赣南马尾松林地表细小死可燃物含水率动态及模型

doi:10.11707/j.1001-7488.LYKX20230388

Abstract

Abstract:

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.

Key words: moisture content of surface fine dead combustibles, prediction model, meteorological regression model, random forest, southern Jiangxi

CLC Number:

S762

Shihao Zhu,Zhiwei Wu,Zhengjie Li,Shun Li. Moisture Dynamics and Modeling of Ground Surface Fine Dead Combustibles in Pinus massoniana Forest in Southern Jiangxi, China[J]. Scientia Silvae Sinicae, 2024, 60(5): 158-168.

Figures/Tables 10

Table 1

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Table 2

Table S1

Meteorological regression model of surface fine dead combustibles moisture content"

坡向 Aspect	时间 Time	逐步回归预测模型 Stepwise regression prediction model	RMSE	R²	MAE
阳坡 Sunny slope	0:00	M=1.08+0.62 T?0.12 W+0.23 W_t-4+2.30 I_t-8?0.94 T_t-10	0.81	0.36	0.61
	2:00	M=19.30+0.76 T+0.21 W?0.21 W_t-2+0.17 W_t-6+47.8 I_t-8-1.12 T_t-10+2.42 I_t-10	0.84	0.36	0.64
	4:00	M=58.06+0.66 T+0.28 W_t-2?0.21 W_t-4+0.15 W_t-8-1.01 T_t-10+148.83 I_t-10	0.92	0.29	0.75
	6:00	M=7 479.61+1.55T+0.16 W?0.98 T_t-2+19 201.79 I_t-2+0.24 W_t-4?0.19 W_t-6-1.01 T_t-10+0.21 H_t-10+0.10 W_t-10	0.97	0.20	0.76
	8:00	M=8 125.94?0.76T+0.23W+1.88 W_t-2+20 860.68 I_t-4+0.21 W_t-6-1.51 T_t-8?0.27 W_t-8+0.25 H_t-10	0.91	0.26	0.74
	10:00	M=5 374.27?0.63T?0.11 W+0.22 W_t-2+1.23 T_t-4+13796.24 I_t-6+0.19 W_t-8?0.97 T_t-10?0.24 W_t-10	0.80	0.37	0.64
	12:00	M=5 206.65-1.19T+0.30I?0.09W?0.15W_t-2+0.92T_t-4+0.19 W_t-4+0.96 T_t-6-1.45 I_t-6?0.10 T_t-8+13 365.52 I_t-8+0.10 W_t-10	0.76	0.35	0.60
	14:00	M=4 420.57-1.44 T_t-2?0.11 W_t-2+0.31 I_t-2?0.21 W_t-4+1.64 T_t-6+0.20 W_t-6+0.13 W_t-8-1.67 I_t-8?0.47 T_t-10+0.11 W_t-10+11 345.30 I_t-10	1.39	0.30	0.77
	16:00	M=1.16+2.38 I?0.10 W?0.67 T_t-2-1.11 T_t-4?0.14 W_t-4+0.26 I_t-4+0.78 T_t-6?0.17 W_t-6?0.19 I_t-6+0.70 T_t-8+0.21 W_t-8?0.41 I_t-8+0.13 W_t-10	0.72	0.47	0.54
	18:00	M=1.19+2.35 I_t-2?0.65 T_t-4?0.92 T_t-6?0.16 W_t-6+ 0.19 I_t-6+0.60 T_t-8?0.10 W_t-8?0.20 I_t-8+0.72 T_t-10+0.17 W_t-10?0.34 I_t-10	0.76	0.40	0.57
	20:00	M=22.65+0.70 T+0.23 W+55.41 I_t-2+2.13 I_t-4- 1.59 T_t-6?0.15 W_t-8+0.15 I_t-8+0.54 T_t-10?0.15 I_t-10	0.78	0.41	0.59
	22:00	M=28.10+1.11 T+0.27 W_t-2-1.09 T_t-4+69.42 I_t-4+2.47 I_t-6?0.44 I_t-8?0.37 T_t-10?0.13 W_t-10	0.79	0.40	0.60
阴坡 Shady slope	0:00	M=1.33+1.29 H+0.16 W+1.72 T_t-2+0.26 W_t-2- 1.43 T_t-4?0.15 W_t-6?0.17 H_t-8+2.64 I_t-8?0.82 T_t-10?0.38 W_t-10?0.39 I_t-10	0.78	0.42	0.61
	2:00	M=1.29+2.04 H+0.32 W+0.98 T_t-2+0.19 W_t-2?0.23 H_t-8?0.22 W_t-8-1.72 T_t-10?0.25 W_t-10+3.26 I_t-10	0.85	0.34	0.65
	4:00	M=1 273.8+0.7 T+3 269.04 I?0.17 W+0.2 W_t-2+1.77T_t-6+0.27 W_t-6+0.69 H_t-8-2.77 T_t-10?0.52 H_t-10?0.3 W_t-10	0.90	0.28	0.70
	6:00	M=913.02+0.61 T?0.28 W_t-2+2343.5 I_t-2+ 1.81 T_t-8+0.93 H_t-8+0.25 W_t-8-2.94 T_t-10	0.94	0.20	0.74
	8:00	M=793.03-1.7 T?0.61 H?0.12 W+1.67 T_t-2?0.21 W_t-2+3.09 I_t-2+2032.7 I_t-4+1.89 H_t-6+0.11 W_t-6- 0.64T_t-10	0.87	0.27	0.70
	10:00	M=2.28-1.42 T?0.18 H?0.13 W?0.11 W_t-2+ 1.17 T_t-4?0.18 W_t-4+3.72 I_t-4+0.1 W_t-8	0.80	0.34	0.64
	12:00	M=2.02?0.17 H+0.19 I-1.81 T_t-2?0.12 W_t-2?0.1 W_t-4+1.61 T_t-6?0.15 W_t-6+2.76 I_t-6	0.75	0.41	0.59
	14:00	M=2.29?0.32 W?0.23 H_t-2-1.66 T_t-4+1.47 T_t-8?0.11 W_t-8+3.53 I_t-8	0.69	0.51	0.55
	16:00	M=2.56+1.46 I?0.45 T_t-2?0.29 W_t-2?0.21 H_t-4-1.17 T_t-6+1.39 T_t-10?0.14 W_t-10+2.9 I_t-10	0.70	0.50	0.55
	18:00	M=1.58+0.08 W+2.06 I_t-2?0.8 T_t-4?0.38 W_t-8-1.07 T_t-8+1.58 T_t-10	0.73	0.47	0.57
	20:00	M=1.54+0.46 T_t-2+2.16 I_t-4-1.59 T_t-6?0.32 W_t-6+0.88 T_t-10?0.2 I_t-10	0.80	0.38	0.62
	22:00	M=2+2.06 T+0.33 W-1.47 T_t-2?0.11 W_t-4- 0.15 H_t-6+3.11 I_t-6?0.84 T_t-8?0.38 W_t-8?0.46 I_t-8	0.79	0.39	0.61

