International Journal of Wildland Fire International Journal of Wildland Fire Society
Journal of the International Association of Wildland Fire
RESEARCH ARTICLE

Fire spread in chaparral—‘go or no-go?’

David R. Weise A C , Xiangyang Zhou B , Lulu Sun B and Shankar Mahalingam B
+ Author Affliations
- Author Affliations

A Forest Fire Laboratory, Pacific Southwest Research Station, USDA Forest Service, 4955 Canyon Crest Drive, Riverside, CA 92507, USA.

B Department of Mechanical Engineering, University of California-Riverside, Riverside, CA 92521, USA. Telephone: +1 951 787 2134; email: xzhou@engr.ucr.edu; lsun@engr.ucr.edu; shankar.mahalingam@ucr.edu

C Corresponding author. Telephone: +1 951 680 1500; fax: +1 951 680 1501; email: dweise@fs.fed.us

International Journal of Wildland Fire 14(1) 99-106 https://doi.org/10.1071/WF04049
Submitted: 24 August 2004  Accepted: 11 October 2004   Published: 7 March 2005

Abstract

Current fire models are designed to model the spread of a linear fire front in dead, small-diameter fuels. Fires in predominantly living vegetation account for a large proportion of annual burned area in the United States. Prescribed burning is used to manage living fuels; however, prescribed burning is currently conducted under conditions that result in marginal burning. We do not understand quantitatively the relative importance of the fuel and environmental variables that determine spread in live vegetation. To address these weaknesses, laboratory fires have been burned to determine the effects of wind, slope, moisture content and fuel characteristics on fire spread in fuel beds of common chaparral species. Four species (Adenostoma fasciculatum, Ceanothus crassifolius, Quercus berberidifolia, Arctostaphylos parryana), two wind velocities (0 and 2 m s-1) and two fuel bed depths (20 and 40 cm) were used. Oven-dry moisture content of fine fuels (<0.63 cm diameter) ranged from 0.09 to 1.06. Seventy of 125 fires successfully propagated the length (2.0 m) of the elevated fuel bed. A logistic model to predict the probability of successful fire spread was developed using stepwise logistic regression. The variables selected to predict propagation were wind velocity, slope percent, moisture content, fuel loading, species and air temperature. Air temperature and species terms were removed from the model for parsimony. The final model correctly classified 94% of the observations. Comparison of results with an empirical decision matrix for prescribed burning in chaparral suggested some agreement between the laboratory data and the empirical tool.


References


Albini FA (1967) A physical model for firespread in brush. In ‘Eleventh symposium (international) on combustion’. pp. 553–560. (The Combustion Institute: Pittsburgh, PA)

Albini FA, Anderson EB (1982) Predicting fire behavior in U.S. mediterranean ecosystems. In ‘Proceedings of the symposium on dynamics and management of mediterranean-type ecosystems’. pp. 483–489. USDA Forest Service General Technical Report PSW-58. (Berkeley, CA)

Albini FA , Stocks BJ (1986) Predicted and observed rates of spread of crown fires in immature jack pine. Combustion Science and Technology  48, 65–76.


Andrews PL (1986) ‘BEHAVE fire behavior prediction and fuel modeling system–BURN subsystem, Part 1.’ USDA Forest Service, Intermountain Research Station General Technical Report INT-194. (Ogden, UT)

Bilgili E , Saglam B (2003) Fire behavior in maquis fuels in Turkey. Forest Ecology and Management  184, 201–207.
CrossRef |

Bruner AD, Klebenow DA (1979) ‘Predicting success of prescribed fires in pinyon-juniper woodland in Nevada.’ USDA Forest Service Research Paper INT-219. (Ogden, UT)

Butler BW, Finney MA, Andrews PL , Albini FA (2004) A radiation-driven model for crown fire spread. Canadian Journal of Forest Research  34, 1588–1599.
CrossRef |

Campbell D (1995) ‘The Campbell Prediction System.’ (Wildland Fire Specialists: Ojai, CA)

Campbell W (2004) ‘Governor’s Blue Ribbon Fire Commission: Report to the Governor.’ http://www.oes.ca.gov/Operational/OESHome.nsf/PDF/BlueRibbonReporttoGov/$file/BlueRibbonRept.pdf [Verified 1 February 2005]

Catchpole WR, Catchpole EA, Butler BW, Rothermel RC, Morris GA , Latham DJ (1998) Rate of spread of free-burning fires in woody fuels in a wind tunnel. Combustion Science and Technology  131, 1–37.


