic differentiation, increasing their survivability as a whole.

Increases in wildfire risk pose a threat to conservation in WUI growth regions.

Ecological change driven by human influence and climate change has often resulted in more arid and fire-prone WUI. Factors include climate change driven vegetation growth and introduction of non-native plants, insects, and plant diseases.[12]

In North America, Chile, and Australia, unnaturally high fire frequencies due to exotic annual grasses have led to the loss of native shrublands.

Fire

Human development has increasingly encroached into the wildland–urban interface. Coupled with a recent increase in large wildland fires, this has led to an increase in fire protection costs. Between 1985–1894 and 2005–2014, the area burned by wildfires in the United States nearly doubled from 18,000 to 33,000 square kilometers. Wildfires in the United States exceeding 50000acres have steadily increased since 1983; the bulk in modern history occurred after 2003.[13] In the United States, from 1985 to 2016, federal wildfire suppression expenditures tripled from $0.4 billion per year to $1.4 billion per year.

Wildfire risk assessment

Calculating the risk posed to a structure located within a WUI is through predictive factors and simulations. Identifying risk factors and simulation with those factors help to understand and then manage the wildfire threat.

For example, a proximity factor measures the risk of fire from wind carried embers which can ignite new spot fires over a mile ahead of a flame front. A vegetation factor measures the risk those wind carried embers have of starting a fire; lower vegetation has a lower risk.

A quantitative risk assessment simulation combines wildfire threat categories. Areas at the highest risk are those where a moderate population overlaps or is adjacent to a wildland that can support a large and intense wildfire and is vulnerable with limited evacuation routes.[14]

Risk factors

The Calkin framework predicts a catastrophic wildfire in the Wildland–urban Interface (WUI), with three categories of factors. These factors allow for an assessment of a degree of wildfire threat. These are ecological factors that define force, human factors that define ignition, and vulnerability factors that define damage. These factors are typically viewed in a geospatial relationship.

The ecological factor category includes climate, seasonal weather patterns, geographical distributions of vegetation, historical spatial wildfire data, and geographic features.[5] The ecological determines wildfire size and intensity.

The human factor category includes arrangement and density of housing. Density correlates with wildfire risk for two reasons. First, people cause fires; from 2001 to 2011, people caused 85% of wildfires recorded by the National Interagency Fire Center (NIFC). Second, housing intensifies wildfires because they contain flammable material and produce mobile embers, such as wood shakes. The relationship between population density and wildfire risk is non-linear. At low population densities, human ignitions are low. Ignitions increase with population density. However, there is a threshold of population density at which fire occurrence decreases. This is true for a range of environments in North America, the Mediterranean Basin, Chile, and South Africa. Possible reasons for a decrease include decreases in open space for ember transmission, fuel fragmentation due to urban development, and higher availability of fire-suppression resources. Areas with moderate population densities tend to exhibit higher wildfire risk than areas with low or high population densities.

The vulnerability factor category is measured with evacuation time through a proximity of habitable structures to roads, matching of administrators to responsibilities, land use, building standards, and landscaping types.

Risk simulations

Wildfire spread is commonly simulated with a Minimum Travel Time (MTT) algorithm.

Prior to MTT algorithms, fire boundaries were modeled through an application of Huygens' principle; boundaries are treated as wave fronts on a two-dimensional surface.

Minimum Travel Time (MTT) methods build on Huygens' principle to find a minimum time for fire to travel between two points. MTT assumes nearly-constant factors such as environmental factors for wind direction and fuel moisture. The MTT is advantageous over Huygens in scalability and algorithm speed. However, factors are dynamic and a constant representation comes at a cost of a limited window and thus MTT is only applicable to short-timescale simulations.[15]

Risk management

Structure and vegetation flammability is reduced through community-focused risk management through reduction of community vulnerabilities. The degree of control of vulnerability to wildfires is measured with metrics for responsibilities and zones of defenses.

Reducing risk through responsibility distribution

The probability of catastrophic WUI wildfire is controlled by assignment of responsibility for three actionable WUI objectives: controlling potential wildfire intensity, reducing ignition sources, and reducing vulnerability. When these objectives are met, then a community is a fire-adapted community. The U.S. Forest Service defines fire-adapted communities as "a knowledgeable and engaged community in which the awareness and actions of residents regarding infrastructure, buildings, landscaping, and the surrounding ecosystem lessens the need for extensive protection actions and enables the community to safely accept fire as a part of the surrounding landscape."

Three groups are responsible for achieving the three WUI objectives, these are land management agencies, local governments, and individuals.[16]

Fire-adapted communities have been successful in interacting with wildfires.

