Influence and prediction of climatic factors on forest fires in China

doi:10.16265/j.cnki.issn1003-3033.2023.12.1736

Abstract

Abstract:

In order to explore the influence mechanism of climatic factors on the weight of forest fire accident, firstly, based on the data of forest fire accidents in China from 2011 to 2020, four kinds of climatic factors, including annual average pressure, temperature, precipitation and sunshine time, were selected as characteristic variables. Secondly, the results of forest fire danger zoning were obtained by the hierarchical clustering method, and the grey correlation coefficient was calculated to obtain the influence degree of climate factors on forest fire. Then, the regression model of four types of climatic factors in China was established to obtain the weight ranking of the impact of climatic factors on forest fires, and the risk value in 2021 was predicted. The results show that the fire danger areas in China could be divided into 10 categories under the influence of climatic factors. The influence of climate on the number of forest fires per unit area is ranked as follows: annual average precipitation > annual average pressure > annual average temperature > annual average sunshine time. Linear regression analysis and grey correlation degree analysis have similarities in the ranking results of fire danger zone. The relative error between the predicted risk value by linear regression analysis and the actual value in 2021 is less than 5%.

Key words: climatic factors, forest fire, fire hazard area, hierarchical clustering, correlation, linear regression

LU Yi, ZHOU Qinyun, SHAO Shuzhen, WANG Wei, DAI Yuqing, WEI Xue'e. Influence and prediction of climatic factors on forest fires in China[J]. China Safety Science Journal, 2023, 33(12): 53-59.

Figures/Tables 8

Tab.1

Fig.1

Fig.2

Tab.2

Tab.3

Regression model and test results of four types of climatic factors in China

类别	线性回归模型		相关系数	F值	显著水平
Ⅳ	y=b₁+b₂X₁+b₃X₂+b₄X₃+b₅X₄	(6)	0.671	6.80	0.027
Ⅷ			0.694	8.60	0.025
Ⅸ			0.688	14.99	0.018
Ⅹ			0.879	50.10	0.001
Ⅰ	y=b₁+b₂X₁+b₃X₂+b₄X₃+b₅X₄+b₆ $X 1 2$ +b₇ $X 2 2$ +b₈ $X 3 2$ + b₉ $X 4 2$ +b₁₀X₁X₂+b₁₁X₁X₃+b₁₂X₁X₄+b₁₃X₂X₄+b₁₄X₂X₄+b₁₅X₃X₄	(7)	0.803	6.39	0.001
Ⅱ			0.601	4.53	0.014
Ⅲ			0.868	35.05	0.001
Ⅴ			0.662	3.92	0.001
Ⅵ			0.846	46.40	0.001
Ⅶ			0.634	5.23	0.020

