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Journal of Resources and Ecology >

2012 , Vol. 3 >Issue 2: 105 - 117

DOI: https://doi.org/10.5814/j.issn.1674-764x.2012.02.002

Articles

Probability Models of Fire Risk Based on Forest Fire Indices in Contrasting Climates over China

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  • 1 Key Laboratory of Water Cycle & Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing
    100101, China;
    2 Graduate University of Chinese Academy of Sciences, Beijing 100049, China;
    3 CSIRO Land and Water, Private Bag 5, Wembley, WA 6913, Australia;
    4 Department of Biological Sciences, Macquarie University, NSW 2109, Australia;
    5 Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing
    100101, China;
    6 Plant Functional Biology & Climate Change Cluster, School of the Environment, University of Technology, Sydney, NSW 2007, Australia

Received date: 2012-05-03

  Revised date: 2012-05-22

  Online published: 2012-06-30

Supported by

this work is supported by the National Basic Research Program, from Ministry of Science and Technology of China (No 2010CB955304).

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Abstract

Fire weather indices have been widely applied to predict fire risk in many regions of the world. The objectives of this study were to establish fire risk probability models based on fire indices over different climatic regions in China. We linked the indices adopted in Canadian, US, and Australia with location, time, altitude, vegetation and fire characteristics during 1998-2007 in four regions using semiparametric logistic (SPL) regression models. Different combinations of fire risk indices were selected as explanatory variables for specific regional probability model. SPL regression models of probability of fire ignition and large fire events were established to describe the non-linear relationship between fire risk indices and fire risk probabilities in the four regions. Graphs of observed versus estimated probabilities, fire risk maps, graphs of numbers of large fire events were produced from the probability models to assess the skill of these models. Fire ignition in all regions showed a significant link with altitude and NDVI. Indices of fuel moisture are important factors influencing fire occurrence in northern China. The fuel indices of organic material are significant indicators of fire risk in southern China. Besides the well skill of predicting fire risk, the probability models are a useful method to assess the utility of the fire risk indices in estimating fire events. The analysis presents some of the dynamics of climate-fire interactions and their value for management systems.

Key words： climate; forest fire; meteorological risk; fire risk indices; semi-parametric logistic regression model

Cite this article

LI Xiaowei, FU Guobin, Melanie J. B. ZEPPEL, YU Xiubo, ZHAO Gang, Derek EAMUS, YU Qiang . Probability Models of Fire Risk Based on Forest Fire Indices in Contrasting Climates over China[J]. Journal of Resources and Ecology, 2012 , 3(2) : 105 -117 . DOI: 10.5814/j.issn.1674-764x.2012.02.002

