北京地区8种树种枯死可燃物含水率预测模型及变化规律

詹航; 牛树奎; 王博

doi:10.12171/j.1000-1522.20190370

北京地区8种树种枯死可燃物含水率预测模型及变化规律

北京林业大学生态与自然保护学院，北京 100083

基金项目: 林业科技推广项目（2015-04）

详细信息

作者简介:
詹航。主要研究方向：生态规划与管理。Email：735772678@qq.com　地址：100083北京市海淀区清华东路35号北京林业大学生态与自然保护学院

责任作者:
牛树奎，教授，博士生导师。主要研究方向：生态规划与管理。Email：niushukui@163.com　地址：同上

中图分类号: S762.2
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出版历程
- 收稿日期: 2019-10-23
- 修回日期: 2020-01-09
- 网络出版日期: 2020-05-17
- 发布日期: 2020-06-30

Prediction models and changing rules of dead fuel moisture content of 8 forest species in Beijing area

School of Ecology and Nature Reserves, Beijing Forestry University, Beijing 100083, China

摘要

摘要:
目的森林可燃物含水率对林火的发生蔓延，尤其是对森林火灾火行为影响重大，可燃物含水率预测模型在预报火灾和预测林火行为方面作用显著。
方法对北京地区8种常见森林树种防火期内可燃物含水率连续测定，分析不同树种不同种类可燃物含水率与当期和前期气象因子间的关系。选择影响程度较大的当期和前期气象因子为自变量建立可燃物含水率的预测模型，并在此基础上定量分析可燃物含水率的日变化和整个防火期内的变化规律。
结果不同树种可燃物含水率存在显著差异，8个树种平均可燃物含水率由大到小依次为：栓皮栎 > 槲栎 > 榆树 > 刺槐 > 五角枫 > 侧柏 > 油松 > 落叶松。不同种类可燃物含水率存在显著差异，可燃物含水率总体上表现为阔叶树大于针叶树，枯叶和枯枝1 hr大于枯枝10 hr和100 hr。枯叶和枯枝1 hr主要受当期气象因子影响，而枯枝10 hr和100 hr主要受前期气象因子影响。所建立的32个线性预测模型各检验指标显示模型拟合效果好。可燃物含水率日变化表现为夜间高白天低，夜间稳定白天变幅大，06:00—08:00达到最大值，而后急剧下降，12:00—14:00左右达到全天最低值。防火期内，可燃物含水率呈现出先上升后下降趋势，11月可燃物含水率较低，但在缓慢增加，12月至次年1月含水率较高，而3月初至4月底可燃物含水率保持很低状态。
结论不同种类不同类型可燃物含水率预测模型精度较高，可为防火工作提供理论支撑，可以实践运用。北京地区3月份和日内中午时间干燥多风，温度较高，可燃物含水率达到很低的状态，森林火险等级较高，应加强管理。
- 可燃物含水率 /
- 防火期 /
- 预测模型 /
- 变化规律
Abstract:
ObjectiveThe forest fuel moisture content has great impact on the occurrence and spread of forest fire, particularly important on the fire behavior, and its prediction forecast models have significant effects on predicting forest fire and forest fire behavior.
MethodThrough the continuous determination of dead fuel moisture content of 8 kinds of forest trees in Beijing area during the period of fire prevention, we researched the relationship between fuel moisture content and the current and previous meteorological data, and established the prediction models by selecting the meteorological factors, which has great influence on the fuel moisture content. On this basis, we quantitatively analyzed the diurnal variation and fire protection period variation of the fuel moisture content.
ResultThe fuel moisture content of different tree species was significantly different, from descending order was Quercus variabilis, Q. aliena, Ulmus pumila, Robinia pseudoacacia, Acer truncatum, Platycladus orientalis, Pinus tabuliformis, Larix principis-rupprechtii. And the fuel moisture content of different types was also significantly different. Generally the broadleaf tree’s fuel moisture content was higher than that of coniferous trees, and the fuel moisture content of dead leaves and 1 hr dead branches were higher than 10 hr and 100 hr. That of 1 hr of dead branches was mainly affected by the current meteorological factors, while that of 10 hr and 100 hr of dead branches were affected by the early meteorological factors. The test indexes of 32 linear prediction models showed that the fitting effect was perfect. The diurnal variation of the fuel moisture content was higher and more stable at night, lower and varied greatly during the day, which peaked at 06:00−08:00, then dropped sharply, and later reached the lowest value at about 12:00−14:00. During the fire protection period, the fuel moisture content showed a trend of rising first, then decreasing. It was low in November but increased slowly and reached a high level from December to January of the following year. From early March to the end of April, the fuel moisture content kept a very low level.
ConclusionHigh-precision prediction methods of different tree species and among different fuel types could offer theoretical support for forest fire prevention, which can be used in practice. In March and at noon time in Beijing, it^,s windy, drying and hot and the fuel moisture content is low, so the fire management should be reinforced in such a fire danger period.
- fuel moisture content /
- period of fire prevention /
- prediction model /
- change rule

