- Research Article
- Open Access
Measuring the Rate of Spread of Chaparral Prescribed fires in Northern California
Fire Ecology volume 4, pages 74–86 (2008)
Prescribed fire is a common method used to produce desired ecological effects in chaparral by mimicking the natural role of fire. Since prescribed fires are usually conducted in moderate fuel and weather conditions, models that accurately predict fire behavior and effects under these scenarios are important for management. In this study, explosive audio devices and steel stakes were used to record the location of the flaming front during seven prescribed fires in mature, chamise (Adenostoma fasciculatum) dominated chaparral in northern California. Intervals between detonations measured the time required for five fires to transverse a fixed distance and were used to estimate the rate of spread (ROS) during headfire burning conditions. In two other fires, a stopwatch was used to measure the time required to travel successive 5 m distances and ROS was calculated. Burns were completed during moderate weather conditions: average temperature 17 °C, average relative humidity 42 %, and wind light and variable, generally from the west ranging from 0 km h−1 to 8 km h−1. Average percent moisture was 92.1 % for chamise live one-hour fuels, and 8.8 % for chamise dead one-hour fuels. Live fuel height averaged 1.2 m. Overall average ROS was 0.36 m s−1 (range 0.22 m s−1 to 0.56 m s−1) for areas where the flaming front advanced upslope. Measurements of ROS were indeterminable for many of the points along the line transects due to lack of visibility from smoke and from fire not traversing the terrain as predicted. For comparison, BehavePlus fire modeling was performed using five different shrub fuel models (NFFL 4, SH5, SH7, SCAL15, SCAL17); all models underestimated ROS and flame length with NFFL model 4 producing values most similar to that recorded during the prescribed fires. Users of the recently developed fuel models may benefit from field studies to verify model adequacy and fire behavior predictions.
The physical characterization of wildland fuels is important for a variety of uses. The rate at which energy is released determines, in part, the size of flames, the height of the plume above the fire, the rate at which the perimeter of the fire increases, the amount of energy that is absorbed by physical and biological resources in the vicinity of the fire, and the ability of fire management activities to manage fire to accomplish desired objectives. The use of prescribed fire to accomplish resource management objectives has global scope with interest increasing in the last several decades (Pyne et al. 1996, Fernandes and Botelho 2003, Anderson et al. 2005).
Chaparral is a Spanish word used to describe a vegetation complex found primarily in California, extending south into Mexico and east into Arizona (Keeley 2000). The species composition of this vegetation varies throughout its range. Common woody species include chamise (Adenostoma fasciculatum), ceanothus (Ceanothus spp.), manzanita (Arctostaphylos spp.), and shrub oaks (Quercus spp.). Thin bands of riparian vegetation are often interspersed in chaparral along permanent and seasonal watercourses and they provide important wildlife habitat; chaparral is an important bird habitat and species composition is affected by post-fire age gradients of prescribed fires (England 1995; J. Potts, University of California, Berkeley, unpublished data). In southern California, several threatened, endangered, and sensitive plants and animals live in or rely on chaparral, riparian zones, and sage scrub complexes (Stephenson and Calcarone 1999).
In California, chaparral occupies approximately 4 million ha, 10 % of the state’s area (Fried et al. 2004). Five of California’s 10 largest wildfires have occurred in this vegetation type from 1923 to 2005 (Stephens and Sugihara 2006), and prescribed fire is frequently used to manage chaparral. Having accurate information on rates of spread and fireline intensity are important factors when planning prescribed fires. For example, in conducting prescribed fires to manage exotic weed species, if the fire moves too quickly it may not produce the desired ecological effect because of insufficient consumption of the duff layer, surface seeds, and seed heads.
A variety of techniques to manage chaparral have been developed over the past 50 years including grazing, herbicide use, mechanical methods, and prescribed burning (e.g. Green 1981, Conrad et al. 1986, Biswell 1989). Prescribed burning is also used to produce desired ecological effects in chaparral by mimicking the natural role of fire. Applying prescribed fire requires a plan that identifies the desired resource outcomes, the fire behavior necessary to achieve the outcomes, the weather and fuel conditions necessary to achieve desired fire behavior, the control forces necessary to conduct the burning, and a risk analysis. The Rothermel (1972) fire model in its various forms is typically the basis for fire behavior calculations used to develop prescribed fire plans in the United States.