References

Anderson K, D L Martell, M D Flannigan, et al. 2000. Modeling of fireoccurrence in the boreal forest region of Canada. In: Kasischke E, B J Stocks (eds.). Fire, climate change, and carbon cycling in the borealforest. New York: Springer-Verlag, 357-367.
Badia-Perpinya A, M Pallares-Barbera. 2006. Spatial distribution ofignitions in Mediterranean periurban and rural areas: the case of Catalonia. International Journal of Wildland Fire, 15: 187-196.
Bartlein P J, S W Hostetler, S L Shafer, et al. 2008. Temporal and spatialstructure in a daily wildfire-start data set from the western United States(1986-96). International Journal of Wildland Fire, 17: 8-17.
Beverly J L, D L Martell. 2005. Characterizing extreme fire and weatherevents in the Boreal Shield ecozone of Ontario. Agricultural and Forest Meteorology, 133: 5-16.
Box E O. 1995. Climate relations of the forests of east and southeast Asia. In: Box E O, R K Peet, T Masuzawa, et al. (eds.). Vegetation Science in Forestry. Dordrecht: Kluwer Academic Publishers.
Brillinger D R, H K Preisler, J W Benoit. 2003. Risk assessment: A forestfire example. http://www.fs.fed.us/psw/publications/preisler/psw_2003_preisler001_ims.pdf.
Brillinger D R, H K Preisler, J W Benoit. 2006. Probabilistic risk assessmentfor wildfires. Environmentrics, 17: 623-633.
Brown P M, E K Heyerdahl, S G Kitchen, et al. 2008. Climate effects onhistorical fires (1630-1900) in Utah. International Journal of Wildland Fire, 17: 28-39.
Cao M, Zhang J H. 1997. Tree species diversity of tropical forest vegetationin Xishangbanna, SW China. Biodiversity and Conservation: 6:995-1006.
Carlson J D, R E Burgan. 2003. Review of users’ needs in operational firedanger estimation: the Oklahoma example. International Journal of Remote Sensing, 24: 1601-1620.
Carrega P. 1991. A meteorological index of forest fire hazard in Mediterranean France. International Journal of Wildland Fire, 1: 79-86.
Carvalho A, M D Flannigan, K Logan, et al. 2008. Fire activity in Portugaland its relationship to weather and the Canadian Fire Weather Index System. International Journal of Wildland Fire, 17: 328-338.
Castro F X, A Tudela, M T Sebastia. 2003. Modeling moisture content inshrubs to predict fire risk in Catalonia (Spain). Agricultural and Forest Meteorology, 116: 49-59.
Catry F X, F C Rego, F Bacao, et al. 2009. Modeling and mapping wildfireignition risk in Portugal. International Journal of Wildland Fire, 18: 921-931.
Chandler P, P Cheney, L Thomas, et al. 1983. Fire in forestry. New York: John Wiley & Sons.
Chou Y H, R A Minnich, R A Chase. 1993. Mapping probability of fireoccurrence in San Jacinto mountains, California, USA. Environmental Management, 17: 129-140.
Chou Y H, R A Minnich, L A Salazar, et al. 1990. Spatial autocorrelation ofwildfire distribution in the Idyllwild Quadrangle, San Jacinto mountains, California, USA. Photogrammetric Engineering and Remote Sensing, 56:1507-1513.
Chuvieco E, I González, F Verdú, et al. 2009. Prediction of fire occurrencefrom live fuel moisture content measurements in a Mediterraneanecosystem. International Journal of Wildland Fire, 18: 430-441.
Cunningham A A, D L Martell. 1972. A stochastic model for the occurrenceof man-caused forest fires. Canadian Journal of Forest Research, 3: 282-287.
Cunningham C J. 1984. Recurring natural fire hazards: a case study of the Blue Mountains, New South Wales, Australia. Applied Geography, 4:5-27.
Davis F W, J Michaelsen. 1995. Sensitivity of fire regime in chaparralecosystems to climate change. In: Moreno J M, W C Oechel (Eds.). Global Change and Mediterranean-type Ecosystems. NewYork: Springer,435-456.
Dayananda P W. 1977. Stochastic models for forest fires. Ecological Modelling, 3: 309-313.
Deeming J E, R E Burgan, J B Cohen. 1977. The National Fire-Danger Rating System-1978. USDA, Forest Service, Gen. Tech. Rep. . Intermountain Forest and Range Experiment Station; Ogden, UT, USA,63.
Di X Y, Wang H L, Yao S R. 1993. Forest fire forecast. Harbin: Northeast Forestry University Publishing Company. (in Chinese)
Domos M, P Gongbing. 1988. The Climate of China. Berlin: Springer Verlag.
Duffy P A, J E Walsh, J M Graham, et al. 2005. Impacts of large-scaleatmospheric-ocean variability on Alaskan fire season severity. Ecological Applications, 15: 1317-1330.
Fairbrother A, J G Turnley. 2005. Predicting risks of uncharacteristicwildfires: application of the risk assessment process. Forest Ecology and Management, 211: 28-35.
Finney M A. 2005. The challenge of quantitative risk analysis for wildlandfire. Forest Ecology and Management, 211: 97-108.
Flannigan M D, J B Harrington. 1988. A study of the relation ofmeteorological variables to monthly provincial area burned by wildfire in Canada (1953–80). Journal of Applied Meteorology, 27: 441-451.
