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Influence of crown architecture on prediction of canopy fuel loads and fire hazard in ponderosa pine forests of the Blac

Influence of crown architecture on prediction of canopy fuel loads and fire hazard in ponderosa pine forests of the Black Hills. Tara L. Keyser, Research Forester, USDA Forest Service, Southern Research Station Frederick (Skip) W. Smith, Professor of silviculture, Colorado State University.

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Influence of crown architecture on prediction of canopy fuel loads and fire hazard in ponderosa pine forests of the Blac

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  1. Influence of crown architecture on prediction of canopy fuel loads and fire hazard in ponderosa pine forests of the Black Hills Tara L. Keyser, Research Forester, USDA Forest Service, Southern Research Station Frederick (Skip) W. Smith, Professor of silviculture, Colorado State University

  2. Forests of the Black Hills • Aspen, lodgepole pine, burr oak, green ash, white spruce, paper birch, open meadows • 85% ponderosa pine

  3. Forest management in the Hills

  4. Mountain Pine Beetle Increasing WUI Increase in large-scale wildfires ~82,500 ha have burned since 2000 in just 21 fire events Jasper Fire ~34,000 ha Current forest management issues

  5. Goal – create structures resistant to the initiation & spread of crown fire Reduce surface fuels Reduce vertical & horizontal continuity of canopy fuels Fuel reduction treatments

  6. Passive crown fire

  7. Active crown fire

  8. Alter canopy fuel structure • Increase Canopy Base Height (CBH) • The lowest height at which there is a sufficient amount of canopy fuel to spread fire into the canopy (Van Wagner 1993) • Reduces the risk of passive crown fire (torching) • Decrease Canopy Bulk Density (CBD) • The density (kg/m3) of foliage and small branches within a stand • CBD values is used to make inferences about the continuity of canopy fuels • Reduces the risk of active crown fire

  9. Estimating CBH and CBD • CBD & CBH are not directly measured • Stand-level variables predicted from fire behavior/effects and forest growth models using standard forest inventory data • One of the more widely used models is the Fire and Fuels Extension to the Forest Vegetation Simulator (FFE-FVS)

  10. CBH and CBD in FFE-FVS • Obtaining CBH & CBD values requires an estimate of crown mass (foliage mass + 0.5*1hr branch mass) of individual trees ≥1.8 m in heightwithin a stand • In FFE-FVS, allometric equations used to predict crown mass for ponderosa pine are based on data from Montana and Idaho (Brown 1978)

  11. Effective CBD (Reinhardt and Crookston 2003) • A canopy fuel profile is created using the aggregated weight of crown fuel within 0.3-m sections of the canopy • A 4-m running average of CBD (kg/m3) around those 0.3-m sections is calculated canopy base height = 0.011 canopy bulk density = MAX Figure from Reinhardt and Crookston (2003)

  12. Distribution of crown mass in FFE-FVS • An important underlying assumption used in the prediction of CBH & CBD is that crown mass is equallydistributed throughout the crown

  13. Distribution of crown mass in the real world

  14. Objectives • Create crown mass equations for ponderosa pine specific to the Black Hills • Describe and predict the vertical distribution of crown mass • Examine the effect Black Hills crown mass equations + distribution models have on estimates of CBD and CBH

  15. Inventory • June - August of 2006, 16 stands were located throughout the BHNF. • One vegetation plot randomly established in each stand. • Each plot was inventoried: Species, DBH, total height, height to the base of the live crown (BLC) recorded for all trees ≥1.8 m tall. • Within each of the 16 stands/plots, 5 trees were selected for destructive sampling.

  16. Stand attributes Note: SDImax = 1112

  17. Destructive sampling

  18. For each section, crown was separated into: • Foliage + 1 hr (<-.6 cm) fuels • 10 hr fuels (≥0.6 x <2.54 cm) • 100 hr fuels (≥2.54 x <7.6 cm) • 1000 hr fuels (≥7.62 cm)

  19. Statistical analyses • Nonlinear regression used to develop allometric equations based on individual tree attributes for total dry mass of live foliage & live 1 hour fuels • Y = b0X1b1X2b2 + ε • The Weibull distribution was used to model the distribution of total crown fuel mass of individual trees • Crown fuel mass = 1 – exp[-(X/β)α] • X = section of crown • β = scale parameter • α = shape parameter • Linear regression used to develop a system of models to predict the scale (β) & shape (α) parameters of individual trees based on individual tree and/or stand-level attributes

