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Simulating seasonal weather influences on wildfire behavior in Glacier National Park, Montana

机译:模拟季节性天气影响蒙大拿州冰川国家公园的野火行为

摘要

Wildfires play a critical role in ecosystem functionality throughout Glacier National Park (GNP), but require accurate modeling to mitigate risks to human lives and property. The process of modeling fire behavior is a computationally intensive, multi-scalar effort involving approximation of interactions between wind, climate, fuel sources, and the fire itself. Despite its importance to understanding fire behavior, the most commonly used fire model (FARSITE) does not integrate fire-weather feedback. My analysis provides a deeper understanding of the seasonal behavior of fire in GNP by comparing the spread of numerous simulated fires during the height of summer against the end of the fire season in October. To explore the variance caused by each model’s treatment of local weather feedbacks, I compare the commonly used FARSITE model—which is easy to use, but relies on steady state temperature and wind inputs—to the performance of the experimental WRF-FIRE model—which requires supercomputing capabilities, but provides the ability to model advanced weather dynamics and feedback loops at multiple spatial and time scales. As an intermediate approach, I added diurnal and orographic wind influences to FARSITE with the WindNinja extension. I ran all models for a 24-hour period for two time periods, on 1 July and 20 October 2013, to determine the relative difference in burned area over the fire season. Across all time intervals, the July runs demonstrate a greater area burned than in October. In addition to reducing seasonal variability, the addition of feedback mechanisms cause WRF-FIRE to predict overall more area burned and a faster rate of spread than with FARSITE. This pattern continues with the addition of diurnal and orographic wind dynamics with WindNinja, generating nearly twice of the total area burned compared to the standard FARSITE model. These results demonstrate that the fire-wind relationship is critical for accurately modeling the impact of wildfires, and that fire-weather feedbacks largely override the impacts of seasonal climatic factors. The results of these simulations provide powerful information to fire managers and ecologists in Glacier National Park, suggesting that models using wind dynamics are essential for understanding the impact of fire in the Northern Rocky Mountains.
机译:野火在整个冰川国家公园(GNP)的生态系统功能中扮演着至关重要的角色,但需要精确的建模来减轻对人类生命和财产的风险。对火灾行为进行建模的过程是一个计算密集型的多尺度工作,涉及风,气候,燃料源和火灾本身之间的近似相互作用。尽管它对理解火灾行为很重要,但最常用的火灾模型(FARSITE)并未集成火灾天气反馈。我的分析通过比较夏季高峰期间与十月份火灾季节末期的大量模拟火灾的蔓延,对GNP中火灾的季节性行为有更深入的了解。为了探究每种模型对本地天气反馈的处理所引起的差异,我将常用的FARSITE模型(易于使用,但依赖于稳态温度和风输入)与实验WRF-FIRE模型的性能进行了比较,该模型需要超级计算功能,但提供了在多个空间和时间尺度上对高级天气动态和反馈回路进行建模的功能。作为一种中间方法,我通过WindNinja扩展将日风和地形风影响添加到FARSITE。我在2013年7月1日和10月20日对所有模型进行了24小时,两个时间段的运行,以确定火灾季节燃烧面积的相对差异。在所有时间间隔内,7月的跑步记录显示比10月的燃烧面积更大。除了减少季节性变化之外,反馈机制的加入还使得WRF-FIRE能够预测总体燃烧面积和蔓延速度比FARSITE快得多。通过使用WindNinja增加昼夜和地形风动力学,这种模式得以继续,与标准FARSITE模型相比,其燃烧面积几乎是总面积的两倍。这些结果表明,火风关系对于准确模拟野火的影响至关重要,而火天气的反馈在很大程度上覆盖了季节性气候因素的影响。这些模拟的结果为冰川国家公园的消防管理人员和生态学家提供了有力的信息,表明使用风动力学的模型对于理解北洛矶山脉火灾的影响至关重要。

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    DiBiase Anthony;

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  • 年度 2014
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