Tab.3

Tab.4

Tab.5

Tab.6

Prediction of forest fire hazards in 2021

省份	年均气压/MPa	年均气温/℃	年均降水量/m	年均日照时间/h	预测值	实际值	误差列
北京	0.100 26	13.00	0.691	2 278.9	0.042 7	0.041 8	0.000 9
天津	0.101 46	13.00	0.609	2 558.4	0.029 0	0.000 0	0.029 0
河北	0.099 34	12.90	0.859	2 160.9	0.013 7	0.013 9	-0.000 2
山西	0.092 82	11.00	0.722	2 458.0	0.021 7	0.021 8	-0.000 1
山东	0.099 01	14.00	0.708	2 249.6	0.007 5	0.007 5	0.000 0
河南	0.095 91	14.00	0.803	1 980.9	0.017 8	0.017 4	0.000 4
陕西	0.093 73	13.00	0.936	1 882.0	0.019 7	0.019 2	0.000 5
内蒙古	0.092 75	3.50	0.328	2 099.6	0.001 2	0.001 1	0.000 1
辽宁	0.103 05	9.30	0.741	2 133.0	0.023 05	0.022 7	0.000 4
吉林	0.098 74	6.80	0.788	2 392.0	0.014 4	0.014 0	0.000 4
黑龙江	0.095 60	4.30	0.804	2 765.0	0.005 0	0.005 0	0.000 0
上海	0.102 07	17.90	1.520	1 740.0	0.050 5	0.000 0	0.050 5
江苏	0.102 56	15.90	1.500	1 799.4	0.012 7	0.012 8	-0.000 1
安徽	0.102 35	18.40	1.290	1 931.5	0.012 2	0.012 6	-0.000 4
湖北	0.097 71	17.50	1.714	1 500.5	0.039 5	0.040 7	-0.001 2
浙江	0.100 86	17.60	1.403	1 384.2	0.058 4	0.057 9	0.000 5
福建	0.100 33	21.20	1.343	1 805.3	0.055 0	0.054 2	0.000 8
江西	0.100 88	18.20	1.310	1 493.1	0.050 9	0.049 0	0.001 9
湖南	0.100 26	18.00	1.342	1 342.5	0.048 8	0.048 5	0.000 3
广西	0.099 45	21.60	1.364	1 669.1	0.073 7	0.072 0	0.001 7
广东	0.100 84	22.90	1.784	1 364.0	0.105 8	0.101 4	0.004 3
海南	0.099 96	25.60	1.613	2 232.3	0.075 7	0.077 1	-0.001 4
重庆	0.096 24	18.20	1.306	1 339.3	0.022 1	0.022 5	-0.000 4
四川	0.089 05	15.40	1.133	1 395.9	0.010 0	0.009 8	0.000 2
贵州	0.094 26	16.70	1.181	1 003.5	0.014 9	0.014 3	0.000 6
云南	0.083 66	17.60	1.157	2 097.7	0.021 1	0.021 8	-0.000 7
西藏	0.065 59	5.40	0.390	2 512.2	0.001 3	0.001 3	0.000 0
甘肃	0.077 27	8.00	0.358	2 353.0	0.011 2	0.011 8	-0.000 6
青海	0.068 86	3.40	0.356	2 170.9	0.012 2	0.011 9	0.000 3
宁夏	0.083 19	8.50	0.296	2 502.0	0.059 3	0.061 0	-0.001 7

Tab.6

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类别	省份	聚类距离	气候特征
Ⅰ	重庆、四川、贵州	2~5	季风湿润气候,空气湿润,降水丰沛,太阳辐射弱,日照时间短,冬无严寒,夏无酷暑
Ⅱ	浙江、福建、江西、湖南、广西	4~5	季风明显,全年气温适中,日照较多,雨量充沛,空气湿润
Ⅲ	上海、江苏、安徽、湖北	3~5	亚热带湿润性季风气候,四季分明,日照充分,雨量充沛,冬冷夏热,春温多变
Ⅳ	广东	7	季风气候,热量资源丰富,气候温暖,冬无严寒
Ⅴ	北京、天津、河北、山西、山东、河南、陕西	2~4	温带、亚热带季风气候,夏季高温多雨,冬季寒冷干燥,干旱少雨
Ⅵ	辽宁、吉林、黑龙江	2~3	季风气候,四季分明,寒冷期长;雨量集中,风大,日照丰富
Ⅶ	西藏、甘肃、青海、宁夏、新疆	3~4	晴天多,日照时间长,少雨,干燥,冬寒夏热,夏季短促,气温高,秋季降温快
Ⅷ	内蒙古	6	风大少雨,昼夜温差大,日照时间充足,月降水变化大
Ⅸ	海南	9	四季不分明,夏无酷热,冬无严寒,气温年较差小,年平均气温高
Ⅹ	云南	25	年温差小,日温差大,干湿季节分明

类别	Ⅰ	Ⅱ	Ⅲ	Ⅳ	Ⅴ	Ⅵ	Ⅶ	Ⅷ	Ⅸ	Ⅹ	均值
年均气压系数	0.645	0.675	0.536	0.731	0.711	0.716	0.996	0.571	0.673	0.496	0.675
年均气温系数	0.742	0.694	0.638	0.669	0.751	0.617	0.470	0.718	0.609	0.573	0.648
年均降水量系数	-0.713	-0.665	-0.700	-0.728	-0.732	-0.613	-0.993	-0.752	-0.525	-0.600	-0.702
年均日照时间系数	0.692	0.689	0.606	0.746	0.672	0.595	0.558	0.642	0.654	0.611	0.646

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