黑河地区森林火灾发生规律的研究
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摘要
黑河地区是我国森林火灾的多发地区,森林火灾对林业资源造成了巨大的破坏,林火的预测预报研究一直是林火研究的重点,能够掌握林火发生发展的有关规律,是科学有效的开展防火灭火工作的重要保障。森林火灾过火面积预测的研究对于黑河地区的林火预测预报具有重要的意义。
本文对黑河地区近37年的森林火灾发生情况进行了分析,得出火灾发生的规律和特点；通过对森林火灾发生的历史资料以及相应的气象资料的分析处理,利用时间序列分析建立了森林火灾过火面积和火灾发生次数预测模型,并对此模型进行了检验和验正。
1959-2008年黑河地区各台站的年均温各台站年均温呈显著升高趋势,降水量呈下降趋势,但不显著。黑河地区月均温最高出现在7月,最低气温出现在1月。降水量7月份最大,最低值出现在2月。四季气温变化幅度存在一定的差异,但是却具有相同的变化趋势,其中春季平均温度增幅最大；秋季平均温度增幅最小。1959-2000年月平均相对湿度和月平均风速,春季(3-5月)的相对湿度最低,7、8月最高,风速的变化月相对湿度的变化趋势相反。
黑河地区1971-2008年发生森林火灾较频繁,39次／年,起火原因不明的火灾占大多,已知原因火灾以人为火灾为主。
黑河地区的森林火灾主要发生在3、4、5、6、9、10月,其中4、5、6月森林火灾面积和次数差异不显著,显著高于3、9、10月。
通过建立时间序列模型分别对该地区的森林火灾过火面积进行预测,结果表明：时间序列模型对火灾发生次数的预测效果较好,说明时间序列分析对历史火灾发生次数数据样本有较好的适应性,通过时间序列分析可以把实时气象数据样本带入模型,对火灾的发生次数和破坏面积进行预测,达到较好的预测效果,但是其预测精度略逊于火灾发生次数时间序列模型。
Heihe region is one of forest fire-prone areas in China. Forest fires have caused temendous damage of forest resources, so the prediction of forest fires has been the focus study on forest fires. Knowing the relevant rules of bringing out and developing of forest fires is an important project to carrying out fighting and protecting fires scientifically and effectively. The study on the prediction of forest burnt areas is of great significance in prediction of forest fres of Heihe region.
This paper analyzed the incidence of forest fires in Heihe region over the past 37 years aid concluded the rules and characteristics of forest fires; based on analyzing and processing historical data of forest fires and corresponding meteorological data, the paper established the forecasting models of forest burnt area and forest fires frequency using time series analysis, further tested and verified the two models.
The data from in Heihe region stations show, from 1959 to 2008, there are significantly higher trends in annual average temperature and downward trend in precipitation, but the latter is not very significant; the maximum monthly temperature is in July and minimum temperature is in January; Maximum precipitation is in July, the lowest value is in February; Range of seasonal temperature changes exits some differences, but does the same trend, in which the largest increase in average temperature is in spring; the smallest increase in average temperature is in autumn.
In Heihe region, forest fires occurred most frequently between 1971 and 2008,39 times average one year. Fires of unknown cause is majority, most of fires of known causes are caused by man. Forest fires in the region is mainly in March, April, May, June, September, October, of which in April, May, June, the difference of the frequent and burnt areas of forest fires is no significant, and in March, September, October, the difference is more significant.
Using time series forest burnt area model and time series forest fires frequency model to forecast separately, the results show that time series forest fires frequency model is better, and illustrate the time series analysis has better adaptability to historical fire occurrence data samples, real-time weather data samples can be brought into the model to predict the number and the damage area of fires which can achieve good results, but the prediction accuracy is slightly worse than the time series fire occurrence model.
本文对黑河地区近37年的森林火灾发生情况进行了分析,得出火灾发生的规律和特点；通过对森林火灾发生的历史资料以及相应的气象资料的分析处理,利用时间序列分析建立了森林火灾过火面积和火灾发生次数预测模型,并对此模型进行了检验和验正。
1959-2008年黑河地区各台站的年均温各台站年均温呈显著升高趋势,降水量呈下降趋势,但不显著。黑河地区月均温最高出现在7月,最低气温出现在1月。降水量7月份最大,最低值出现在2月。四季气温变化幅度存在一定的差异,但是却具有相同的变化趋势,其中春季平均温度增幅最大；秋季平均温度增幅最小。1959-2000年月平均相对湿度和月平均风速,春季(3-5月)的相对湿度最低,7、8月最高,风速的变化月相对湿度的变化趋势相反。
黑河地区1971-2008年发生森林火灾较频繁,39次／年,起火原因不明的火灾占大多,已知原因火灾以人为火灾为主。
黑河地区的森林火灾主要发生在3、4、5、6、9、10月,其中4、5、6月森林火灾面积和次数差异不显著,显著高于3、9、10月。
通过建立时间序列模型分别对该地区的森林火灾过火面积进行预测,结果表明：时间序列模型对火灾发生次数的预测效果较好,说明时间序列分析对历史火灾发生次数数据样本有较好的适应性,通过时间序列分析可以把实时气象数据样本带入模型,对火灾的发生次数和破坏面积进行预测,达到较好的预测效果,但是其预测精度略逊于火灾发生次数时间序列模型。
Heihe region is one of forest fire-prone areas in China. Forest fires have caused temendous damage of forest resources, so the prediction of forest fires has been the focus study on forest fires. Knowing the relevant rules of bringing out and developing of forest fires is an important project to carrying out fighting and protecting fires scientifically and effectively. The study on the prediction of forest burnt areas is of great significance in prediction of forest fres of Heihe region.
This paper analyzed the incidence of forest fires in Heihe region over the past 37 years aid concluded the rules and characteristics of forest fires; based on analyzing and processing historical data of forest fires and corresponding meteorological data, the paper established the forecasting models of forest burnt area and forest fires frequency using time series analysis, further tested and verified the two models.
The data from in Heihe region stations show, from 1959 to 2008, there are significantly higher trends in annual average temperature and downward trend in precipitation, but the latter is not very significant; the maximum monthly temperature is in July and minimum temperature is in January; Maximum precipitation is in July, the lowest value is in February; Range of seasonal temperature changes exits some differences, but does the same trend, in which the largest increase in average temperature is in spring; the smallest increase in average temperature is in autumn.
In Heihe region, forest fires occurred most frequently between 1971 and 2008,39 times average one year. Fires of unknown cause is majority, most of fires of known causes are caused by man. Forest fires in the region is mainly in March, April, May, June, September, October, of which in April, May, June, the difference of the frequent and burnt areas of forest fires is no significant, and in March, September, October, the difference is more significant.
Using time series forest burnt area model and time series forest fires frequency model to forecast separately, the results show that time series forest fires frequency model is better, and illustrate the time series analysis has better adaptability to historical fire occurrence data samples, real-time weather data samples can be brought into the model to predict the number and the damage area of fires which can achieve good results, but the prediction accuracy is slightly worse than the time series fire occurrence model.
引文
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