Abstract
Knowledge of forest fuels and their potential fire behavior across a landscape is essential in fire management. Four customized fire behavior fuel models that differed significantly in fuels characteristics and environmental conditions were identified using hierarchical cluster analysis based on fuels data collected across a boreal forest landscape in northeastern China. Fuel model I represented the dense and heavily branched Pinus pumila shrubland which has significant fine live woody fuels. These forests occur mainly at higher mountain elevations. Fuel model II is applicable to forests dominated by Betula platyphylla and Populus davidiana occurring in native forests on hill slopes or at low mountain elevations. This fuel model was differentiated from other fuel models by higher herbaceous cover and lower fine live woody loading. The primary coniferous forests dominated by Larix gmelini and Pinus sylvestris L. var. mongolica were classified as fuel model III and fuel model IV. Those fuel models differed from one another in average cover and height of understory shrub and herbaceous layers as well as in aspect. The potential fire behavior for each fuel model was simulated with the BehavePlus5.0 fire behavior prediction system. The simulation results indicated that the Pinus pumila shrubland fuels had the most severe fire behavior for the 97th percentile weather condition, and had the least severe fire behavior under 90th percentile weather condition. Fuel model II presented the least severe fire potential across weather conditions. Fuel model IV resulted in greater fire severity than Fuel model III across the two weather scenarios that were examined.
Similar content being viewed by others
References
Anderson HE (1982) Aids to determining fuel models for estimating fire behavior. USDA Forest Service General Technical Report INT-122
Andrews PL (1986) BEHAVE: fire behavior prediction and fuel modeling system—BURN subsystem. USDA Forest Service General Technical Report INT-194
Andrews PL, Loftsgaarden DO, Bradshaw LS (2003) Evaluation of fire danger rating indexes using logistic regression and percentile analysis. International Journal of Wildland Fire 12:213–226
Andrews PL, Bevins CD, Seli RC (2008) BehavePlus fire modeling system user’s guide. USDA Forest Service General Technical Report RMRS-GTR-106
Arroyo LA, Pascual C, Manzanera JA (2008) Fire models and methods to map fuel types: the role of remote sensing. Forest Ecology and Management 256:1239–1252
Brown JK (1970) Physical fuel properties of ponderosa pine forest floors and cheatgrass. USDA Forest Service General Technical Report INT-174
Burgan RE (1987) Concepts and interpreted examples in advanced fuel modeling. USDA Forest Service General Technical Report INT-238
Burgan RE (1988) Revisions to the 1978 National Fire-Danger Rating System. USDA Forest Service General Technical Report INT-39
Burgan RE, Rothermel RC (1984) BEHAVE: Fire behavior prediction and fuel modeling system—FUEL subsystem. USDA Forest Service General Technical Report INT-167
Burgan RE, Klaver RW, Klaver JM (1998) Fuel models and fire potential from satellite and surface observations. International Journal of Wildland Fire 8:159–170
Byram GM (1963) An analysis of the drying process in forest fuel materials. In: International symposium on humidity and moisture
Carlson JD, Burgan RE (2003) Review of users’ needs in operational fire danger estimation: the Oklahoma example. International Journal of Remote Sensing 24:1601–1620
Chen HW, Chang Y, Hu YM, Liu ZH, Zhou R, Jing GZ, Zhang HX, Hu CH, Zhang CM (2008) Load of forest surface dead fuel in Huzhong area of Daxing’an ling Mountains and relevant affecting factors. Chinese Journal of Ecology 27:50–55 (in Chinese with English abstract)
Cheney (1992) A National Fire Danger Rating System for Australia. International Forest Fire News No. 6
Chuvieco E, Congalton RG (1989) Application of remote sensing and geographical information system to forest fire hazard mapping. Remote Sensing of Environment 37:147–159
Deeming JE, Burgan RE, Cohen JD (1978) The National Fire-Danger Rating System. USDA Forest Service General Technical Report INT-39
Dimitrakopoulos AP (2002) Mediterranean fuel models and potential fire behavior in Greece. International Journal of Wildland Fire 11:127–130
Dymond CC, Roswintiarti O, Brady M (2004) Characterizing and mapping fuels for Malaysia and western Indonesia. International Journal of Wildland Fire 13:323–334
Fernandes PM (2001) Fire spread prediction in shrub fuels in Portugal. Forest Ecology and Management 144:67–74
Forestry Canada Fire Danger Group (1992) Development and structure of the Canadian Forest Fire Behavior Prediction System. Canada Forest Service Report ST-X-3
Garnica JGF (2001) Modeling the spatial variability of forest fuel arrays. PhD dissertation, Colorado State University, Colorado
Hu HQ (2005) Predicting forest surface fuel load by using forest stand factors. Scientia Silvae Sinicae 41:97–100 (in Chinese with English abstract)
Keane RE, Burgan R, van Wagtendonk J (2001) Mapping wildland fuels for fire management across multiple scales: integrating remote sensing, GIS, and biophysical modeling. International Journal of Wildland Fire 10:301–319
Kessell SR (1976) Gradient modeling: a new approach to fire modeling and wilderness resource management. Environmental Management 1:39–48
Kim YH, Bettinger P, Finney M (2009) Spatial optimization of the pattern of fuel management activities and subsequent effects on simulated wildfire. European Journal of Operational Research 197:253–265
