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应用生态学报 ›› 2016, Vol. 27 ›› Issue (7): 2212-2224.doi: 10.13287/j.1001-9332.201607.007

• 研究论文 • 上一篇    下一篇

大兴安岭典型林分地表死可燃物含水率动态变化及预测模型

胡海清1, 陆昕1, 孙龙1*, 关岛2   

  1. 1东北林业大学林学院, 哈尔滨 150040;
    2赤峰市林业局, 内蒙古赤峰 024000
  • 收稿日期:2015-12-21 发布日期:2016-07-18
  • 通讯作者: *E-mail: luxin@nefu.edu.cn
  • 作者简介:胡海清,男,1961年生,教授. 主要从事林火生态与管理研究. E-mail: huhq@nefu.edu.cn
  • 基金资助:
    本文由林业公益性行业科研专项(201404402)资助

Dynamics and prediction models of ground surface dead fuel moisture content for typical stands in Great Xing’an Mountains, Northeast China.

HU Hai-qing1, LU Xin1, SUN Long1*, GUAN Dao2   

  1. 1College of Forestry, Northeast Forestry University, Harbin 150040, China;
    2Chifeng City Forestry Bureau, Chifeng 024000, Inner Mongolia, China
  • Received:2015-12-21 Published:2016-07-18
  • Contact: *E-mail: luxin@nefu.edu.cn
  • Supported by:
    This work was supported by the Forestry Industry Research Special Funds for Public Welfare Projects of China (201404402).

摘要: 对春季和秋季大兴安岭地区西林吉林业局山杨-白桦混交林、落叶松林、樟子松林、落叶松-白桦混交林、白桦林5种典型林分不同坡位地表细小死可燃物含水率动态进行研究,构建了不同季节防火期、不同林分地表死可燃物含水率的预测模型,并分析了其预测误差.结果表明: 相同林分地表可燃物含水率在春季和秋季差异显著;在相同季节相同林分下不同坡位可燃物含水率存在差异.采用Nelson模型对地表死可燃物含水率预测的平均绝对误差(MAE)的平均值为0.13,略低于Simard模型(0.14),明显低于气象要素回归模型(0.25).Nelson和Simard模型的预测效果好于气象要素回归模型.秋季模型对地表死可燃物含水率的预测精度好于春季模型和春季秋季混合模型.

Abstract: The fuel moisture content dynamics of mixed forest of Populus davidiana-Betula platyphylla, Larix gmelinii, Pinus sylvestris var. mongolica, mixed forest of L. gmelinii-B. platyphylla, B. platyphylla at different slope positions in spring and autumn were investigated in Xilinji Forestry Bureau ofthe Great Xing’an Mountains region. The moisture content prediction models of different stands in different seasons were established and the predicted errors were analyzed.The results showed that the fuel moisture content in the same stand varied with slope position. The mean absolute error of Nelson model (0.13) was lower than that of Simard model (0.14), and was significantly lower than that of meteorological element regression model (0.25). The prediction accuracy of the autumn model was higher than the spring model and spring-autumn mixed model.