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应用生态学报 ›› 2012, Vol. 23 ›› Issue (11): 3149-3156.

• 研究报告 • 上一篇    下一篇

平地无风条件下蒙古栎阔叶床层的火行为Ⅱ.火焰长度和驻留时间影响因子分析与预测模型

张吉利,刘礴霏,邸雪颖,褚腾飞,金森**   

  1. (东北林业大学林学院, 哈尔滨 150040)
  • 出版日期:2012-11-18 发布日期:2012-11-18

Fire behavior of Mongolian oak leaves fuel bed under no-wind and zero-slope conditions. Ⅱ. Analysis of the factors affecting flame length and residence time and related prediction models.

ZHANG Ji-li, LIU Bo-fei, DI Xue-ying, CHU Teng-fei, JIN Sen   

  1. (College of Forestry, Northeast Forestry University, Harbin 150040, China)
  • Online:2012-11-18 Published:2012-11-18

摘要: 以帽儿山地区蒙古栎凋落叶的含水率、载量和床层高度为控制变量,模拟野外凋落叶床层状态,进行了100次平地无风条件下的室内点烧试验,分析含水率、载量和床层高度对火焰长度和驻留时间的影响,并建立了多元线性预测模型.结果表明: 含水率与火焰长度呈极显著线性负相关(P<0.01),与驻留时间的线性关系并不显著(P>0.05);载量、床层高度与火焰长度和驻留时间均呈极显著线性正相关(P<0.01).床层高度与含水率、载量的交互作用对火焰长度有显著影响;含水率与载量、床层高度的交互作用对驻留时间有显著影响.火焰长度预测模型的预测效果较好,能解释火焰长度83.3%的变异,平均绝对误差为7.8 cm,平均相对误差为16.2%;驻留时间预测模型的效果略差,仅能解释驻留时间54%的变异,平均绝对误差为9.2 s,平均相对误差为18.6%.

Abstract: Taking fuel moisture content, fuel loading, and fuel bed depth as controlling factors, the fuel beds of Mongolian oak leaves in Maoershan region of Northeast China in field were simulated, and a total of one hundred experimental burnings under no-wind and zero-slope conditions were conducted in laboratory, with the effects of the fuel moisture content, fuel loading, and fuel bed depth on the flame length and its residence time analyzed and the multivariate linear prediction models constructed. The results indicated that fuel moisture content had a significant negative liner correlation with flame length, but less correlation with flame residence time. Both the fuel loading and the fuel bed depth were significantly positively correlated with flame length and its residence time. The interactions of fuel bed depth with fuel moisture content and fuel loading had significant effects on the flame length, while the interactions of fuel moisture content with fuel loading and fuel bed depth affected the flame residence time significantly. The prediction model of flame length had better prediction effect, which could explain 83.3% of variance, with a mean absolute error of 7.8 cm and a mean relative error of 16.2%, while the prediction model of flame residence time was not good enough, which could only explain 54% of variance, with a mean absolute error of 9.2 s and a mean relative error of 18.6%.