赣南马尾松林地表细小死可燃物含水率动态及模型  

Moisture Dynamics and Modeling of Ground Surface Fine Dead Combustibles in Pinus massoniana Forest in Southern Jiangxi,China

作　　者：朱诗豪 吴志伟 李政杰 李顺 Zhu Shihao;Wu Zhiwei;Li Zhengjie;Li Shun(School of Geography and Environment,Jiangxi Normal University Key Laboratory of Poyang Lake Wetland and Watershed Research of Ministry of Education,Jiangxi Normal University Key Laboratory of Natural Disaster Monitoring,Early Warning and Assessment of Jiangxi Province Nanchang 330022)

机构地区：[1]江西师范大学地理与环境学院、江西师范大学鄱阳湖湿地与流域研究教育部重点实验室、江西省自然灾害监测预警与评估重点实验室,南昌330022

出　　处：《林业科学》2024年第5期158-168,共11页Scientia Silvae Sinicae

基　　金：国家自然科学基金项目(32271897)。

摘　　要：[目的]建立森林地表细小死可燃物(枯落叶、细枯枝、枯草等)含水率预测模型,预警区域森林火灾引燃的可能性及其潜在火行为。[方法]基于野外长期定位观测的赣南地区典型植被类型马尾松林地表细小死可燃物含水率数据,在不同地形条件和时间段进行气象因子随机森林相对重要性排序和皮尔逊相关性分析,建立地表细小死可燃物含水率随机森林模型和气象要素回归模型,比较不同模型精度指标,筛选适合赣南地区的森林火灾预测模型。[结果]赣南地区马尾松林地表细小死可燃物含水率具有明显变异性,阴坡含水率显著高于阳坡,在防火期初期最明显。地表细小死可燃物含水率与各气象要素(温度、相对湿度、风速、光照强度)具有极显著相关性(P<0.001);随机森林模型预测精度高于气象要素回归模型,阴坡2种模型精度均高于阳坡;具有滞后效应的光照强度因子对地表细小死可燃物含水率影响最大,影响地表细小死可燃物含水率的关键因素在阳坡是相对湿度、阴坡是风速。[结论]具有滞后效应的气象因子对赣南地区马尾松林地表细小死可燃物含水率有显著影响,考虑增加这些因素能更好预测地表细小死可燃物含水率变化,为火险预警提供可靠依据。【Objective】The moisture content of the surface fine dead combustibles(SFDC,including dead leaves,thin branches,dead grass,needles,etc.)significantly influences forest fire ignition and behavior.Understanding of the SFDC is essential for early warning of forest fire in a region.This study focuses on predicting SFDC in Masson pine forests in southern Jiangxi Province.【Method】We conducted long-term field observations of SFDC in Masson pine,a prevalent vegetation type in southern Jiangxi.The study involved a comparative analysis of various predictive models,considering meteorological factors'random forest relative importance and Pearson correlation in different terrains and times.【Result】SFDC in Masson pine forests shows a notable variability,with higher moisture content on shady slopes compared to sunny slopes,especially at the early time of fire prevention periods.A strong correlation(P<0.001)exists between SFDC and meteorological factors(temperature,humidity,wind speed,sunlight).The random forest model outperformed the meteorological factor regression model in accuracy,particularly on shady slopes.Sunlight,with a lag effect,and air humidity on sunny slopes and wind speed on shady slopes were the most influential factors.【Conclusion】Meteorological factors with time lag critically affect SFDC in Masson pine forests.Improved consideration of these factors enhances the prediction accuracy of the moisture content of the SFDC,offering a reliable basis for early warning of fire risk.

关 键 词：地表细小死可燃物含水率 预测模型 气象要素回归模型 随机森林 赣南地区 

分 类 号：S762[农业科学—森林保护学]