Krasnow K, Schoennagel T, Veblen TT (2009) Forest fuel mapping and evaluation of LANDFIRE fuel maps in Boulder County, Colorado, USA. Forest Ecology and Management 257:1603–1612
Liu ZH, He HS, Chang Y, Hu YM (2010) Analyzing the effectiveness of alternative fuel reductions of a forested landscape in Northeastern China. Forest Ecology and Management 259:1255–1261
Lutes DC, Keane RE, Caratti JF (2009) A surface fuel classification for estimating fire effects. International Journal of Wildland Fire 18:802–814
McKenzie D, Raymond CL, Kellogg L-KB, Norheim RA, Andreu AG, Bayard AC, Kopper KE, Elman E (2007) Mapping fuels at multiple scales: landscape application of the Fuel Characteristic Classification System. Canadian Journal of Forest Research 37:2421–2437
Miller JD, Danzer SR, Watts JM, Stone S, Yool SR (2003) Cluster analysis of structural stage classes to map wildland fuels in a Madrean ecosystem. Journal of Environmental Management 68:239–252
Ottmar RD, Sandberg DV, Riccardi CL, Prichard SJ (2007) An overview of the fuel characteristic classification system-quantifying, classifying, and creating fuelbeds for resource planning. Canadian Journal of Forest Research 37:2383–2393
Pierce KB, Ohmann JL, Wimberly MC, Gregory MJ, Fried JS (2009) Mapping wildland fuels and forest structure for land management: a comparison of nearest neighbor imputation and other methods. Canadian Journal of Forest Research 39:1901–1916
Piñol J, Beven K, Viegas DX (2005) Modelling the effect of fire-exclusion and prescribed fire on wildfire size in Mediterranean ecosystems. Ecological Modelling 183:397–409
Poulos HM (2009) Mapping fuels in the Chihuahuan Desert borderlands using remote sensing, geographic information systems, and biophysical modeling. Canadian Journal of Forest Research 39:1917–1927
Poulos HM, Camp AE, Gatewood RG, Loomis L (2007) A hierarchical approach for scaling forest inventory and fuels data from local to landscape scales in the Davis Mountains, Texas, USA. Forest Ecology and Management 244:1–15
Pyne SJ, Andrews PL, Laven RD (1996) Introduction to wildland fire, 2nd edn. Wiley, New York
Reich RM, Lundquist JE, Bravo VA (2004) Spatial models for estimating fuel loads in the Black Hills, South Dakota, USA. International Journal of Wildland Fire 13:119–129
Riccardi CL, Ottmar RD, Sandberg DV, Andreu A, Elman E, Kopper K, Long J (2007) The fuelbed: a key element of the fuel characteristic classification system. Canadian Journal of Forest Research 37:2394–2412
Rothermel RC (1972) A mathematical model for predicting fire spread in wildland fuels. USDA Forest Service General Technical Report INT-115
Sah JP, Ross MS, Snyder JR, Koptur S, Cooley HC (2006) Fuel loads, fire regimes, and post-fire fuel dynamics in Florida Keys pine forests. International Journal of Wildland Fire 15:463–478
Sandberg DV, Ottmar RD, Cushon GH (2001) Characterizing fuels in the 21st century. International Journal of Wildland Fire 10:381–384
Scott JH, Burgan RE (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
Shan YL (2003) Study on forest fuel of Daxing’an Mountains in Northeast China. PhD. thesis, Department of Forest, Northeast Forestry University, Harbin, China (in Chinese with English abstract)
Shu LF, Wang MY, Tian XR, Li ZQ, Xiao RJ (2003) Fire environment mechanism of ground fire formation in Daxingan Mountains. Journal of Natural Disasters 12:62–67 (in Chinese with English abstract)
Shu LF, Wang MY, Li ZQ, Xiao RJ, Tian XR (2004) Dwarf Siberian pine forest fire environment in Daxingan Mountains. Journal of Mountain Science 22:36–39 (in Chinese with English abstract)
Stottlemyer AD, Shelburne VB, Waldrop TA, Rideout-Hanzak S, Bridges W (2009) Fuel characterization in the southern Appalachian Mountains: an application of Landscape Ecosystem Classification. International Journal of Wildland Fire 18:423–429
van Wagner CE (1977) Conditions for start and spread of crown fire. Canadian Journal of Forest Research 7:23–34
Wang MY, Shu LF, Xiao RJ, Li J, Du JH (2004) Landscape dynamics analysis of Daxing’an Mountains Huzhong Zone under the disturbance of forest fires. Journal of Mountain Science 22:702–706 (in Chinese with English abstract)
Wilson BA, Ow CFW, Heathcott M, Milne D, McCaffrey TM, Franklin SE (1994) Landsat MSS classification of fire fuel types in Wood Buffalo National Park. Global Ecology and Biogeography Letters 4:33–39
Xu HC (1998) Forest in Great Xing’ an Mountains of China. Science Press, Beijing, pp 1–231
Xu HC, Li ZD, Qiu Y (1997) Fire disturbance history in virgin forest in northern region of great Xing’ an Mountains. Acta Ecologica Sinica 17:337–343 (in Chinese with English abstract)
Acknowledgments
This research is funded by the Knowledge Innovation Project of Chinese Academy of Sciences (CAS; Grant No. KZCX2-YW-444) and the Natural Science Foundation of China (NSFC; Grant Nos. 41071120 and 2011CB403206). We thank Rui Zhou, Guo Zhi Jing, Hong Xin Zhang, Chang He Hu, and Chang Meng Zhang for their help with the field work. We are grateful to Yuan Man Hu, Yue Hui Li, Cang Ren Bu and Miao Liu for their constructive suggestions that greatly improved this manuscript. We thank Roger Ottmar and two anonymous reviewers for their comments which greatly improved earlier versions of this manuscript.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Wu, Z.W., He, H.S., Chang, Y. et al. Development of Customized Fire Behavior Fuel Models for Boreal Forests of Northeastern China. Environmental Management 48, 1148–1157 (2011). https://doi.org/10.1007/s00267-011-9707-3
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00267-011-9707-3