  20. Foliage mass • FOL = 0.0865DBH1.8916 LCR1.1358 • R2 = 0.89 • Black Hills equations predicted, on average, 25% greater foliage mass than Brown (1978)

  21. R2 = 0.76 1 hr fuel mass • 1HF = 1.5439 LCR5.6131 • Black Hills equations predicted, on average, 90% less 1 hr mass than Brown (1978)

  22. Distribution of crown fuel within individual trees • Weibull distribution statistics • Scale parameter (β) : • 4.4 - 7.9 • Shape (α) parameter: • 1.4 - <3.6

  23. Parameter prediction • β = 7.1386 - 0.0608(HT) • HT = Tree height • R2 = 0.51 • α = 3.3126 - 0.0214(HT) - 1.1622(RD) • RD = Relative density (SDIobs/SDImax) • R2 = 0.71

  24. Impact on CBH estimates

  25. Impact on CBD estimates (kg/m3)

  26. Fire hazard • Fire hazard indices (torching and crowning index) & fire type was assessed using NEXUS 2.0 • 97% weather conditions • Probable maximum momentary gust (53 km/hr) • Fuel model 5 (shrub fuel model)

  27. Torching Index • Torching index (TI) = 6.1 m open windspeed at which fire is carried from the surface into the crown • Function of: surface fuel loading and moisture content, foliar moisture content, wind reduction by the canopy, slope, and CBH(Scott and Reinhardt 2001) • Lower TIs = increased susceptibility to passive crown fire

  28. Impact of modified CBH on TI

  29. Crowning Index • Crowning index (CI) = 6.1 m open windspeed at which active crown fire can occur • Function of: surface fuel moisture content, slope, and CBD(Scott and Reinhardt 2001) • Lower CIs = increased susceptibility to active crown fire

  30. Impact of modified CBD on CI

  31. Potential fire behavior

  32. Conclusions • Crown mass equations for ponderosa pine in the Black Hills resulted in substantially different crown mass estimates than produced by Brown (1978): • Underestimated foliage mass by an average of 25% • Overestimated 1 hr fuel mass by an average of 90%

  33. Conclusions (cont.) • Using a allometric equations developed for ponderosa pine in the Hills + a non-uniform distribution of crown fuel mass resulted in: • Similar estimates of CBH • An average 67% increase in CBD over original methods • Increase ranged from +20 to +140%

  34. Conclusions (cont.) • Using a threshold of 0.1 kg/m3 for CBD, FVS misidentified high hazard structures • Original CBD values resulted in only 2 of the 16 stands possessing a CBD >0.1 kg/m3 threshold • Modified CBD values resulted in an additional 10 stands possessing a CBD >0.1 kg/m3 threshold

  35. Conclusions (cont.) • Modified estimates of CBH had little impact on TI • Modified estimates of CBD resulted in a lowering of CI for 15 of the 16 stands • Modified estimates of CBH and CBD resulted in potential fire type changing from passive to active crown fire in 8 of the 16 stands

  36. Implications • Underestimating CBD and fire hazard indices may result in the misidentification of stands in need of treatment • Underestimating CBD could create situations where fuels treatments do not reduce CBD below the critical thresholds required to minimize crown fire hazard

  37. Recommendations • Widespread use of tree mass allometries be verified for different tree species and development of local equations be undertaken where substantial differences in crown fuel mass estimates occur • A non-uniform distribution of crown mass be used when aggregating tree crown mass to identify the position and amount of canopy mass to calculate CBD as used in fire prediction models

  38. Actions taken • Incorporation of new biomass estimates and vertical distribution models for ponderosa pine in the Black Hills into FVS is complete (waiting for distribution/release of update) • New JFSP funded project implementing similar research for other fire-prone tree species in the Interior West (Doug-fir, lodgepole pine, spruce/fir, P-J) • Results from study are published in: • Keyser and Smith (2010) – Forest Science • JFSP final report #JFSP #06-3-3-13

  39. Acknowledgements • JFSP funding #06-3-3-1 • Field technicians • Charity Weaver & Adam Ridley • Chad Keyser for initial FORTRAN coding assistance & Stephanie Rebain implementation of results into FFE • Blaine Cook, Silviculturist, Black Hills National Forest • Mike Battaglia and Vicki Williams

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