The limitations of the Rothermel model for fire spread prediction in live fuels such as chaparral have been described (Lindenmuth and Davis 1973, Cohen and Bradshaw 1986) even though the model has been used with some success (Stevenson et al. 1974, Albini and Anderson 1982, Dimitrakopoulos and Dritsa 2003). Live fuels differ from dead fuels chemically and physically, and recent work with chaparral fuels has shown the importance of convection to successful fire spread under marginal conditions (Zhou et al. 2005, 2007). Ignition tests of individual chaparral leaves resulted in a wide range in ignition temperatures when a heating rate typical of wildland fires was used (Fletcher et al. 2007), and little to no dependence of time to ignition and ignition temperature on leaf thickness and leaf moisture mass was observed. A physical fire spread model initially developed by Pagni and Peterson (1973) was tested with limited success in chaparral prescribed fires and has been recently modified and compared with laboratory rate of spread experiments by Koo et al. (2005).
Several fuel models have been developed to represent chaparral for use with the Rothermel fire behavior model. The Northern Forest Fire Laboratory (now the Missoula Fire Sciences Laboratory) fuel model 4 (one of the original 13 fire behavior fuel models — Albini 1976) was primarily developed for chaparral fire behavior prediction during the severe period of the fire season (Anderson 1982). More recently, Scott and Burgan (2005) have developed 40 additional fuel models, two of which may improve accuracy of fire behavior predictions outside the severe period of the fire season, such as prescribed fire applications. Several custom fuel models have also been developed for southern California chaparral (Cohen 1986, Weise 1997) and the Fuel Characteristic Classification System contains descriptions of several chaparral fuel beds (Riccardi et al. 2007).
Although there are new research results available describing various aspects of fire behavior in chaparral (e.g., Weise et al. 2005, Sun et al. 2006), they have not been incorporated into operational fire spread models at this time. Comparison of predicted spread rates from the current tools available to fire managers with observed fire spread rates in operational-scale prescribed burns is therefore useful. Procedures to adjust predicted spread rates to observed spread rates exist (Rothermel and Rinehard 1983, Fujioka 2002); however, the information on actual spread rates in chaparral during prescribed fires is very limited. This research project utilized field-scale fire behavior measurements (ROS and flame length) made as part of a larger group of studies examining fire spread in living shrub fuels (Weise et al. 2004) and compared these estimates to predictions calculated from the available shrub fuel models under the same conditions. Information from this study could be used by fire managers to inform plans on the use of prescribed fire in chaparral.
Study Location and Prescribed Fire Characteristics
The chaparral prescribed fires were conducted at the University of California Hopland Research and Extension Center (HREC) and at the adjacent US Bureau of Land Management South Cow Mountain Recreation Area (COW) in Mendocino County, California, USA. Two fires were conducted at HREC in late spring of 1995 and five (four at HREC, one at COW) in the fall and winter of 2006–2007. Burns were conducted in mature chaparral on predominately west-facing slopes (Figure 1). The most common shrub species was chamise. Less common shrub species found in the plots include buckbrush (Ceanothus cuneatus), Eastwood manzanita (Arctostaphylos glandulosa), and leather oak (Quercus durata).
On June 1, 1995, two prescribed fires occurred at HREC (0.25 ha, HREC1; 1 ha, HREC2). One 0.5 ha plot was burned at COW by helitorch on December 1, 2005. At HREC, three plots (1 ha, HREC3; 0.5 ha, HREC4; 0.5 ha, HREC5) were burned on February 4, 2006, and one plot (8 ha, HREC6) was burned on December 2, 2006. Drip torches were used to ignite all prescribed fires with the exception of the one prescribed fire at COW.
Vegetation and Fire Behavior Measurements
In the 1995 burn plots, one transect of steel stakes was installed with 5 m separating each stake. Transects were placed perpendicular to the slope contour in the two burn plots in an effort to ocularly record rate of spread (ROS) during headfire burning conditions using a stopwatch to time intervals between stakes. In the 2006 plots, one to four line transects of varying lengths were installed in each burn plot before burning. At 5 m to 10 m intervals along each transect, explosive audio devices (Piccolo PeteFootnote 1 fireworks or shotgun shell primers) were attached to a branch in the live crown and as the fire passed each interval, detonations were recorded with audio and video tape recorders. Time between successive detonations was used as a measure of ROSFootnote 2.