Cohen JD (1986) ‘Estimating fire behavior with FIRECAST: user’s manual.’ USDA Forest Service General Technical Report PSW-90. (Berkeley, CA)

Countryman CM (1964) ‘Mass fires and fire behavior.’ USDA Forest Service Research Paper PSW-19. (Berkeley, CA)

Countryman CM (1982) ‘Physical characteristics of some northern California brush fuels.’ USDA Forest Service General Technical Report PSW-61. (Berkeley, CA)

Countryman CM, Philpot CW (1970) ‘Physical characteristics of chamise as a wildland fuel.’ USDA Forest Service Research Paper PSW-66. (Berkeley, CA)

De Luis M, Baeza MJ, Raventos J , Gonzalez-Hildalgo JC (2004) Fuel characteristics and fire behavior in mature Mediterranean gorse shrublands. International Journal of Wildland Fire  13, 79–87.
CrossRef |

Engstrom JD, Butler JK, Baxter LL, Fletcher TH , Weise DR (2004) Ignition behavior of live California chaparral leaves. Combustion Science and Technology  176, 1577–1591.
CrossRef |

Fernandes PAM (2001) Fire spread prediction in shrub fuels in Portugal. Forest Ecology and Management  144, 67–74.
CrossRef |

Green LR (1981) ‘Burning by prescription in chaparral.’ USDA Forest Service General Technical Report PSW-51. (Berkeley, CA)

Hilbruner MW (1988) Fire behavior modeling for burn prescription specification. MS thesis, Department of Forest and Wood Sciences, Colorado State University, Fort Collins, CO, USA.

Hosmer DW, Lemeshow S (2000) ‘Applied logistic regression.’ 2nd edn. (Wiley Interscience: New York)

Marsden-Smedley JB, Catchpole WR , Pyrke A (2001) Fire modeling in Tasmanian buttongrass moorlands. IV. Sustaining versus nonsustaining fires. International Journal of Wildland Fire  10, 255–262.
CrossRef |

McCaw WL (1997) Predicting fire spread in Western Australian mallee-heath shrubland. PhD thesis. University of New South Wales, Canberra.

Morvan D , Dupuy JL (2004) Modeling the propagation of a wildfire through a Mediterranean shrub using a multiphase formulation. Combustion and Flame  138, 199–210.
CrossRef |

Ottmar RD, Vihnanek RE, Regelbrugge JC (2000) ‘Stereo photo series for quantifying natural fuels—Vol. IV: pinyon-juniper, chaparral, and sagebrush types in the southwestern United States.’ (National Wildfire Coordinating Group, National Interagency Fire Center #PMS 833: Boise, ID)

Raybould S , Roberts T (1983) A matrix approach to fire prescription writing. USDA Forest Service Fire Management Notes  44, 7–10.


Rothermel RC (1972) ‘A mathematical model for predicting fire spread in wildland fuels.’ USDA Forest Service Research Paper INT-115. (Ogden, UT)

Rothermel RC , Philpot CW (1973) Predicting changes in chaparral flammability. Journal of Forestry  71, 640–643.


Weise DR , Biging GS (1997) A qualitative comparison of fire spread models incorporating wind and slope effects. Forest Science  43, 170–180.


Wilson RAJr (1982) ‘A reexamination of fire spread in free burning porous fuel beds.’ USDA Forest Service Research Paper INT-289. (Ogden, UT)

Wilson RA (1985) Observations of extinction and marginal burning states in free burning porous fuel beds. Combustion Science and Technology  44, 179–194.


Wilson RAJr (1990) ‘Reexamination of Rothermel’s fire spread equations in no-wind and no-slope conditions.’ USDA Forest Service Research Paper INT-434. (Ogden, UT)

Zhou X, Weise D , Mahalingam S (2005) Experimental measurements and numerical modeling of marginal burning in live chaparral fuel beds. Proceedings of the Combustion Institute  30, 2287–2294.




* The use of trade names and model numbers is for information purposes only and does not constitute endorsement by the U.S. Department of Agriculture.


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