云南松林凋落物表层细小可燃物含水率预测模型研建

在2008年防火期2～5级火险天气条件下,在云南松纯林测定了137组林内气温(x1)、相对湿度(x2)、凋落物层表面温度(x3)、腐殖质层表面温度(x4)、凋落物层厚度(x5)、凋落物表层细小可燃物含水率(y)数据.分析表明,影响云南松林凋落物上层细小可燃物含水率的因子依次为:x3>x2>x4>x1,凋落物层厚度与含水率相关性很小.可以不参与模型的建立.用多元回归方法建立了云南松林细小可燃物含水率预测模型.新落叶的含水率对火环境因子的响应滞后是造成模型误差的主要原因.     

人工落叶松林凋落物层细小可燃物含水率模型的研究

将人工落叶松林凋落物层细小可燃物含水率作为研究对象,获取了162组观测数据,观测因子包括细小可燃物含水率(y)、凋落物层厚度(x1)、腐殖质层表面温度(x2)、凋落物层表面温度(x3)、林内气温(x4)、相对湿度(x5)。研究结果表明,影响细小可燃物含水率的因子从小到大依次为x4相似文献   

秦岭南坡华山松林潜在火险性影响因子的研究

应用37块标准地、185个样方的调查资料,在地表总易燃可燃物载量、枯落物含水率为10％时的易燃可燃物总含水量2个因子上,对秦岭南坡华山松林的5个林型进行了潜在火险性排序;以山坡下部华山松林型为代表,结合测定树冠层易燃可燃物密度,研究了林龄、针阀混交比对地表火和树冠火潜在火险性的影响,对此并进行了数量化趋势描述;文中同时也探讨了林分潜在火险性的研究方法．     

直接估计法在帽儿山林场白桦林可燃物含水率的适用性分析

在春季防火期内按林型内凋落物的不同空间位置的分类标准采集白桦林的地表死可燃物,并依此放入由小到大的铁丝网中,在室内进行试验,记录数据,对两种林型分别采用Nelson模型和Simard模型作为平衡含水率对温湿度响应模型,使用直接估计法对地表死可燃物的含水率结果进行了预测。结果表明:(1)基于Nelson模型的可燃物含水率预测误差往往小于基于Simard模型,一般来说基于Nelson模型的含水率预测效果要好于使用Simard模型预测含水率效果。但是,对于白桦林的腐殖质和混合可燃物则两者相差不大,预测效果都比较好,误差要求在3%以内都可以使用;(2)对于白桦林内凋落物及半腐殖质,误差要求在3%以内时,使用Nelson模型预测可燃物含水率的预测精度要更好;(3)白桦林内不同层可燃物含水率的预测精度由高到低依次为:混合可燃物、腐殖质、半腐殖质和凋落物。由于试验在室内进行,尽最大可能减少可外界误差影响,因此可以作为使用直接估计法预测白桦林含水率模型误差的最低值。     

华山松林细小可燃物层燃烧初始蔓延速度的初步研究

用自行设计的燃烧床研究了华山松林细小可燃物含水率、载量对燃烧初始蔓延速度的影响,通过分析试验过程中各测点的温度变化曲线,提出了可燃物层燃烧的初始蔓延速度的计算方法,即试验台水平方向上两热电偶的距离除以两热电偶测定到最高温的时间差;华山松林细小可燃物层燃烧的初始蔓延速度随可燃物含水率的增加而下降,载量是影响华山松林细小可燃物燃烧初始蔓延速度的一个重要因素,含水率为5.68%的华山松林细小可燃物的平均初始蔓延速度为0.0227m.s-1,该速度接近华山松林细小可燃物层在森林防火期燃烧的最大初始蔓延速度。     