Transect starting points were placed midslope such that the entire transect was in view of observers to confirm detonations or the time when the headfire reached the next steel stake (for 1995 fires). At each interval in the 2006 fires, slope, aspect, maximum live shrub height, and percent cover of all shrub species in a 5 m radius circular plot were measured. In the 1995 fires, the maximum live shrub height and percent cover of all shrub species were measured along the line-transect created by the steel stakes.
Prior to each burn, soil and fuel samples were collected to estimate percent moisture content. Samples were immediately stored in metal 10 cm diameter soil sampling cans and dried in the laboratory for 24 h at 95 °C. Sample weight before and after drying was measured using an electronic scale with a precision of ±0.01 g. Percent moisture content was calculated on a dry weight basis. Soil was collected at two depths: 0 cm to 3 cm, and 3 cm to 6 cm below the surface. Live and dead 1 h time lag fuels were collected for shrubs with overstory cover greater than 10 %.
Prescribed fires were ignited by hand crews using drip torches (one helitorch ignition) in a headfire configuration (Martin and Dell 1978) to produce relatively uniform fire behavior. Flame lengths were estimated ocularly during the fire and these estimates were reviewed by watching a video taken of the prescribed fires.
Fire Behavior Modeling
Behave Plus (Anderson et al. 2005) was used to model fire behavior of the chaparral prescribed fires. In addition to NFFL model 4, SH5 and SH7 shrub fuel models (Scott and Burgan 2005) and chaparral models developed for southern California (SCAL15 and SCAL17; Weise 1997) were used in simulations to determine which would more closely match field results.
Input variables included surface fuel model, fuel moistures, slope, and wind speed. All of these variables were measured in the field with the exception of complete data for 10 h and 100 h dead fuel moisture. Measurements indicated that 10 h fuel moisture was approximately 1 % greater than 1 h fuel moisture, and 100 h was 2 % greater. Assuming that 10 h fuel moisture was 0.5 % greater than 1 h and 100 h moisture was 1 % higher resulted in only a 1 % difference in ROS estimates and almost no change in flame length estimates. To compare with field measurements, model prediction variables were calculated for the average, maximum, and minimum wind speeds recorded during the prescribed fires.
Vegetation and Weather
Most of the transects were installed in nearly pure chamise but a few shrubs of other associated species were found in some plots (Table 1). The height of the chaparral was similar between plots.
For most of the plots, weather conditions were ideal for prescribed burning. Weather for the COW burn plot was influenced by an approaching storm, which resulted in cloudy skies and high relative humidity compared to burning conditions at the other plots (Table 2).
Soil and Fuel Moisture
Live fuel moisture contents for the late-spring 1995 prescribed fires (over 130 %) were much higher than those for the 2006 burns. Percent moisture content of the soils and dead fuels reflect the relatively dry conditions under which most burning was conducted. For the December 2006 burns at HREC, 10 h fuel moisture was reported to be 9 % (R.J. Keiffer, University of California, Berkeley, unpublished data). Table 3 summarizes soil and fuel conditions prior to each burn.
Fire Behavior Observations
As expected, plots burned under varying weather conditions that influenced ROS; Table 4 summarizes fire behavior observations. Overall average ROS was 0.36 m s−1 (range 0.22 m s−1 to 0.56 m s−1) for areas where the flaming front advanced upslope. Flame lengths were higher in the HREC2 plot relative to HREC1 because of higher air temperature and lower humidity during burning. Cloud cover was also lower during the HREC2 fire (60 % versus 100 % for HREC1), which could increase fuel temperature and increase flame lengths through increased burning rates and larger flame depths.
Overcast skies and high relative humidity during the COW burn prohibited attempts to ignite with drip torches; therefore, a helitorch was used to apply fire to the entire perimeter of the plot. As a result, the fire moved unpredictably through most of the transect, obviating any upslope ROS measurements. Despite this burning pattern, ROS measurements for this plot were the highest compared to ROS measurements from all other plots, probably because of a ring-fire ignition pattern that was used to ignite the entire unit at once.
For HREC3, view was obscured by a bench in the slope contour, but the flaming front appeared to progress upslope through the transect. A flanking fire burned through HREC4 and therefore ROS was not recorded. Both transects in HREC5 were burned by headfire conditions. However, fire behavior was influenced by wind blowing across the slope and may have increased ROS. For HREC6, the first transect was successful at recording upslope ROS; all other transects within this plot were burned by flanking fire.