Gianelle D, L Vescovo, F Mason. 2009. Estimation of grassland biophysicalparameters using hyperspectral reflectance for fire risk map prediction. International Journal of Wildland Fire, 18: 815-824.
Gill A M, P H R Moore. 1996. Regional and historical fire weather patternspertinent to the January 1994 Sydney bushfires. Proceedings of the Linnean Society of New South Wales, 116: 27-35.
Gillett N P, A J Weaver, F W Zwiers, et al. 2004. Detecting the effects ofclimate change on Canadian forest fires. Geophysical Research Letters,31, L18211.
Griffiths D. 1999. Improved formula for the drought factor in McArthur’sforest fire danger meter. Australian Forest Journal, 62: 210-214.
Grissino-Mayer H D. 1999. Modeling fire interval data from the American Southwest with the Weibull distribution. International Journal of Wildland Fire, 9: 37-50.
Gu Y S, D M Pearsall, Xie S C, et al. 2008. Vegetation and fire history of a Chinese site in southern tropical Xishuangbanna derived from phytolithand charcoal records from Holocene sediments. Journal of Biogeography, 35: 325-341.
Guo T N. 2005. Fire situation in 2004 and tendency as well as countermeasures in the future. Fire Science Technology, 24: 263-266.
Hastie T J, R Tibshirani, J Friedman. 2001. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. New York: Springer.
Hosmer D W, S Lemeshow. 1989. Applied Logistic Regression. New York: Wiley.
Huesca M, J Litago, A Palacios-Orueta, et al. 2009. Assessment of forestfire seasonality using MODIS fire potential: A time series approach. Agricultural and Forest Meteorology, 149: 1946-1955.
Jiang W Y, S A G Leroy, N Ogle, et al. 2008. Natural and anthropogenicforest fires recorded in the Holocene pollen record from a Jinchuanpeat bog, northeastern China. Palaeogeography Palaeoclimatology Palaeoecology, 261: 47-57.
Johnson E A, S L Gutsell. 1994. Fire frequency models, methods andinterpretations. Advances in Ecological Research, 25: 239-287.
Keane R E, S A Drury, E C Karau, et al. 2010. A method for mappingfire hazard and risk across multiple scales and its application in firemanagement. Ecological Modelling, 221: 2-18.
Keeley J E. 2004. Impact of antecedent climate on fire regimes in coastal California. International Journal of Wildland Fire, 13: 173-182.
Keetch J J, G M Byram. 1968. A Drought Index for Forest Fire Control. USDA Forest Service Research Paper. Forest Service Southeastern Forest Experiment Station; Asheville, North Carolina, 1–32.
Kilinc M, J Beringer. 2007. The spatial and temporal distribution oflightning strikes and their relationship. Journal of Climate, 20: 1161-1173.
Mandallaz D, Ye R. 1997. Prediction of forest fires with Poisson models. Canadian Journal of Forest Research, 27: 1685-1694.
Martell D L, E Bevilacqua, B J Stocks. 1989. Modelling seasonal variationin daily people-caused forest fire occurrence. Canadian Journal of Forest Research, 19: 1555-1563.
Martell D L, S Otukol, B J Stocks. 1987. A logistic model for predictingdaily people-caused forest fire occurrence in Ontario. Canadian Journalof Forest Research, 17: 394-401.
Mcarthur A G. 1967. Fire behaviour in eucalypt forest. Comm. Australian Forest Timber Burn Leaflet, 25.
Moreira F, F X Catry, F Rego, et al. 2010. Size-dependent pattern of wildfireignitions in Portugal: when do ignitions turn into big fires? Landscape Ecology, 25: 1405-1417.
Moritz M A 2003. Spatiotemporal analysis of controls on shrubland fireregimes: Age dependency and fire hazard. Ecology, 84: 351-361.
Nash C H, E A Johnson. 1996. Synoptic climatology of lightning-causedforest fires in subalpine and boreal forests. Canadian Journal of Forest Research, 26: 1859-1874.
Noble I R, G A V Bary, A M Gill. 1980. McArthur’s fire-danger metersexpressed as equations. Australian Journal of Ecology, 5: 201-203.
Peng R, F Schoenberg. 2001. Estimation of wildfire hazard using spatialtemporalfire history data. Los Angeles: Statistics Department, Universityof California.
Peters D P, R A Pielke, B T Bestelmeyer, et al. 2004. Cross-scaleinteractions, non-linearities, and forecasting catastrophic events.
Proceedings of the National Academy of Sciences of the United States of America, 101: 15130-15135.
Preisler H K, D R Brillinger, R E Burgan, et al. 2004. Probability basedmodels for estimating wildfire risk. International Journal of Wildland Fire, 13: 133-142.
Preisler H K, Chen S C, Fujioka F, et al. 2008. Wildland fire probabilitiesestimated from weather model-deduced monthly mean fire dangerindices. International Journal of Wildland Fire, 17: 305-316.
Preisler H K, A L Westerling. 2007. Statistical model for forecastingmonthly large wildfire events in Western United States. Journal of Applied Meteorology and Climatology, 46: 1020-1030.