The key benefit of fire-adapted communities is that a reliance on individuals as a core block in the responsibility framework reduces WUI expenditures by local, regional, and national governments.[17]

Reducing risk through zone defenses

The risk of a structure to ignite in a wildfire is calculated by a Home Ignition Zone (HIZ) metric. The HIZ includes at a minimum the space within a 200foot radius around a structure.[18] The HIZ is a guideline for whoever is responsible for structure wildfire protection; landlords and tenants (homeowner if they are the same) are responsible for physically constructing and maintaining defense zones while local government defines land use boundaries in a way that defense zones are effective (note: fire-resistant is arbitrary and is not defined in hours of resistance for a given degree of heat; these guidelines are relaxed for non-evergreen trees which are less flammable; this guide is not intended to prevent combustion of individual structures in a wildfire—it is intended to prevent catastrophic wildfire in the WUI):

Challenges to risk management

There are three challenges.

An example of the fire-adapted communities performance was demonstrated in November 2018 when the Camp Fire passed through the community of Concow in Butte County, California. The Concow community was a fire-adapted community.[19] This late season fire provided a stress test of the fire-adapted communities theory. The Concow community was destroyed. The wildfire continued through the community without demonstrating the expected slowing of the flame front. If there was a slowing it was less than anticipated though any slowing contributed to allowing residents to evacuate ahead of the flame front. The wildfire continued through wildlands between the community of Concow and the town of Paradise, California. The wildfire then destroyed the town of Paradise which was in the process of developing into a fire-adapted community.[20] The wildfire ignition is suspected to have originated with unhardened electrical transmission line infrastructure which had recently been redesigned though had not been reconstructed and the new design did not include hardening against ignition where it passed through the WUI.[21] The Camp Fire demonstrated limitations of the fire-adapted community theory in late season wildfires driven by Katabatic winds, and in the land management agencies' responsibility in controlling infrastructure ignition sources.