滇中高原森林凋落物不同分解阶段C、N、P的化学计量特征及种间差异

【目的】为了解滇中高原磨盘山典型森林凋落物在不同分解阶段的化学计量特征,揭示滇中高原森林不同分解阶段凋落物的质量特征,为更好地促进滇中高原森林生态系统的凋落物分解进程和养分循环提供理论依据。【方法】以滇中高原的华山松Pinus armandii林、云南松Pinus yunnanensis林、高山栎Quercus semecarpifolia林、滇油杉Keteleeria evelyniana林、常绿阔叶林5种林地为试验区,人为地将自然状态下的森林凋落物分为未分解层、半分解层、已分解层,用以模拟凋落物的不同分解阶段,对不同分解层凋落物的碳氮磷(C、N、P)含量、化学计量比及元素释放率进行分析。【结果】1)随着凋落物分解程度的加剧,5种森林凋落物的C含量不断减少,P、N含量大体呈增加趋势,其中云南松林及华山松林的N含量呈先增加后减少的趋势,常绿阔叶林P含量为先减少后增加,且C、N、P含量在同一分解层中不同森林之间差异显著;2)5种森林凋落物的C∶N、C∶P随着凋落物分解程度的不断降低,云南松林的二者比值最高,N∶P在云南松林、华山松林和常绿阔叶林中先升高后降低,在高山栎林中先降低后升高,在滇油杉林中逐渐降低,且滇中高原森林凋落物C∶P和N∶P均显著小于全球平均水平;3)森林凋落物中C、N、P的总释放率均为滇油杉林>华山松林>高山栎林>云南松林>常绿阔叶林,常绿阔叶林前期元素释放效率快,后期减弱,华山松林和云南松林则相反,滇中磨盘山5种森林凋落物的化学元素易富集难释放。【结论】根据碳、氮、磷的化学计量特征表明,森林种间差异及不同的分解阶段会显著影响凋落物分解过程中的碳氮磷含量、化学计量比及其释放效率。     

大兴安岭典型林型地表可燃物含水率预测模型

准确地模拟地表可燃物含水率动态变化规律,对于预测预报林火发生或林火行为具有重要意义。应用Nelson模型和Simard模型,以5种典型林型(分别为阴坡落叶松林、阴坡落叶松-白桦混交林、阴坡白桦林、阳坡樟子松林和阳坡白桦林)的地表可燃物为研究对象,分析了气象因子与地被可燃物含水率的相关性,用2015年春季防火期实测数据构建了5种不同林型的可燃物含水率预测模型,并用2016年春季防火期监测数据对预测结果进行验证。结果表明:Nelson模型得出5种林分预测结果为:阴坡落叶松林阳坡白桦林阴坡白桦落叶松林阴坡白桦林阳坡樟子松林。Simard模型得出5种林分预测结果为:阴坡落叶松林阴坡白桦林阳坡白桦林阴坡白桦落叶松林阳坡樟子松林。2种模型预测阴坡落叶松的含水率准确率均高于其他4种林分。Nelson模型给出的阴坡落叶松平均绝对误差为0.09,平均相对误差(MRE)为0.09,Simard模型给出的平均绝对误差为0.10,平均相对误差为0.10,Nelson模型略优于Simard模型。研究结果可为该区的森林防火提供快速和准确的火险预测预报,进而为有效防控森林火灾提供重要的技术支撑。     

一种快速测定云南松和华山松林下细小可燃物负荷量的方法

在野外测定云南松和华山松纯林细小可燃物层平均厚度和负荷量的基础上,建立了细小可燃物层的平均厚度与负荷量的关系模型。经验证,云南松和华山松林下细小可燃物负荷量模型的平均误差分别为8.11%和10.43%。根据细小可燃物层的平均厚度推算可燃物负荷量的方法具有数据容易采集、操作简便的优点,适用于凋落物层被人为破坏的林分和灭火指挥员急需数据的紧急情况下进行可燃物负荷量的推算。     