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图 1 可燃物含水率与气象因子关系

Figure 1. Relations between fuel moisture content and meteorological factors

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图 2 不同树种可燃物含水率

Figure 2. Fuel moisture content of different tree species

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图 3 不同种类可燃物含水率

Figure 3. Moisture content of different fuel types

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图 4 不同树种不同种类可燃物含水率日变化预测

Figure 4. Diurnal variation prediction of fuel moisture content of different tree species and different fuel types

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图 5 不同种类不同树种可燃物含水率防火期变化预测

Figure 5. Variation prediction of fuel moisture content of different tree species and different fuel types during fire prevention period

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表 1 不同树种可燃物含水率显著性分析

Table 1 Significance analysis on the fuel moisture content of different tree species

项目 Item	平方和 Sum of squares	自由度 Degree of freedom	均方 Mean squares	F	显著性 Significance
组间 Intergroup	2 121.117	7	303.017	52.288	0
组内 Intragroup	6 252.933	1 079	5.795
总计 Total	8 374.050	1 086

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表 2 不同种类可燃物含水率显著性分析

Table 2 Significance analysis on the fuel moisture content of different fuel types

项目 Item	平方和 Sum of squares	自由度 Degree of freedom	均方 Mean squares	F	显著性 Significance
组间 Intergroup	11 085.522	3	3 695.174	554.101	0
组内 Intragroup	3 594.471	539	6.669
总计 Total	14 679.993	542

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表 3 可燃物含水率与气象因子Pearson相关系数

Table 3 Pearson correlation coefficients of fuel moisture content and meteorological factors