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时滞 Time lag	阳坡 Sunny slope				阴坡 Shady slope
时滞 Time lag	温度 Temperature	相对湿度 Relative humidity	风速 Wind speed	光照强度 Illumination	温度 Temperature	相对湿度 Relative humidity	风速 Wind speed	光照强度 Illumination
当前时间 Current time	?0.38^***	0.23^***	0.12^***	?0.10^***	?0.34^***	0.14^***	?0.06	?0.15^***
前2 h 2 hours ago	?0.37^***	0.22^***	0.12^***	?0.17^***	?0.34^***	0.14^***	?0.07^***	?0.18^***
前4 h 4 hours ago	?0.36^***	0.21^***	0.12^***	?0.17^***	?0.33^***	0.14^***	?0.07^***	?0.19^***
前6 h 6 hours ago	?0.35^***	0.20^***	0.12^***	?0.15^***	?0.32^***	0.14^***	?0.08^***	?0.19^***
前8 h 8 hours ago	?0.33^***	0.20^***	0.13^***	?0.13^***	?0.31^***	0.15^***	?0.08^***	?0.18^***
前10 h 10 hours ago	?0.32^***	0.20^***	0.13^***	?0.11^***	?0.30^***	0.15^***	?0.08^***	?0.18^***

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

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坡向 Aspect	时间 Time	气象因子 Meteorological factor	增加的均方误差 IncMSE	增加的节点纯度 IncNodePurity
阳坡 Sunny slope	0:00	H_t-10	0.23	26.96
		I_t-8	0.27	24.12
		T_t-10	0.14	19.61
		I_t-6	0.20	13.45
		T_t-8	0.09	12.96
	20:00	I_t-4	0.22	22.31
		H_t-8	0.09	16.99
		H_t-6	0.11	15.78
		T_t-8	0.12	15.01
		T_t-6	0.10	13.91
	22:00	I_t-6	0.24	23.98
		H_t-8	0.14	19.44
		H_t-10	0.10	17.92
		T_t-10	0.12	16.86
		T_t-8	0.11	14.41
阴坡 Shady slope	14:00	I_t-4	0.25	30.20
		I_t-6	0.15	20.40
		I_t-2	0.08	14.24
		W	0.09	13.13
		I	0.04	10.62
	16:00	I_t-6	0.28	27.08
		I_t-8	0.16	21.54
		I_t-4	0.14	19.15
		W_t-2	0.09	13.58
		I_t-2	0.08	12.80
	18:00	I_t-8	0.22	32.09
		I_t-10	0.12	19.49
		I_t_-6	0.12	18.26
		I_t-4	0.12	16.44
		W_t-4	0.11	14.56