平衡含水率法预测死可燃物含水率的研究进展

死可燃物含水率预报是森林火险天气预报的重要内容,准确预测死可燃物含水率是做好森林火险天气预报和火行为预报的关键.平衡含水率法预测死可燃物含水率在物理上十分可靠,若研究对象可精确描述,理论上其含水率的预测是准确的.因此,该方法是重要的可燃物含水率预测方法.本文对该方法的理论基础和应用情况进行综述.结果表明:1)平衡含水率的预测模型主要有4种,其中Simard模型、Van Wagner模型和Anderson模型都是统计模型,其应用具有一定的局限性;而Nelson模型为半物理模型,在预测可燃物含水率上的效果好,应用广.2)可燃物类型影响平衡含水率,但具体机理还没有系统研究.3)对时滞的影响因子研究相对较少.可燃物的种类、物理性质对时滞都有影响.4)现有平衡含水率法中,Catchpole等的方法因采用Nelson的半物理模型而具普适性,有良好的应用前途.5)平衡含水率法在实际预测中得到广泛应用,是美国火险等级系统和加拿大森林火险等级系统及其他类似系统中可燃物含水率预测的主要方法.     

森林地表死可燃物含水率预测模型研究进展

林火是影响森林生态系统的重要因子之一,林火蔓延和发展深受森林可燃物含水率的影响,尤其是林火的发生直接受地表死可燃物含水率的影响。因此,准确预测森林地表死可燃物含水率是预报森林火险和火行为的关键,加强森林死可燃物含水率预测模型研究尤为重要。从森林可燃物含水率的研究方法、研究模型及模型精度3方面综述研究现状,并对比评价现有模型。针对目前研究的诸多问题,提出5点展望:1)加强研究重点火险区野外含水率动态。利用已有的森林火险因子采集站和森林火险监测站获取不同环境因子和可燃物含水率及气象因子监测数据,构建重点火险区基于气象参数的森林可燃物含水率预测模型。2)加强森林可燃物的基础数据监测和收集。这可为全面构建森林火险等级系统奠定坚实的数据基础,同时还应建立精准的森林可燃物类型划分体系。3)加强研究可燃物含水率的空间异质性。应考虑不同影响因子下可燃物含水率动态,特别是了解小尺度内森林可燃物含水率的空间异质性,才能更准确进行林火预测预报。4)结合应用增强回归树(BRT)方法来提高模型精度。在可燃物含水率模型精度影响因子的研究中,运用BRT方法多次随机抽取一定量的数据,量化分析不同因子对模型精度的影响程度。5)结合GIS进行大尺度火险预警研究。综合应用RS和GIS技术,建立可燃物含水率的遥感反演模型,在准确模拟森林可燃物含水率空间分布的基础上,建立基于可燃物含水率的不同火险等级的预测模型。     

计划烧除对思茅松林下可燃物碳贮量的影响

以云南景谷县思茅松林为典型案例,从追求最大固碳效率为出发点,研究和评价计划烧除林和常规林2种森林管理模式林下各类可燃物含水率、载量、碳贮量和腐殖层厚度等。试验结果表明:经计划烧除后林下枯落物载量和细小可燃物载量跟常规林没有明显区别,且可燃物载量变化幅度相对稳定,但腐殖质层的高载量致使林下可燃物载量比常规林高2.92t.hm-2,平均碳贮量比常规林高1.943 t.hm-2,且腐殖层厚度比常规林厚0.526 cm,说明计划烧除可增加森林的固碳量,达到森林增汇效益。     

Spatial heterogeneity of dead fuel moisture content in a Larix gmelinii forest in Inner Mongolia using geostatistics

Spatial heterogeneity of fuel moisture content determines the spread rate and direction of a forest fire.Research on the spatial heterogeneity of the moisture content of dead fuel of Larix gmelinii Rupr.showed that:(1) fuel moisture content in litter layer semi-humus layer humus layer,and the coefficient of variation decreased with sampling depth;(2) the sill value of the semi-humus layer was highest,the humus layer moderate,the litter layer the smallest,overall,the spatial heterogeneity of the semi-humus layer was the highest.The humus layer in the slant direction and three layers in a vertical direction showed strong spatial correlation with the lowest nugget coefficient of 0.0968;(3)the fuel moisture content of the humus layer showed strong spatial anisotropy;and,(4) estimating the total moisture content of the sampling site by stimulated sampling reasonable control of the sampling interval,and increasing the sampling intensity can reduce the error.When the sampling intensity is increased to more than 16 and the sampling interval 3 m,the standard error is 15%.The spatial heterogeneity of fuel moisture content is best revealed by increasing sampling density,sampling in different fire seasons,and in different slope directions and positions.The results can provide a scientific basis for forest fire prediction and prevention.     