种类 Type	当期和前期气象因子 Current and previous meteorological factor	相关系数 Correlation coefficient
种类 Type	当期和前期气象因子 Current and previous meteorological factor	榆树 Ulmus pumila	槲栎 Quercus aliena	刺槐 Robinia pseudoacacia	油松 Pinus tabuliformis	五角枫 Acer truncatum	华北落叶松 Larix principis-rupprechtii	栓皮栎 Quercus variabilis	侧柏 Platycladus orientalis
枯叶Dead leaf	温度 Temperature	− 0.747**	− 0.726**	− 0.790**	− 0.617**	− 0.415**	− 0.709**	− 0.491**	− 0.816**
	湿度 Humidity	0.637**	0.445**	0.676**	0.390**	0.374**	0.363**	0.386**	0.440**
	风速 Wind speed	− 0.570**	− 0.385**	− 0.568**	− 0.352**	− 0.347**	− 0.375**	− 0.273**	− 0.353**
	前2 h平均温度 Average temperature of former 2 hours	− 0.652**	− 0.679**	− 0.699**	− 0.565**	− 0.333**	− 0.672**	− 0.432**	− 0.780**
	前2 h平均相对湿度 Average relative humidity of former 2 hours	0.498**	0.364**	0.565**	0.285**	0.249**	0.266**	0.266**	0.337**
	前2 h平均风速 Average wind speed of former 2 hours	− 0.158	− 0.033	− 0.156	− 0.027	− 0.037	− 0.028	0.045	0.017
1 hr	温度 Temperature	− 0.563**	− 0.565**	− 0.433**	− 0.554**	− 0.488**	− 0.581**	− 0.665**	− 0.682**
	湿度 Humidity	0.433**	0.403**	0.329**	0.526**	0.374**	0.528**	0.515**	0.567**
	风速 Wind speed	− 0.426**	− 0.371**	− 0.126	− 0.451**	− 0.265**	− 0.335**	− 0.362**	− 0.521**
	前2 h平均温度 Average temperature of former 2 hours	− 0.530**	− 0.491**	− 0.349**	− 0.460**	− 0.420**	− 0.497**	− 0.596**	− 0.575**
	前2 h平均相对湿度 Average relative humidity of former 2 hours	0.355**	0.313**	0.246**	0.404**	0.253**	0.414**	0.414**	0.479**
	前2 h平均风速 Average wind speed of former 2 hours	− 0.102	− 0.044	− 0.032	− 0.107	0.017	− 0.113	− 0.048	− 0.145
10 hr	温度 Temperature	− 0.477**	− 0.454**	− 0.457**	− 0.397**	− 0.475**	− 0.359**	− 0.405**	− 0.392**
	湿度 Humidity	0.451**	0.392**	0.293**	0.389**	0.409**	0.135	0.328**	0.339**
	风速 Wind speed	− 0.227**	− 0.201*	− 0.145	− 0.120	− 0.127	0.029	− 0.105	− 0.149
	前20 h平均温度 Average temperature of former 20 hours	− 0.361**	− 0.316**	− 0.324**	− 0.302**	− 0.387**	− 0.301**	− 0.305**	− 0.384**
	前20 h平均相对湿度 Average relative humidity of former 20 hours	0.507**	0.451**	0.369**	0.434**	0.421**	0.274**	0.366**	0.421**
	前20 h平均风速 Average wind speed of former 20 hours	− 0.179*	− 0.147	− 0.165	− 0.125	− 0.085	− 0.067	− 0.117	− 0.147
100 hr	温度 Temperature	− 0.155	− 0.329**	− 0.195*	− 0.361**	− 0.156	− 0.138	− 0.402**	− 0.366**
	湿度 Humidity	0.167	0.310**	0.238**	0.163	0.229**	0.213*	0.350**	0.369**
	风速 Wind speed	− 0.076	− 0.116	− 0.168	− 0.118	− 0.111	− 0.080	− 0.152	− 0.139
	前100 h平均温度 Average temperature of former 100 hours	− 0.387**	− 0.381**	− 0.288**	− 0.489**	− 0.269**	− 0.234**	− 0.510**	− 0.339**
	前100 h平均相对湿度 Average relative humidity of former 100 hours	0.378**	0.469**	0.572**	0.594**	0.658**	0.583**	0.496**	0.376**
	前100 h平均风速 Average wind speed of former 100 hours	− 0.111	− 0.078	− 0.148	− 0.139	− 0.163	− 0.163	− 0.153	− 0.177*
注：“”和“”分别表示0.05和0.01水平下相关性显著。Notes: and ** mean significant correlations at P < 0.05 level and P< 0.01 level, respectively.

