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    陈锋, 林向东, 牛树奎, 王叁, 李德,
    . 气候变化对云南省森林火灾的影响[J]. 北京林业大学学报, 2012, 34(6): 7-15.
    引用本文:
    陈锋, 林向东, 牛树奎, 王叁, 李德,
    . 气候变化对云南省森林火灾的影响[J]. 北京林业大学学报, 2012, 34(6): 7-15.
    CHEN Feng, LIN Xiang-dong, NIU Shu-kui, WANG San1, LI De, . Influence of climate change on forest fire in Yunnan Province, southwestern China[J]. Journal of Beijing Forestry University, 2012, 34(6): 7-15.
    Citation: CHEN Feng, LIN Xiang-dong, NIU Shu-kui, WANG San1, LI De, . Influence of climate change on forest fire in Yunnan Province, southwestern China[J]. Journal of Beijing Forestry University, 2012, 34(6): 7-15.

    气候变化对云南省森林火灾的影响

    Influence of climate change on forest fire in Yunnan Province, southwestern China

    • 摘要: 火灾是森林生态系统最重要的自然干扰因子,森林火灾的发生与气候变化有着十分密切的关系,对森林和人类造成了严重危害。本文在将云南省划分为5个生态区的基础上,选取了年均风速、年均气温日较差、年均湿润系数作为气象要素指标,年森林受害率作为火灾指标,对1982—2008年云南省各生态区气象因子与森林火灾之间的关系进行了研究。结果表明:1)1982—2008年各生态区气候因子变化趋势明显,年均湿润系数、年均风速变化较为同步,整体上呈显著下降趋势,年气温日较差变化存在非同步性。2)1982—2008年各生态区年森林受害率均呈现出反曲线式的显著下降趋势,年森林受害率随时间变化且呈显著的 S函数关系。3)不同生态区气象因子对森林受害率的影响不同,年均风速和年均气温日较差对年森林受害率的影响较大,而年均湿润系数对年森林受害率的影响较小,应用逻辑斯蒂回归模型可以很好地解释年森林受害率与气象因子之间的关系。4)由于植被类型、地形地貌等下垫面性质的差异,不同生态区森林火灾受到气候因素的影响程度不同,在生态Ⅰ区,植被类型为热带雨林、海拔较低、人为干扰因素较大,气候变化对森林火灾的影响相对较小,在生态Ⅴ区,植被类型为寒温带针叶林、海拔较高、人口干扰因素较小,气象因子对森林火灾的影响相对较大。森林火灾的发生应是各影响因子协同作用的结果,因此,在进行森林防火区划时,按照生态区划分更为合理,可以为防火部门提供科学的防火决策和管理依据。

       

      Abstract: In order to study the relationship between meteorological factors of different ecotopes and forest fire during 19822008, the annual mean wind speed, daily temperature range and wet coefficient were selected as indicators of meteorological elements, forest suffer rate as indicator of forest fire based on dividing Yunnan Province into five ecotopes. The results showed that: 1) meteorological factor of each ecotope changed obviously, annual mean wet coefficient and wind speed changed relatively synchronous, showing a whole significantly down trend, but the annual mean daily temperature range changed asynchronous. 2) Annual forest suffer rate of each ecotope presented a significant downward trend and showed inverse curve from 1982 to 2008, and the changing pattern of annual forest suffer rate varied with time series and showed a significant “S” function. 3) Different meteorological factors of different ecotopes had distinct influence on forest suffer rate, which was affected by annual mean wind speed and the annual mean daily temperature range more heavily than by the annual mean wet coefficient. Using logistic regression model can well explain the relationship between meteorological factor and forest suffer rate. 4) Due to the difference of vegetation type, topography and other factors, forest fire of each ecotope were affected differently by meteorological factors. In the area of ecotopeⅠ,which was covered by nonflammable fuels (evergreen broadleaved forest), lower elevation, and dense population , meteorological factors had limited impact on forest fires. While in the area of ecotopeⅤ,which was covered by flammable fuels (coniferous forest), higher elevation and sparse population, meteorological factors had larger impact on forest fires. Forest fire was the result of various influencing factors together. Therefore, in the forest fire prevention division, dividing by ecotope is more reasonable, which can provide scientific basis for decision making and management of fire prevention for fire department.

       

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