Fire Behavior Modeling
NFFL model 4 consistently produced the highest fire behavior predictions and the largest changes in response to varying wind speeds (Figure 2). With the exception of HREC3, ROS and flame length predictions ranged from 26 % to 85 % of field measurements for the range of wind speeds recorded during the prescribed fires. NFFL model 4 and SH5 over-predicted ROS at the maximum wind speed for the HREC3 fire. SH5 and SH7 produced ROS estimates that were approximately 50 % to 75 % of that produced by NFFL model 4. SCAL15 and SCAL17 produced the lowest ROS estimates, averaging 12 % to 25 % of those produced by NFFL model 4. Flame length estimates from the different fuel models produced a similar pattern with NFFL model 4, producing the largest estimates, ranging from 27 % to 73 % of field measurements for the range of wind speeds recorded during the prescribed fires (Figure 2).
By manipulating prescription variables to produce desired fire intensities (Raybould and Roberts 1981), prescribed burning can be used to increase edge within chaparral for wildlife habitat and to increase biodiversity by creating openings for regeneration (Stephenson and Calcarone 1999). Low intensity prescribed burns in chaparral can be used to reduce fire hazard to riparian habitats; however, resultant hillslope erosion and dry ravel may impact riparian zones (Dougherty and Riggan 1981, Barro et al 1989). In contrast, high intensity wildfires often cause extensive changes to chaparral landscapes, which then gradually recover following various successional pathways (Keeley 2000). The ability to more accurately model fire behavior in chaparral can assist in developing prescriptions to meet ecological and management objectives.
Above ground chaparral structure was significantly changed by our prescribed fires as is common in most prescribed fires in this vegetation type. Before the fire, 87 % to 100 % of the seven burn plots were covered by shrubs (Table 1). The relatively dry conditions under which these burns were conducted is reflected in the soil and dead fuel moisture contents, yet the seasonal differences are also reflected in the live fuel moisture contents (Table 2). Despite these variable conditions, after the fire, no live branches were found in the plots or transects, indicating uniform fire coverage. Shrub skeletons did exist in the plots after the fires but were composed of materials with larger diameters.
Implementation of the methods used in this study is relatively easy and can be completed on the day of the burn. Conversely, it is extremely difficult to predict with certainty where the fire will spread and, therefore, precisely where to place the explosive devices or steel stakes. For more than half of the transects installed, the flaming front did not traverse the transects as predicted even though ignitions were specifically applied to produce headfires. While the explosive devices aided in recording ROS when the direct view of the transect was obstructed, tall vegetation or smoke can interfere with observations. Furthermore, the variability in heat required to detonate the explosive devices is unknown and this may add to the variability in ROS estimations. The limitations with these methods are similar to those reviewed in Simard et al. (1982); however, the triangle method, recording the time required for a fire to traverse an equilateral triangle (Simard et al. 1982, Moore et al. 1995), solves the problem of having to predict the precise direction of the flaming front.
When a fire burned through a transect as predicted, the methods used in this study appear to estimate ROS without bias. An inevitable difficulty is accounting for factors such as wind that affect fire behavior in experimental fires (Gould et al. 2007) (Figure 1). During some of these prescribed fires, we observed flaming fronts advancing periodically through the vegetation in narrow strips. These leading strips usually occurred at focal points along the ignition line where fire was applied directly to the shrub canopy. Installing transects at predetermined focal points may increase the likelihood of collecting accurate one-dimensional ROS measurements.
Using Behave Plus (Andrews et al. 2005), fire behavior model results for the five different surface fuel models (NFFL 4, SH5, SH7, SCAL15, SCAL17) were quite diverse. NFFL fuel model 4 produced results that were the most similar to those recorded from the field even though this model was intended for severe fire weather conditions. Incorporating the range in wind speeds recorded during the prescribed fires into the models, the range of ROS predictions overlapped the averages observed in four of the six prescribed fires (Figure 2). This demonstrates the sensitivity of the live shrub fuel models to varying wind speeds (Weise et al. 2005) and the importance of accurately recording wind in the field (Gould et al. 2007).