Pu Y S, Zhang J H, Cao M. 2001. A strategic study on biodiversityconservation in Xishuangban. Journal of Forestry Research, 12: 25-30. (in Chinese)
R Development Core Team. 2010. R: a language and environment forstatistical computing. Vienna, Austria: R Foundation for Statistical Computing.
Ray D, D Nepstad, P Moutinho. 2005. Micrometeorological and canopycontrols of fire susceptibility in a forestedAmazon landscape. Ecological Applications, 15: 1664-1678.
Reed W J. 1998. Determining changes in historical forest fire frequencyfrom a time-since-fire map. Journal of Agricultural, Biological and Environmental Statisctics, 3: 430-450.
Reed W J, C P S Larsen, E A Johnson, et al. 1998. Estimation of temporalvariations in fire frequency from time-since-fire map data. Forest Science,44: 465-475.
Ren M. 1985. An outline of China’s physical geography. Beijing: Foreign Languages Press.
Renkin R A, D G Despain. 1992. Fuel moisture, forest type, andlightningcaused fire in Yellowstone National Park. Canadian Journal of Forest Research, 22: 37-45.
Richard R, J R Heim. 2002. A review of twentieth-century drought indicesused in the United States. American Meteorological Society: 1149-1165.
Roads J, Fujioka F, Chen S, et al. 2005. Seasonal fire danger forecasts forthe USA. International Journal of Wildland Fire, 14: 1-18.
Rothermel R C. 1972. A mathematical model for predicting fire spread inwildland fuels. USDA Forest Service, Intermountain Forest and Range
Experiment Station Research Paper, Ogden UT.
Saglam B, E Bilgili, K Omer, et al. 2006. Determination of surface fuelsmoisture contents based on weather conditions. Forest Ecology and Management, 234S: S75.
Sakamoto Y, Ishiguro M, Kitagawa G. 1986. Akaike Information Criterion Statistics. D. Reidel Publishing Company.
Shu L, Zhang X, Dai X, et al. 2003. Forest fire research (Ⅱ)-fire forecast. World Forestry Research, 16. (in Chinese)
Sweeting M M. 1999. Karst in China: Its Geomorphology and Environment. New York: Springer.
Taylor A H, V Trouet, C N Skinner. 2008. Climatic influences on fireregimes in montane forests of the southern Cascades, California, USA. International Journal of Wildland Fire, 17: 60-71.
Tian X R, Shu L F, Wang M Y, et al. 2009. Spatial and temporal distributionof lightning fire and forecasting model for Daxing’anling Region. Forest Research, 22: 14-20.
Turner J A, B D Lawson. 1978. Weather in the Canadian Forest Fire Danger Rating System. A user guide to national standards and practices. Victoria: Pacific Forest Research Centre.
Van Wagner C E. 1970. Conversion of William’s severity rating for use withthe Fire Weather Index. Canadian Forest Service, 5.
Van Wagner C E. 1977. Conditions for the start and spread of crown fire. Canadian Journal of Forest Research, 7: 23-34.
Van Wagner C E. 1987. Development and structure of the Canadian forestfire weather index system. Ottawa: Canadian Forestry Service, 37.
Vazquez A, J M Moreno. 1998. Patterns of lightning- and human-causedfires in peninsular Spain. International Journal of Wildland Fire, 8: 103-115.
Viegas D X, M T Viegas. 1994. A relationship between rainfall and burnedarea for Portugal. International Journal of Wildland Fire, 4: 11-16.
Viegas D X, M T Viegas, AD Ferreira. 1992. Moisture content of fine fuelsand fire occurrence in Central Portugal. International Journal of Wildland Fire, 2: 69-86.
Viney N R. 1991. A review of fine fuel moisture modelling. International Journal of Wildland Fire, 1: 215-234.
Whitlock C, S L Shafer, J Marlon. 2003. The role of climate and vegetationchange in shaping past and future fire regimes in the northwestern USand the implications for ecosystem management. Forest Ecology and Management, 178: 5-21.
Williams D E. 1959. Fire season severity rating. Government of Canada, Department of Northern Affairs and National Resources, 13.
Xu H C. 1998. Da Hinggan Ling Mountain forests in China. Beijing: Science Press. (in Chinese)
Yang G, Di X Y, Zeng T, et al. 2010. Prediction of area burned underclimatic change scenarios: A case study in the Great Xing’an Mountains boreal forest. Journal of Forestry Research, 21. (in Chinese)
Zhao F J, Shu L F, Tiao X R, et al. 2009. Change trends of forest fire dangerin Yunnan Province in 1957-2007. Chinese Journal of Ecology, 28: 2333-2338. (in Chinese)
Zhou B, Shen C D, Sun Y M, et al. 2006. A biomass burning record fromthe Lingtai Loess Section during the last 370 ka and implication forclimate and environment. Chinese Science Bulletin, 51: 2116-2124.
Zhou Y L, Ai C L, Dong S L, et al. 1991. Vegetation of Greater Xingan in China. Beijing: Science Press. (in Chinese)
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