See also

External links

Notes and References

  1. Web site: Stein. Susan M.. Comas. Sara J.. Menakis. James P.. Steward. Susan I.. Cleveland. Helene. Bramwell. Lincoln. Radeloff. Volker. Wildfire, Wildlands, and People: Understanding and Preparing for Wildfire in the Wildland-Urban Interface. USDA Forest Service. USDA. 8 May 2018.
  2. Radeloff. V. C.. Hammer. R. B.. Stewart. S. I.. Fried. J. S.. Holcomb. S. S.. McKeefry. J. F.. The Wildland-Urban Interface in the United States. Ecological Applications. 2005. 15. 3. 799–805. 8 May 2018. 10.1890/04-1413. 52087252 .
  3. Radeloff . Volker C. . Helmers . David P. . Kramer . H. Anu . Mockrin . Miranda H. . Alexandre . Patricia M. . Bar-Massada . Avi . Butsic . Van . Hawbaker . Todd J. . Martinuzzi . Sebastián . Syphard . Alexandra D. . Stewart . Susan I. . Rapid growth of the US wildland-urban interface raises wildfire risk . Proceedings of the National Academy of Sciences . 27 March 2018 . 115 . 13 . 3314–3319 . 10.1073/pnas.1718850115 . 29531054 . 5879688 . 2018PNAS..115.3314R . free .
  4. Syphard . Alexandra D. . Radeloff . Volker C. . Hawbaker . Todd J. . Stewart . Susan I. . Conservation Threats Due to Human-Caused Increases in Fire Frequency in Mediterranean-Climate Ecosystems . Conservation Biology . June 2009 . 23 . 3 . 758–769 . 10.1111/j.1523-1739.2009.01223.x . 22748094 . 205657864 . free .
  5. Keane. Robert E.. Holsinger. Lisa M.. Parsons. Russell A.. Gray. Kathy. February 2008. Climate change effects on historical range and variability of two large landscapes in western Montana, USA. Forest Ecology and Management. 254. 3. 375–389. 10.1.1.165.4567. 10.1016/j.foreco.2007.08.013. 7262853 .
  6. Radeloff. Volker C.. Helmers. David P.. Kramer. H. Anu. Mockrin. Miranda H.. Alexandre. Patricia M.. Bar-Massada. Avi. Butsic. Van. Hawbaker. Todd J.. Martinuzzi. Sebastián. Syphard. Alexandra D.. Stewart. Susan I.. 27 March 2018. Rapid growth of the US wildland-urban interface raises wildfire risk. Proceedings of the National Academy of Sciences. 115. 13. 3314–3319. 2018PNAS..115.3314R. 10.1073/pnas.1718850115. 5879688. 29531054. free.
  7. Tania Schoennagel. Schoennagel. Tania. Balch. Jennifer K.. Brenkert-Smith. Hannah. Dennison. Philip E.. Harvey. Brian J.. Krawchuk. Meg A.. Mietkiewicz. Nathan. Morgan. Penelope. Moritz. Max A.. Rasker. Ray. Turner. Monica G.. 2 May 2017. Adapt to more wildfire in western North American forests as climate changes. Proceedings of the National Academy of Sciences of the United States of America. 114. 18. 4582–4590. 10.1073/pnas.1617464114. 5422781. 28416662. Whitlock. Cathy. 2017PNAS..114.4582S . free.
  8. Loss . Scott R. . Will . Tom . Marra . Peter P. . The impact of free-ranging domestic cats on wildlife of the United States . Nature Communications . 29 January 2013 . 4 . 1 . 1396 . 10.1038/ncomms2380 . 23360987 . 2013NatCo...4.1396L . free .
  9. Brownstein . John S. . Skelly . David K. . Holford . Theodore R. . Fish . Durland . 19453928 . Forest fragmentation predicts local scale heterogeneity of Lyme disease risk . Oecologia . 27 September 2005 . 146 . 3 . 469–475 . 10.1007/s00442-005-0251-9 . 16187106 . 2005Oecol.146..469B .
  10. Simon . Julie A. . Marrotte . Robby R. . Desrosiers . Nathalie . Fiset . Jessica . Gaitan . Jorge . Gonzalez . Andrew . Koffi . Jules K. . Lapointe . Francois-Joseph . Leighton . Patrick A. . Lindsay . Lindsay R. . Logan . Travis . Milord . Francois . Ogden . Nicholas H. . Rogic . Anita . Roy-Dufresne . Emilie . Suter . Daniel . Tessier . Nathalie . Millien . Virginie . Climate change and habitat fragmentation drive the occurrence of B orrelia burgdorferi, the agent of Lyme disease, at the northeastern limit of its distribution . Evolutionary Applications . August 2014 . 7 . 7 . 750–764 . 10.1111/eva.12165 . 25469157 . 4227856 .
  11. Williams . Nicholas S. G. . Hahs . Amy K. . Vesk . Peter A. . 2015-02-01 . Urbanisation, plant traits and the composition of urban floras . Perspectives in Plant Ecology, Evolution and Systematics . 17 . 1 . 78–86 . 10.1016/j.ppees.2014.10.002 . 1433-8319. 11343/217228 . free .
  12. Keane . Robert E. . Agee . James K. . Fulé . Peter . Keeley . Jon E. . Key . Carl . Kitchen . Stanley G. . Miller . Richard . Schulte . Lisa A. . Ecological effects of large fires on US landscapes: benefit or catastrophe? . International Journal of Wildland Fire . 2008 . 17 . 6 . 696 . 10.1071/WF07148 . 4799766 .
  13. Web site: Stein. Susan M.. Comas. Sara J.. Menakis. James P.. Steward. Susan I.. Cleveland. Helene. Bramwell. Lincoln. Radeloff. Volker. Wildfire, Wildlands, and People: Understanding and Preparing for Wildfire in the Wildland-Urban Interface. 8 May 2018. USDA Forest Service. USDA.
  14. Haas. Jessica R.. Calkin. David E.. Thompson. Matthew P.. 2013. A national approach for integrating wildfire simulation modeling into Wildland Urban Interface risk assessments within the United States. Landscape and Urban Planning. 119. 44–53. 10.1016/j.landurbplan.2013.06.011. 8 May 2018.
  15. Finney . Mark A . Fire growth using minimum travel time methods . Canadian Journal of Forest Research . 1 August 2002 . 32 . 8 . 1420–1424 . 10.1139/x02-068 .
  16. Calkin . David E. . Cohen . Jack D. . Finney . Mark A. . Thompson . Matthew P. . How risk management can prevent future wildfire disasters in the wildland-urban interface . Proceedings of the National Academy of Sciences . 16 December 2013 . 111 . 2 . 746–751 . 10.1073/pnas.1315088111 . 24344292 . 3896199 . 2014PNAS..111..746C . free .
  17. Web site: Frequently Asked Questions – Fire Adapted Communities. July 10, 2014. USDA Forest Service Fire and Aviation Management. https://web.archive.org/web/20171001135211/https://www.fs.fed.us/fire/prev_ed/fac/faqs.pdf. October 1, 2017. June 6, 2018.
  18. Web site: Home Ignition Zone Self Assessment for Homeowners. 2011. Firewise Wisconsin. https://web.archive.org/web/20180430183553/https://dnr.wi.gov/files/pdf/pubs/fr/fr0474.pdf. April 30, 2018. June 6, 2018.
  19. Faith Berry "Firewise property in California survives wildfire, considers next steps to focus on fire adapted approach." NFOA blog, Jul 8, 2013. Assessed 2/3/2019. https://community.nfpa.org/community/fire-break/blog/2013/07/08/firewise-property-in-california-survives-wildfire-considers-next-steps-to-focus-on-fire-adapted-approach
  20. Ballard . Heidi L. . Evans . Emily R. . Wildfire in the Foothills: youth working with communities to adapt to wildfire . 2012 . 10.2737/NRS-RN-160 . free .
  21. Web site: Pacific Gas and Electric Company South of Palermo Reinforcement Project. Cpuc.ca.gov. January 26, 2019.