Fuel models SH5 and SH7 produced fire behavior outputs that were 8 % to 70 % of what was observed in the field. This was a bit surprising because these fuel models were developed to more closely simulate prescribed fire conditions when weather tends to be more moderate. Custom fuel models developed for southern California chamise-dominated chaparral produced the lowest ROS estimates, approximately 10 % of what was observed in the field, and do not seem appropriate for northern California chaparral in our study sites.
Flame length estimates from BehavePlus using the five surface models were much lower than what was observed in the field. Comparisons of field measurements and model predictions should be interpreted with caution because observed flame lengths are likely to be overestimated. Flames are inherently unstable and it is difficult to make estimates of their lengths in chaparral fires without reference points. Yet it should be noted that modeled flame lengths were approximately 50 % of observed estimates using NFFL 4 and were much lower in the other fuel models. The HREC5 fire had relatively large ROS and flame lengths when compared to all model outputs (Figure 2). As with most burns, variable wind direction and speed (Table 1 and 2) coupled with ignition patterns may influence fire behavior by preheating fuels, which confounds estimates. Higher wind speeds could have resulted in the larger ROS and flame length estimates for this prescribed fire.
Prescribed fire is one method that can produce desired ecological effects in chaparral by mimicking the natural role of fire. Fire managers utilize available tools such as fuel models to aid in the development of management plans. Because prescribed fires are usually conducted in moderate fuel and weather conditions, models that accurately predict fire behavior and effects under these scenarios are therefore important for management. Experimental fires that measure fire behavior both in the field and in the laboratory are needed to test models and provide information that can be incorporated into future models to improve accuracy.
Current fuel models designed to estimate fire behavior of a flaming front are difficult to verify with field measurements. Uncontrollable factors that exist during experimental fire in the field such as variable wind patterns (Gould et al. 2007), topography, and vegetation structure inevitably confound field observations. Measurements of fire behavior (ROS and flame length) from chaparral prescribed fires in northern California were much higher than predictions using the current fuel models. Discrepancies in estimates are due to both the difficulties in estimating fire behavior and inadequacy of the models to predict the dynamic nature of fire in live fuels. Information from this study could be used by fire managers to inform plans on the use of prescribed fire in chaparral.
Trade names are provided for informational purposes only and do not constitute endorsement by the US Department of Agriculture.
A video demonstration of ROS measurements using audio explosive devices in a chaparral prescribed fire is available at http://www.cnr.berkeley.edu/stephens-lab/links.htm.
Albini, F.A. 1976. Estimating wildfire behavior and effects. USDA Forest Service General Technical Report INT-30.
Albini, F.A., and E.B. Anderson. 1982. Predicting fire behavior in US Mediterranean ecosystems. Pages 483–489 in: C.E. Conrad, and W.C. Oechel, technical coordinators. Proceedings of the symposium on dynamics and management of Mediterranean-type ecosystems. USDA Forest Service General Technical Report. PSW-58.
Anderson, H.E. 1982. Aids to determining fuel models for estimating fire behavior. USDA Forest Service General Technical Report INT-GTR-122.
Anderson, A.L., G.D. Cook, L.K. Corbett, M.M. Douglas, R.W. Eager, J.R. Russell-Smith, S.A. Setterfield, R.W. Williams, and J.Z. Woinarski. 2005. Fire frequency and biodiversity conservation in Australian tropical savannas: implications from the Kapalga fire experiment. Austral Ecology 30: 155–167.
Andrews, P.L., C.D. Bevins, and R.C. Seli. 2005. BehavePlus fire modeling system version 3.0 user’s guide. USDA Forest Service General Technical Report RMRS-GTR-106.
Barro, S.C., P.M. Wohlgemuth, and A.G. Campbell. 1989. Post-fire interactions between riparian vegetation and channel morphology and the implications for stream channel rehabilitation choices. Pages 51–53 in: D.L. Abell, technical coordinator. Proceedings of the California Riparian Systems Conference: protection, management, and restoration for the 1990s. USDA Forest Service General Technical Report PSW-110.
Biswell, H.H. 1989. Prescribed burning in California wildland vegetation management. University of California Press, Berkeley, USA.
Cohen, J.D. 1986. Estimating fire behavior with FIRECAST: user’s manual. USDA Forest Service General Technical Report PSW-90.
