Predicting wildfire occurrence distribution with spatial point process models and its uncertainty assessment: a case study in the Lake Tahoe Basin, USA

doi:10.1071/WF14001

	Predicting wildfire occurrence distribution with spatial point process models and its uncertainty assessment: a case study in the Lake Tahoe Basin, USA
	Loudermilk, E. Louise 3; Scheller, Robert M.3; Stanton, Alison ; Skinner, Carl 2; Yang, Jian 1,4,5; Dilts, Thomas E.4; Weisberg, Peter J.4
刊名	INTERNATIONAL JOURNAL OF WILDLAND FIRE
	2015
卷号	24 期号:3 页码:380-390
关键词	Climatic Water Deficit Model Uncertainty Multi-model Inference Predictive Modelling Spatial Point Process
ISSN号	1049-8001
DOI	10.1071/WF14001
英文摘要	Strategic fire and fuel management planning benefits from detailed understanding of how wildfire occurrences are distributed spatially under current climate, and from predictive models of future wildfire occurrence given climate change scenarios. In this study, we fitted historical wildfire occurrence data from 1986 to 2009 to a suite of spatial point process (SPP) models with a model averaging approach. We then predicted human-and lightning-caused wildfire occurrence over the 2010-2100 period in the Lake Tahoe Basin, a forested watershed in the western US with an extensive wildland-urban interface. The purpose of our research was threefold, including (1) to quantify the influence of biophysical and anthropogenic explanatory variables on spatial patterns of wildfire occurrence, (2) to model current and future spatial distribution of wildfire occurrence under two carbon emission scenarios (A2 and B1), and (3) to assess prediction uncertainty due to model selection. We found that climate variables exerted stronger influences on lightning-caused fires, with climatic water deficit the most important climatic variable for both human-and lightning-caused fires. The recent spatial distribution of wildfire hotspots was mainly constrained by anthropogenic factors because most wildfires were human-caused. The future distribution of hotspots (i.e. places with high fire occurrence density), however, was predicted to shift to higher elevations and ridge tops due to a more rapid increase of lightning-caused fires. Landscape-scale mean fire occurrence density, averaged from our top SPP models with similar empirical support, was predicted to increase by 210% and 70% of the current level under the A2 and B1 scenarios. However, individual top SPP models could lead to substantially different predictions including a small decrease, a moderate increase, and a very large increase, demonstrating the critical need to account for model uncertainty.
WOS研究方向	Forestry
语种	英语
出版者	CSIRO PUBLISHING
WOS记录号	WOS:000354808600010
内容类型	期刊论文
源URL	[http://210.72.129.5/handle/321005/122033]
专题	中国科学院沈阳应用生态研究所
通讯作者	Yang, Jian
作者单位	1.Chinese Acad Sci, Inst Appl Ecol, State Key Lab Forest & Soil Ecol, Shenyang 110164, Peoples R China 2.US Forest Serv, Pacific Southwest Res Stn, USDA, Redding, CA 96002 USA 3.Portland State Univ, Environm Sci & Management Dept, Portland, OR 97207 USA 4.Univ Nevada, Dept Nat Resources & Environm Sci, Reno, NV 89557 USA 5.Univ Kentucky, Dept Forestry, Lexington, KY 40546 USA
推荐引用方式 GB/T 7714	Loudermilk, E. Louise,Scheller, Robert M.,Stanton, Alison,et al. Predicting wildfire occurrence distribution with spatial point process models and its uncertainty assessment: a case study in the Lake Tahoe Basin, USA[J]. INTERNATIONAL JOURNAL OF WILDLAND FIRE,2015,24(3):380-390.
APA	Loudermilk, E. Louise.,Scheller, Robert M..,Stanton, Alison.,Skinner, Carl.,Yang, Jian.,...&Weisberg, Peter J..(2015).Predicting wildfire occurrence distribution with spatial point process models and its uncertainty assessment: a case study in the Lake Tahoe Basin, USA.INTERNATIONAL JOURNAL OF WILDLAND FIRE,24(3),380-390.
MLA	Loudermilk, E. Louise,et al."Predicting wildfire occurrence distribution with spatial point process models and its uncertainty assessment: a case study in the Lake Tahoe Basin, USA".INTERNATIONAL JOURNAL OF WILDLAND FIRE 24.3(2015):380-390.