基于粒子群算法和BP神经网络的多因素林火等级预测模型

针对现有小尺度林火预测模型预测结果有效性、可扩展性等方面的不足,通过考虑多种火险因素,构建BP神经网络预测模型以提高预测精度,在此基础上借助粒子群算法加快BP神经网络收敛速度,进而提出一种混成的多因素森林火险等级预测模型particle swarm optimization based back-propagation neural network (PSO-BP)。所构建的预测模型,能够同时考虑气候因素(日最高气温、日平均气温、24 h降水量、连旱天数、日照时数、日平均相对湿度、日平均风速)、地形地貌因素(海拔、坡度、坡向、土壤含水量)、可燃物因素(植被类型、可燃物含水率、地被物载量)、人为因素(人口密度、距人类活动区域的距离) 16个变量。基于南京林业大学下蜀林场森林防火实验站传感器网络所采集的实际数据及现场测量数据,通过一组试验验证提出模型的有效性。结果表明:基于训练数据集及检验样本所构建的模型能够开展有效的火险等级预测;模型的计算复杂度较单独使用BP神经网络模型明显下降。     

Diurnal variation models for fine fuel moisture content in boreal forests in China

Studying diurnal variation in the moisture content of fine forest fuel(FFMC) is key to understanding forest fire prevention. This study established models for predicting the diurnal mean, maximum, and minimum FFMC in a boreal forest in China using the relationship between FFMC and meteorological variables. A spline interpolation function is proposed for describing diurnal variations in FFMC. After 1 day with a 1 h field measurement data testing, the results indicate that the accuracy of the sunny slope model was 100% and 84% when the absolute error was 3% and 10%, respectively, whereas the accuracy of the shady slope model was 72% and 76% when the absolute error was 3% and 10%, respectively. The results show that sunny slope and shady slope models can predict and describe diurnal variations in fine fuel moisture content, and provide a basis for forest fire danger prediction in boreal forest ecosystems in China.     

Quantifying fine fuel dynamics and structure in dry eucalypt forest (Eucalyptus marginata) in Western Australia for fire management

Techniques for rapid visual assessment of fuel characteristics have a broad range of applications in wildland fire management and research. We developed and tested a technique for assessing forest fuels which provides hazard ratings for distinct layers within the overall fuel complex, including bark, elevated shrubs, near-surface and surface (forest litter) fuels. These layers are comprised predominantly of fine fuel particles <6 mm diameter. The technique was used to model fuel accumulation in dry eucalypt forest of Eucalyptus marginata at two locations with contrasting understorey structures. We found that visual fuel hazard ratings described patterns of fuel dynamics over time in a similar fashion to models for fuel load accumulation. Visual hazard ratings can be related qualitatively to factors that reflect the difficulty of fire suppression by experienced fire fighters including visibility through the forest, access, difficulty of working machinery, flame height and spotting potential. The ability to relate hazard ratings to fire spread prediction needs to be tested.     

森林可燃物含水率及其预测模型研究进展

森林可燃物是森林火灾发生的物质基础,其含水率的变化直接影响森林可燃物着火的难易程度。高效准确地模拟森林可燃物含水率动态变化的规律,对预测预报林火发生或林火行为具有重要意义。文中从可燃物含水率的影响因子、理论算法、预测模型3个方面阐述了森林可燃物含水率及其预测模型研究进展;指出了研究存在的问题;提出了可燃物含水率研究展望:加强野外定位观测研究,优化测定方法并强化野外采样和室内试验标准化工作,加强可燃物含水率时空异质性研究,加强观测尺度外推问题研究并构建含水率遥感反演模型。     