Cohen, J.D., and B. Bradshaw. 1986. Fire behavior modeling-a decision tool. Pages 1–5 in: A.L. Koonce, editor. Prescribed burning in the midwest: state-of-the-art. Proceedings of a symposium. University of Wisconsin, Stevens Point, 3–6 March 1986.
Conrad, C.E., G.A. Boby, and S.C. Hunter. 1986. Chaparral and associated ecosystems management: a 5 year research and development program. USDA Forest Service General Technical Report PSW-91.
Dimitrakopoulos A.P., and S. Dritsa. 2003. Novel nomographs for fire behavior prediction in Mediterranean and sub Mediterranean vegetation types. Forestry 76(5): 479–490.
Dougherty, R., and P.J. Riggan. 1982. Operational use of prescribed fire in southern California chaparral. Pages 502–510 in: C.E. Conrad, and W.C. Oechel, technical editors. Proceedings of the symposium on dynamics and management of Mediterranean-type ecosystems. USDS Forest Service General Technical Report PSW-58.
England, A.S. 1995. Avian community organization along a post-fire gradient in California chaparral. Dissertation, University of California, Davis, USA.
Fernandes, P.M., and H.S. Botelho. 2003. A review of prescribed burning effectiveness in fire hazard reduction. International Journal of Wildland Fire 12: 117–128.
Fletcher, T.H., B.M. Pickett, S.G. Smith, G.S. Spittle, M.G. Woodhouse, E. Haake, and D.R. Weise. 2007. Effects of moisture on ignition behavior of moist California chaparral and Utah leaves. Combustion Science and Technology 179(6): 1183–1203.
Fried, J.S., C.L. Bolsinger, and D. Beardsley. 2004. Chaparral in southern and central coastal California in the mid-1990s: area, ownership, condition, and change. USDA Forest Service Research Bulletin PNW-RB-240.
Fujioka, F.M. 2002. A new method for the analysis of fire spread modeling errors. International Journal of Wildland Fire 11: 193–203.
Green, L.R. 1981. Burning by prescription in chaparral. USDA Forest Service General Technical Report PSW-51.
Gould, J.S., W.L. McCaw, N.P. Cheney, P.F. Ellis, I.K. Knight, and A.L. Sullivan. 2007. Project Vesta—fire in dry eucalyptus forest: fuel structure, fuel dynamics, and fire behavior. Ensis-CSIRO, Canberra, Australian Capital Territory, and Department of Environment and Conservation, Perth, Western Australia, Australia.
Keeley, J. 2000. Chaparral. Pages 203–254 in: M.G. Barbour, and W.D. Billing, editors. North American terrestrial vegetation. Second edition. Cambridge University Press, United Kingdom.
Koo, E., P. Pagni, J. Woycheese, S. Stephens, D. Weise, and J. Huff. 2005. A simple physical model for forest fire spread. Pages 851–862 in: D.T. Gottuk, and B.Y. Lattimer, editors. Fire safety science-Proceedings of 8th International Symposium, International Association of Fire Safety Science, 18–23 September 2005. Tsinghua University, Beijing, China.
Lindenmuth, A.W., and J.R. Davis. 1973. Predicting fire spread in Arizona’s oak chaparral. USDA Forest Service Research Paper RM-101.
Martin, R.E., and J.D. Dell. 1978. Planning for prescribed burning in the inland northwest. USDA Forest Service General Technical Report PNW-76.
Moore, P.H.R., A.M. Gill, and R. Kohnert. 1995. Quantifying bushfires for ecology using two electronic devices and biological indicators. CALM Science Supplement 4: 83–88.
Pagni, P.J., and T.G. Peterson. 1973. Flame spread through porous fuels. Pages 1099–1107 in: Proceedings of the Fourteenth Symposium (International) on Combustion. The Combustion Institute, 20–25 August 1972, Pennsylvania State University, University Park, USA.
Pyne, S.J., P.L. Andrews, and R.D. Laven. 1996. Introduction to wildland fire. Second edition. John Wiley and Sons, New York, New York, USA.
Raybould, S., and T. Roberts. 1983. A matrix approach to fire prescription writing. Fire Management Notes 44: 7–10.
Riccardi, C.L., R.D. Ottmar, D.V. Sandberg, A. Andreu, E. Elman, K. Kopper, and J. Long. 2007. The fuelbed: a key element of the fuel characteristic classification system. Canadian Journal of Forest Research. 37(12): 2394–2412.