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表 4 可燃物含水率预测模型变量选择

Table 4 Prediction model variables of fuel moisture content

可燃物种类 Fuel type	变量选择 Variable selection
可燃物种类 Fuel type	当期气象因子 Current meteorological factor			前期气象因子 Previous meteorological factor
枯叶 Dead leaf	温度 Temperature	相对湿度 Relative humidity	风速 Wind speed	前2 h平均温度 Average temperature of former 2 hours	前2 h平均相对湿度 Average relative humidity of former 2 hours	前2 h平均风速 Average wind speed of former 2 hours
1 hr	温度 Temperature	相对湿度 Relative humidity	风速 Wind speed	前2 h平均温度 Average temperature of former 2 hours	前2 h平均相对湿度 Average relative humidity of former 2 hours	前2 h平均风速 Average wind speed of former 2 hours
10 hr	温度 Temperature	相对湿度 Relative humidity	风速 Wind speed	前20 h平均温度 Average temperature of former 20 hours	前20 h平均相对湿度 Average relative humidity of former 20 hours	前20 h平均风速 Average wind speed of former 20 hours
100 hr	温度 Temperature	相对湿度 Relative humidity	风速 Wind speed	前100 h平均温度 Average temperature of former 100 hours	前100 h平均相对湿度 Average relative humidity of former 100 hours	前100 h平均风速 Average wind speed of former 100 hours

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表 5 可燃物含水率模型参数及检验指标值

Table 5 Prediction model parameters of fuel moisture content and verification indices

树种 Tree species	类型 Type	b₀	b₁	b₂	b₃	b₄	b₅	b₆	R²	MAE	MRE
榆树 Ulmus pumila	枯叶 Dead leaf	− 1.273	− 0.029	0.021	− 0.251	0.012	− 0.015	0.202	0.776	2.83	13.44
	1 hr	− 1.558	0.007	0.007	− 0.158	− 0.012	− 0.003	0.123	0.418	2.26	11.31
	10 hr	− 1.888	− 0.008	− 0.001	0.049	0.003	0.005	− 0.014	0.454	1.05	6.47
	100 hr	− 2.362	0.010	0.005	0.043	− 0.016	0.004	0.017	0.343	1.53	12.69
槲栎 Quercus aliena	枯叶 Dead leaf	− 1.237	− 0.008	0.001	− 0.04	0.002	0.000	0.043	0.559	1.62	6.11
	1 hr	− 1.551	− 0.018	0.001	− 0.076	0.012	− 0.002	0.075	0.422	1.62	9.02
	10 hr	− 1.833	− 0.013	− 0.004	0.042	0.008	0.005	− 0.012	0.428	1.11	7.01
	100 hr	− 2.158	− 0.002	0.003	0.026	− 0.001	0.004	0.057	0.421	0.82	5.60
刺槐 Robinia pseudoacacia	枯叶 Dead leaf	− 1.141	− 0.013	0.009	− 0.088	0.005	− 0.004	0.076	0.778	1.92	6.45
	1 hr	− 1.793	− 0.024	0.006	0.127	0.018	− 0.005	− 0.088	0.405	0.95	6.11
	10 hr	− 1.86	− 0.029	− 0.011	0.083	0.02	0.008	− 0.078	0.456	1.42	11.91
	100 hr	− 2.629	− 0.003	0.002	− 0.006	− 0.002	0.011	0.116	0.411	1.56	13.98
油松 Pinus tabuliformis	枯叶 Dead leaf	− 1.439	− 0.007	0.005	− 0.041	0.003	− 0.004	0.041	0.44	1.42	6.65
	1 hr	− 1.648	− 0.017	0.012	− 0.122	0.013	− 0.009	0.113	0.498	1.94	10.71
	10 hr	− 2.235	− 0.012	0.001	0.116	0.004	0.007	− 0.027	0.376	1.56	12.47
	100 hr	− 2.375	− 0.004	− 0.002	0.012	− 0.004	0.008	0.040	0.578	0.76	6.76
五角枫 Acer truncatum	枯叶 Dead leaf	− 1.353	− 0.018	0.012	− 0.146	0.016	− 0.012	0.138	0.370	2.69	11.84
	1 hr	− 1.69	− 0.012	0.01	− 0.019	0.008	− 0.008	0.043	0.359	1.47	8.33
	10 hr	− 2.162	− 0.009	0.002	0.100	0.001	0.005	− 0.009	0.423	1.36	9.74
	100 hr	− 2.391	− 0.001	0.001	0.008	− 0.001	0.006	0.054	0.514	0.67	5.64
落叶松 Larix principis-rupprechtii	枯叶	− 1.395	− 0.014	0.004	− 0.096	0.002	− 0.004	0.082	0.547	2.14	12.2
	1 hr	− 1.967	− 0.016	0.015	0.090	0.009	− 0.009	− 0.059	0.504	1.15	8.46
	10 hr	− 1.963	− 0.012	− 0.006	0.070	0.006	0.005	− 0.036	0.358	0.92	7.05
	100 hr	− 2.510	− 0.002	0.003	0.033	− 0.002	0.01	0.078	0.404	1.64	12.88
栓皮栎 Quercus variabilis	枯叶 Dead leaf	− 1.447	− 0.005	0.011	− 0.052	0.002	− 0.008	0.075	0.371	1.94	8.39
	1 hr	− 1.573	− 0.011	0.008	0.009	0.004	− 0.004	0.016	0.535	1.42	7.24
	10 hr	− 1.644	− 0.007	− 0.001	0.046	0.003	0.003	− 0.019	0.331	1.08	5.36
	100 hr	− 2.221	− 0.001	0.004	0.047	− 0.006	0.005	0.036	0.509	1.09	7.64
侧柏 Platycladus orientalis	枯叶 Dead leaf	− 1.300	− 0.013	0.008	− 0.019	− 0.002	− 0.005	0.044	0.707	2.05	8.90
	1 hr	− 1.597	− 0.040	− 0.004	− 0.145	0.030	0.003	0.114	0.652	1.58	10.39
	10 hr	− 2.078	0.001	0.002	0.070	− 0.008	0.005	− 0.016	0.358	1.33	8.80
	100 hr	− 2.349	− 0.005	0.004	0.046	0.001	0.004	0.007	0.357	0.91	7.88