南方8种森林地表死可燃物在平地无风时的燃烧蔓延速率与预测模型

【目的】研究Rothermel林火蔓延速率预测模型及另外2种以Rothermel模型为核心的蔓延速率预测模型对南方8种典型森林地表死可燃物的适用性,为林火蔓延速率预测提供理论支撑和指导。【方法】以南方地区8种典型速率地表死可燃物为对象,根据研究对象的野外实际条件,在东北林业大学帽儿山实验林场风洞实验室内,构建不同可燃物床层含水率、载量及高度的可燃物床层,每种可燃物在平地无风条件下进行36次点烧试验,共288次点烧,记录每种可燃物类型不同配比条件下的蔓延速率。通过直接使用Rothermel模型、重新估计Rothermel模型参数、对Rothermel模型形式改进后自建模型的对比,得到最合适的预测模型。【结果】 1)平地无风条件下,南方8种典型森林地表死可燃物床层最大蔓延速率为0.55 m ·min -1 ,平均蔓延速率由大到小依次为:华山松、云南松、毛竹、柳杉、杉木、马尾松、麻栎及青冈栎。2)直接使用Rothermel模型预测的林火蔓延速率误差较大,平均绝对误差为0.18 m ·min -1 ,平均相对误差为70.0%。3)重新估计参数后的Rothermel模型及自建模型,预测的可燃物蔓延速率精度显著提高,平均绝对误差分别为0.04、0.037 m ·min -1 ,平均相对误差分别<18%、16.45%。4)重新估计参数的Rothermel模型与自建模型的预测误差的差异不显著,其中自建模型的预测值与实测值的 R 2 变化在0.71~0.90,平均为0.80。【结论】对南方8种典型森林的地表死可燃物类型,在平地无风条件下,重新估计参数的Rothermel模型及自建模型的预测精度相近,但自建模型可能更简单易用,可预测平地无风条件下可燃物地表火蔓延速率。     

Moisture content thresholds for ignition and rate of fire spread for various dead fuels in northeast forest ecosystems of China

Fuel moisture content is one of the important factors that determine ignition probability and fire behaviour in forest ecosystems. In this study, ignition and fire spread moisture content thresholds of 40 dead fuel were performed in laboratory experiments, with a focus on the source of ignition and wind speed. Variability in fuel moisture content at time of ignition and during fire spread was observed for different fuels. Matches were more efficient to result in ignition and spread fire with high values of fuel moisture content compared to the use of cigarette butts. Some fuels did not ignite at 15% moisture content, whereas others ignited at 40% moisture content and fire spread at 38% moisture content in the case of matches, or ignited at 27% moisture content and spread fire at 25% moisture content using cigarette butts. A two-way ANOVA showed that both the source of ignition and the wind speed affected ignition and fire spread threshold significantly, but there was no interaction between these factors. The relationship between ignition and fire spread was strong, with R2 = 98% for cigarette butts, and 92% for matches. Further information is needed, especially on the density of fuels, fuel proportion(case of mixed fuels), fuel age, and fuel combustibility.     

Improved recruitment and early growth of Scots pine (Pinus sylvestris L.) seedlings after fire and soil scarification

The success of seedling recruitment of Scots pine (Pinus sylvestris L.) is strongly dependent on soil surface properties, such as humus depth and moisture content. In an undisturbed forest floor, seedlings are seldom able to become established due to the high incidence of desiccation in the organic soil layer. Methods that remove the organic soil layer are often necessary to improve the availability for radicles to reach the more stable moisture regime in the mineral soil. In this study we investigated pine-seedling establishment after mechanical soil scarification, burning of litter (O_L) and burning of litter and humus (O_L and O_FH) in two mature pine stands in Germany. The herbaceous layer of the first stand was dominated by grasses (Molinia caerulea L. and Deschampsia flexuosa L.), whereas the herbaceous layer of the second stand was dominated by blueberry (Vaccinium myrtillus L.). Pine seeds were placed in experimental plots, and seedling numbers and heights were recorded at regular intervals. All treatments that removed organic soil resulted in higher seedling counts than did the undisturbed forest floor. The highest seedling counts were found on scarified and severely burnt plots, whereas seedling counts were lower on lightly burnt plots. Seedlings were significantly taller on burnt plots. This study shows that pine regeneration is stimulated by fire, not only in boreal forests, but also under central European conditions. With the expectation of higher fire frequency in the near future due to climatic changes, natural regeneration and succession on burnt sites should receive more focus in forest management and research.