气候变化领域集成服务门户(CCPortal): Topological data analysis reveals parameters with prognostic skill for extreme wildfire size

CCPortal

DOI	10.1088/1748-9326/aba8c2
	Topological data analysis reveals parameters with prognostic skill for extreme wildfire size
	Bendick R.; Hoylman Z.H.
发表日期	2020
ISSN	17489318
卷号	15 期号:10
英文摘要	A topological data analysis (TDA) of 200 000 U.S. wildfires larger than 5 acres indicates that events with the largest final burned areas are associated with systematically low fuel moistures, low precipitation, and high vapor pressure deficits in the 30 days prior to the fire start. These parameters are widely used in empirical fire forecasting tools, thus confirming that an unguided, machine learning (ML) analysis can reproduce known relationships. The simple, short time scale parameters identified can therefore provide quantifiable forecast skill for wildfires with extreme sizes. In contrast, longer aggregates of weather observations for the year prior to fire start, including specific humidity, normalized precipitation indices, average temperature, average precipitation, and vegetation indices are not strongly coupled to extreme fire size, thus afford limited or no enhanced forecast skill. The TDA demonstrates that fuel moistures and short-term weather parameters should optimize the training of ML algorithms for fire forecasting, whilst longer-term climate and ecological measures could be downweighted or omitted. The most useful short-term meteorological and fuels metrics are widely available with low latency for the conterminous U.S, and are not computationally intensive to calculate, suggesting that ML tools using these data streams may suffice to improve situational awareness for wildfire hazards in the U.S. © 2020 The Author(s). Published by IOP Publishing Ltd
英文关键词	Fire; Machine learning; Topological data analysis; Topology; United States
语种	英语
scopus关键词	Data streams; Fuels; Information analysis; Moisture; Topology; Weather forecasting; Ecological measures; Precipitation indices; Situational awareness; Specific humidity; Topological data analysis; Vapor pressure deficit; Weather observations; Weather parameters; Fires; data set; environmental factor; parameter estimation; vegetation type; wildfire; United States
来源期刊	Environmental Research Letters
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/153666
作者单位	Department of Geosciences, University of Montana, Missoula, MT, United States; Montana Climate Office, W.A. Franke College of Forestry and Conservation, University of Montana, Missoula, MT, United States
推荐引用方式 GB/T 7714	Bendick R.,Hoylman Z.H.. Topological data analysis reveals parameters with prognostic skill for extreme wildfire size[J],2020,15(10).
APA	Bendick R.,&Hoylman Z.H..(2020).Topological data analysis reveals parameters with prognostic skill for extreme wildfire size.Environmental Research Letters,15(10).
MLA	Bendick R.,et al."Topological data analysis reveals parameters with prognostic skill for extreme wildfire size".Environmental Research Letters 15.10(2020).