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参考文献(30)

[1]	赵凤君, 王明玉, 舒立福. 森林火灾中的树冠火研究[J]. 世界林业研究, 2010, 23(1):39−43. Zhao F J, Wang M Y, Shu L F. Areview of crown fire research[J]. World Forestry Research, 2010, 23(1): 39−43.
[2]	胡海清, 牛树奎, 金森, 等. 林火生态与管理[M]. 北京: 中国林业出版社, 2005: 31−37. Hu H Q, Niu S K, Jin S, et al. Fire ecology and management[M]. Beijing: China Forestry Publishing House, 2005: 31−37.
[3]	周涧青. 大兴安岭南部主要森林类型可燃物负荷量及其潜在地表火行为研究[D]. 北京: 北京林业大学, 2014. Zhou J Q. Research on forest fuel loadings and potential surface fire behavior of major forest types in southern Daxing’an Mountains[D]. Beijing: Beijing Forestry University, 2014.
[4]	Mitsopoulos I D, Dimitrakopoulos A P. Canopy fuel characteristics and potential crown fire behavior in Aleppo pine(Pinus halepensis Mill) forests[J]. Annals of Forest Science, 2007, 64(3): 287−299. doi: 10.1051/forest:2007006
[5]	Kreye J K, Kobziar L N, Zipperer W C. Effects of fuel load and moisture content on fire behaviour and heating in masticated litter-dominated fuels[J]. International Journal of Wildland Fire, 2013, 22(4): 440−445. doi: 10.1071/WF12147
[6]	牛树奎, 王叁, 贺庆棠, 等. 北京山区主要针叶林可燃物空间连续性研究: 可燃物垂直连续性与树冠火发生[J]. 北京林业大学学报, 2012, 34(3):1−7. Niu S K, Wang S, He Q T, et al. Spatial continuity of fuels in major coniferous forests in Beijing mountainous area: fuel vertical continuity and crown fire occurrence[J]. Journal of Beijing Forestry University, 2012, 34(3): 1−7.
[7]	王舜娆, 金森. 基于热重分析的南昌地区8种可燃物的热解动力学及燃烧性排序[J]. 中南林业科技大学学报, 2015, 35(11):94−98. Wang S R, Jin S. Study on pyrolysis kinetics and combustibility ordering of 8 kinds of fuels in Nanchang area based on thermogravimetric analysis[J]. Journal of Central South University of Forestry & Technology, 2015, 35(11): 94−98.
[8]	王明玉, 舒立福, 姚树人. 北京地区森林可燃物人工调控技术[J]. 森林防火, 2012, 9(3):46−48. doi: 10.3969/j.issn.1002-2511.2012.03.019 Wang M Y, Shu L F, Yao S R. Artificial control technology of forest fuel in Beijing area[J]. Forest Fire Prevention, 2012, 9(3): 46−48. doi: 10.3969/j.issn.1002-2511.2012.03.019
[9]	Matthews S. A process-based model of fine fuel moisture[J]. International Journal of Wild land Fire, 2006, 15: 155−168. doi: 10.1071/WF05063
[10]	屈宇, 于汝元, 张延达, 等. 营林防火的理论与实践[J]. 林业资源管理, 2002(4):13−16. doi: 10.3969/j.issn.1002-6622.2002.04.004 Qu Y, Yu R Y, Zang Y D, et al. Theory and practice on forest fire prevention by means of tending[J]. Forest Resources Management, 2002(4): 13−16. doi: 10.3969/j.issn.1002-6622.2002.04.004
