气候变化领域集成服务门户(CCPortal): Spatial Wildfire Risk Modeling Using a Tree-Based Multivariate Generalized Pareto Mixture Model

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DOI	10.1007/s13253-023-00596-5
	Spatial Wildfire Risk Modeling Using a Tree-Based Multivariate Generalized Pareto Mixture Model
	Cisneros, Daniela; Hazra, Arnab; Huser, Raphael
发表日期	2024
ISSN	1085-7117
EISSN	1537-2693
英文摘要	Wildfires pose a severe threat to the ecosystem and economy, and risk assessment is typically based on fire danger indices such as the McArthur Forest Fire Danger Index (FFDI) used in Australia. Studying the joint tail dependence structure of high-resolution spatial FFDI data is thus crucial for estimating current and future extreme wildfire risk. However, existing likelihood-based inference approaches are computationally prohibitive in high dimensions due to the need to censor observations in the bulk of the distribution. To address this, we construct models for spatial FFDI extremes by leveraging the sparse conditional independence structure of Husler-Reiss-type generalized Pareto processes defined on trees. These models allow for a simplified likelihood function that is computationally efficient. Our framework involves a mixture of tree-based multivariate generalized Pareto distributions with randomly generated tree structures, resulting in a flexible model that can capture nonstationary spatial dependence structures. We fit the model to summer FFDI data from different spatial clusters in Mainland Australia and 14 decadal windows between 1999 and 2022 to study local spatiotemporal variability with respect to the magnitude and extent of extreme wildfires. Our proposed method fits the margins and spatial tail dependence structure adequately and is helpful in providing extreme wildfire risk estimates. Our results identify a significant increase in spatially aggregated fire risk across a substantially large portion of Mainland Australia, which raises serious climatic concerns. Supplementary material to this paper is provided online.
英文关键词	Climate change; Graphical model; Generalized Pareto process; Husler-Reiss distribution; McArthur forest fire danger index; Spatial extreme; Wildfire risk assessment
语种	英语
WOS研究方向	Life Sciences & Biomedicine - Other Topics ; Mathematical & Computational Biology ; Mathematics
WOS类目	Biology ; Mathematical & Computational Biology ; Statistics & Probability
WOS记录号	WOS:001168026700001
来源期刊	JOURNAL OF AGRICULTURAL BIOLOGICAL AND ENVIRONMENTAL STATISTICS
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/298565
作者单位	King Abdullah University of Science & Technology; Indian Institute of Technology System (IIT System); Indian Institute of Technology (IIT) - Kanpur
推荐引用方式 GB/T 7714	Cisneros, Daniela,Hazra, Arnab,Huser, Raphael. Spatial Wildfire Risk Modeling Using a Tree-Based Multivariate Generalized Pareto Mixture Model[J],2024.
APA	Cisneros, Daniela,Hazra, Arnab,&Huser, Raphael.(2024).Spatial Wildfire Risk Modeling Using a Tree-Based Multivariate Generalized Pareto Mixture Model.JOURNAL OF AGRICULTURAL BIOLOGICAL AND ENVIRONMENTAL STATISTICS.
MLA	Cisneros, Daniela,et al."Spatial Wildfire Risk Modeling Using a Tree-Based Multivariate Generalized Pareto Mixture Model".JOURNAL OF AGRICULTURAL BIOLOGICAL AND ENVIRONMENTAL STATISTICS (2024).