Rothermel, R.C. 1972. A mathematical model for predicting fire spread in wild land fuels. USDA Forest Service Research Paper INT-RP-115.
Rothermel, R.C., and C.W. Philpot. 1973. Predicting changes in chaparral flammability. Journal of Forestry 71: 640–643.
Rothermel, R.C., and G.C. Rinehard. 1983. Field procedures for verification and adjustment of the fire behavior predictions. USDA Forest Service General Technical Report INT-142.
Scott, J.H., and R.E. Burgon. 2005. Standard fire behavior fuel models: a comprehensive set for use with Rothermel’s surface fire spread model. USDA Forest Service General Technical Report RMRS-GTR-153.
Simard, A.J., A.G. Deacon, and K.B. Adams. 1982. Nondirectional sampling of wildland fire spread. Fire Technology. 18: 221–228.
Stephens, S.L., and N.G. Sugihara. 2006. Fire management and policy since European settlement. Pages 431–443 in: N.G. Sugihara, J.W. van Wagtendonk, J. Fites-Kaufman, K.E. Shaffer, and A.E. Thode, editors. Fire in California’s Ecosystems. University of California Press, Berkeley, USA.
Stephenson, J.R., and G.M. Calcarone. 1999. Southern California mountains and foothills assessment: habitat and species conservation issues. USDA Forest Service General Technical Report GTR-PSW-175.
Stevenson, A.E., D.A. Schermerhorn, and S.C. Miller. 1974. Simulation of southern California forest fires. Pages 147–155 in: Proceedings of the Fifteenth Symposium (International) on Combustion. The Combustion Institute, 25–31 August 1974, The Toshi Center Hall, Tokyo, Japan.
Sun, L., X. Zhou, S. Mahalingam, and D.R. Weise. 2006. Comparison of burning characteristics of live and dead chaparral fuels. Combustion and Flame 144: 349–359.
Weise, D.R. 1997. Resource management: the fire element. Newsletter of the California Fuels Committee). Summer 1997: Pages 4–5. Available at <http://www.firemodels.org/downloads/behaveplus/publications/Weise_1997_ResourceManagement_SprSum.pdf>. Accessed 10 March. 2008.
Weise, D., T. Fletcher, L. Baxter, S. Mahalingam, X. Zhou, P. Pagni, R. Linn, and B. Butler. 2004. A fundamental look at fire spread in California chaparral. Pages 186–193 in: Proceedings of 11th Annual AFAC Conference and Inaugural Bushfire CRC Conference, 7–9 October 2004, Perth, Western Australia, Australia.
Weise, D.R., Z. Xiangyang, S. Lulu, and S. Mahalingam. 2005. Fire spread in chaparral — “go or no-go?” International Journal of Wildland Fire 14: 99–106.
Zhou, X., D.R. Weise, and S. Mahalingam. 2005. Experimental measurements and numerical modeling of marginal burning in live chaparral fuel beds. Pages 2287–2294 in: Proceedings of the 30th International Symposium on Combustion. The Combustion Institute, 25–30 July 2004, University of Chicago, Illinois, USA.
Zhou, X., S. Mahalingam, and D.R. Weise. 2007. Experimental study and large eddy simulation of effect of terrain slope on marginal burning in shrub fuel beds. Pages 2547–2255 in: Proceedings of the 31st International Symposium on Combustion. The Combustion Institute, 6–11 August 2006, University of Heidelburg, Germany.
The senior author thanks Bob Lane for introducing him to the UC Hopland Research and Extension Center and for being part of the 1995 prescribed fires. We thank the dedication and support of Mark Tolbert and the CalFire engine, ground, and helicopter crews. Funding for this research was supplied by the US Joint Fire Sciences Program and US National Fire Plan. We thank Steve Quarles, all summer technicians and staff at the BLM Ukiah Field Office, and Hopland Research and Extension Center for their assistance. This manuscript was prepared, in part, by a U.S. Government employee on official time and is therefore in the public domain and not subject to copyright laws.
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Stephens, S.L., Weise, D.R., Fry, D.L. et al. Measuring the Rate of Spread of Chaparral Prescribed fires in Northern California. fire ecol 4, 74–86 (2008). https://doi.org/10.4996/fireecology.0401074
- fire behavior
- flame length
- fuel model
- fuel moisture
- live fuel
- shrub cover