[11]	魏云敏, 鞠琳. 森林可燃物载量研究综述[J]. 森林防火, 2006(4):18−21. doi: 10.3969/j.issn.1002-2511.2006.04.008 Wei Y M, Ju L. Summary of the study on forest fuel load[J]. Forest Fire Prevention, 2006(4): 18−21. doi: 10.3969/j.issn.1002-2511.2006.04.008
[12]	Viegas D X, Pinol J, Viegas M T. Estimating live fine fuels moisture content using meteorologically-based indices[J]. International Journal of Wildland Fire, 2001, 10(2): 223−240. doi: 10.1071/WF01022
[13]	Nelson Jr., R M. A method for describing equilibrium moisture content of forest fuels[J]. Canadian Journal of Forest Research, 1984, 14(4): 597−600. doi: 10.1139/x84-108
[14]	Wotton B M, Beverly J L. Stand-specific litter moisture content calibrations for the Canadian Fine Fuel moisture code[J]. International Journal of Wildland Fire, 2007, 16(4): 463−472.
[15]	Simard A J. The moisture content of forest fuels (1): a review of the basic concepts [M]. Toronto: Canadian Development Forest Fire Research Institute, 1968.
[16]	Catchpole E A, Catchpole W R, Viney N R, et al. Estimating fuel response time and predicting fuel moisture content from field data[J]. International Journal of Wildland Fire, 2001, 10(2): 215−222. doi: 10.1071/WF01011
[17]	Slijepcevic A, Anderson W R, Matthews S. Testing existing models for predicting hourly variation in fine fuel moisture in eucalypt forests[J]. Forest Ecology and Management, 2013, 306: 202−215. doi: 10.1016/j.foreco.2013.06.033
[18]	高开通, 刘鹏举, 唐小明. 森林资源小班火险天气等级预报方法研究[J]. 北京林业大学学报, 2013, 35(4):61−66. Gao K T, Liu P J, Tang X M. Forecasting forest fire risk grade of forest subcompartment[J]. Journal of Beijing Forestry University, 2013, 35(4): 61−66.
[19]	张恒, 金森, 邸雪颖. 基于FWI湿度码的塔河林业局地表凋落物含水率预测[J]. 应用生态学报, 2014, 25(7):2049−2055. Zhang H, Jin S, Di X Y. Prediction of litter moisture content in Tahe Forestry Bureau of Northeast China based on FWI moisture codes[J]. Chinese Journal of Applied Ecology, 2014, 25(7): 2049−2055.
[20]	宋雨, 胡海清, 孙龙, 等. 大兴安岭不同坡位地表可燃物含水率的动态变化与建模[J]. 森林工程, 2017, 33(5):1−7. Song Y, Hu H Q, Sun L, et al. Dynamic change and modeling of moisture content of surface fuel in different slope positions of Daxing’anling[J]. Forest Engineering, 2017, 33(5): 1−7.
[21]	李海洋, 胡海清, 孙龙. 我国森林死可燃物含水率与气象和土壤因子关系模型研究[J]. 森林工程, 2016, 32(3):1−6. doi: 10.3969/j.issn.1001-005X.2016.03.001 Li H Y, Hu H Q, Sun L. Research on relational models of moisture content of dead forest fuel with meteorological factors and soil factors in China[J]. Forest Engineering, 2016, 32(3): 1−6. doi: 10.3969/j.issn.1001-005X.2016.03.001
