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    宫大鹏, 康峰峰, 刘晓东. 新巴尔虎草原火时空分布特征及对气象因子响应[J]. 北京林业大学学报, 2018, 40(2): 82-89. DOI: 10.13332/j.1000-1522.20170402
    引用本文: 宫大鹏, 康峰峰, 刘晓东. 新巴尔虎草原火时空分布特征及对气象因子响应[J]. 北京林业大学学报, 2018, 40(2): 82-89. DOI: 10.13332/j.1000-1522.20170402
    Gong Da-peng, Kang Feng-feng, Liu Xiao-dong. Spatial and temporal distribution patterns of grassland fire and its response to meteorological factors in XinBarag Prairie of northwestern China[J]. Journal of Beijing Forestry University, 2018, 40(2): 82-89. DOI: 10.13332/j.1000-1522.20170402
    Citation: Gong Da-peng, Kang Feng-feng, Liu Xiao-dong. Spatial and temporal distribution patterns of grassland fire and its response to meteorological factors in XinBarag Prairie of northwestern China[J]. Journal of Beijing Forestry University, 2018, 40(2): 82-89. DOI: 10.13332/j.1000-1522.20170402

    新巴尔虎草原火时空分布特征及对气象因子响应

    Spatial and temporal distribution patterns of grassland fire and its response to meteorological factors in XinBarag Prairie of northwestern China

    • 摘要:
      目的草原火是草原生态系统重要的干扰因子,严重影响着系统的结构与功能。新巴尔虎草原草是我国重要的草原生态系统,随着极端气候事件的频发,加剧了该地区发生重特大草原火灾的可能性。本研究可为草原火的火险区划和管理提供科学依据。
      方法本研究基于遥感影像,借助地理信息系统和ENVI等软件,分析了2001—2016年新巴尔虎草原火时空特征,并结合气象数据,探讨草原过火面积对气象因子的响应特性。
      结果新巴尔虎草原火在时间和空间上呈现规律性分布。在时间上,过火面积年变化存周期性变化规律,2003年和2013年分别出现过火面积峰值,周期间隔10年;过火面积月变化差异明显,过火面积集中在4、5月和9、10月,时间对应研究区域火灾频发的春秋两季;在空间上,高频度火灾发生区域全部分布于国境线附近,越境火灾风险比较大。草原火过火面积与气象因子有着密切关系。在月平均气温为0~10 ℃、月总降水量在0~20 mm、月平均相对湿度40%以下及月平均风速大于5 m/s的区域最利于草原火灾的发生和蔓延。
      结论新巴尔虎草原火预防工作在区域上应集中在新巴尔虎草原西部、东南部和东北部地区,加强边境地区草原火的监测和管理。在时间上应关注每年4、5月和9、10月,特别是月平均气温0~10 ℃、月总降水量0~20 mm、月平均相对湿度40%以下及月平均风速大于5 m/s的地区。未来可从更长的时间和更广的空间尺度来对草原火的发生周期进行模拟和预测,更有效地探究野火的长期动态变化规律及其影响因素。

       

      Abstract:
      ObjectiveGrassland fire is a key disturbance factor in natural ecosystems and it greatly influences ecosystem structure and function. XinBarag Prairie of northwestern China is one of the important grassland ecosystems in China. With the frequent occurrence of extreme weather events, more extra serious wildfires will occur in this region. This study could provide a scientific basis for zoning grassland fire risks and wildfire management.
      MethodIn this study, the spatial and temporal patterns of wildfires in XinBarag Prairie were analyzed using statistical software, GIS and ENVI based on MODIS data from 2001 to 2016. Through the integration of meteorological data, the impact of meteorological factors on the characteristics of monthly variations in grassland fire was investigated.
      ResultXinBarag Prairie had regular distribution in the spatial and temporal patterns of wildfires. In the temporal patterns, annual burned area had a fluctuation period of 10 years generally in the whole region. The monthly burned area had significant differences among various months and the burned area was mainly distributed in April, May, September and October. The time corresponds to spring and autumn, in which fire disaster occurred frequently. In spatial patterns, the high frequencies of wildfires were all widely distributed near the country s borders. The large-scale fires crossing the border line will pose a serious threat to the steppe in China; There were close relations between grassland fire and meteorological factors. Grassland fires easily occurred and spreaded in areas with monthly average temperature ranged between 0 and 10 ℃, monthly total rainfall ranged between 0 and 20 mm, monthly mean relative humidity was below 40% and monthly mean wind speed was above 5 m/s.
      ConclusionThe preventing region of grassland fire in XinBarag Prairie should concentrate in its western, southeastern and northeastern regions, and the monitoring and management of grassland fire in border region should be strengthened.In time, we should concern April, May, September and October, especially the areas with monthly average temperature between 0 and 10 ℃, monthly total rainfall between 0 and 20 mm, monthly mean relative humidity below 40% and monthly mean wind speed above 5 m/s. In addition, future research will focus on the fluctuation period of grassland fire from longer temporal and wider spatial scale to explore the dynamic change of wildfires and its influencing factors more effectively.

       

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