[22]	张运林, 孙萍, 胡海清, 等. 风速对蒙古栎阔叶床层两个重要失水时间的影响[J]. 中南林业科技大学学报, 2018, 38(4):65−71. Zang Y L, Sun P, Hu H Q, et al. Effects of wind speed on two key drying times of fuelbeds composed of Mongolian oak leaves[J]. Journal of Central South University of Forestry & Technology, 2018, 38(4): 65−71.
[23]	胡海清, 罗碧珍, 罗斯生, 等. 大兴安岭典型林型地表可燃物含水率预测模型[J]. 中南林业科技大学学报, 2018, 38(11):1−9. Hu H Q, Luo B Z, Luo S S, et al. The prediction of moisture content of surface ground fuel of typical forest stand in Daxing’anling Mountains[J]. Journal of Central South University of Forestry & Technology, 2018, 38(11): 1−9.
[24]	尹坤. 基于Logistic曲线的森林可燃物含水率模型[D]. 哈尔滨: 东北林业大学, 2015. Yin K. The model of forest fuel moisture with Logistic curve[D]. Harbin: Northeast Forestry University, 2015.
[25]	邵潇, 牛树奎, 张晨, 等. 北京地区森林枯死可燃物含水率预测模型及变化规律[J]. 广东农业科学, 2015, 42(17):37−46. doi: 10.3969/j.issn.1004-874X.2015.17.007 Shao X, Niu S K, Zhang C, et al. Prediction model and change regularity of moisture of forest surface dead fuel in Beijing[J]. Guangdong Agricultural Sciences, 2015, 42(17): 37−46. doi: 10.3969/j.issn.1004-874X.2015.17.007
[26]	林文琪. 北京的珍贵自然遗产: 植物多样性[J]. 生态学报, 2006, 26(4):969−979. doi: 10.3321/j.issn:1000-0933.2006.04.001 Lin W Q. Precious natural heritage of Beijing: plant diversity[J]. Acta Ecologica Sinica, 2006, 26(4): 969−979. doi: 10.3321/j.issn:1000-0933.2006.04.001
[27]	李连强, 牛树奎, 陈锋, 等. 北京妙峰山林场地表潜在火行为及燃烧性分析[J]. 北京林业大学学报, 2019, 41(3):58−67. Li L Q, Niu S K, Chen F, et al. Analysis on surface potential fire behavior and combustion of Miaofeng Mountain Forest Farm in Beijing[J]. Journal of Beijing Forestry University, 2019, 41(3): 58−67.
[28]	陶长森, 牛树奎, 陈羚, 等. 妙峰山林场主要针叶林冠层特征及潜在火行为[J]. 北京林业大学学报, 2018, 40(5):82−89. Tao C S, Niu S K, Chen L, et al. Canopy characteristics and potential crown fire behavior of main coniferous forest in Miaofeng Mountain Forest Farm in Beijing[J]. Journal of Beijing Forestry University, 2018, 40(5): 82−89.
[29]	涂志华, 北京山区森林土壤碳−氮−水耦合循环机制研究[D]. 北京: 北京林业大学, 2015. Tu Z H. Mechanism study on the coupling cycle of soil carbon-nitrogen-waterof forest ecosystems in Beijing mountainous area[D]. Beijing: Beijing Forestry University, 2015.
[30]	Jolly W M, Hadlow A M. A comparison of two methods for estimating conifer live foliar moisture content[J]. International Journal of Wildland Fire, 2011, 21(2): 180−185.