气候变化领域集成服务门户(CCPortal): Estimating the influence of field inventory sampling intensity on forest landscape model performance for determining high-severity wildfire risk

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DOI	10.1038/s41598-024-53359-8
	Estimating the influence of field inventory sampling intensity on forest landscape model performance for determining high-severity wildfire risk
	Hecht, Hagar; Krofcheck, Dan J.; Carril, Dennis; Hurteau, Matthew D.
发表日期	2024
ISSN	2045-2322
起始页码	14
结束页码	1
卷号	14 期号:1
英文摘要	Historically, fire has been essential in Southwestern US forests. However, a century of fire-exclusion and changing climate created forests which are more susceptible to uncharacteristically severe wildfires. Forest managers use a combination of thinning and prescribed burning to reduce forest density to help mitigate the risk of high-severity fires. These treatments are laborious and expensive, therefore optimizing their impact is crucial. Landscape simulation models can be useful in identifying high risk areas and assessing treatment effects, but uncertainties in these models can limit their utility in decision making. In this study we examined underlying uncertainties in the initial vegetation layer by leveraging a previous study from the Santa Fe fireshed and using new inventory plots from 111 stands to interpolate the initial forest conditions. We found that more inventory plots resulted in a different geographic distribution and wider range of the modelled biomass. This changed the location of areas with high probability of high-severity fires, shifting the optimal location for management. The increased range of biomass variability from using a larger number of plots to interpolate the initial vegetation layer also influenced ecosystem carbon dynamics, resulting in simulated forest conditions that had higher rates of carbon uptake. We conclude that the initial forest layer significantly affects fire and carbon dynamics and is dependent on both number of plots, and sufficient representation of the range of forest types and biomass density.
语种	英语
WOS研究方向	Science & Technology - Other Topics
WOS类目	Multidisciplinary Sciences
WOS记录号	WOS:001158746700053
来源期刊	SCIENTIFIC REPORTS
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/298372
作者单位	United States Department of Energy (DOE); Sandia National Laboratories; United States Department of Agriculture (USDA); United States Forest Service; University of New Mexico
推荐引用方式 GB/T 7714	Hecht, Hagar,Krofcheck, Dan J.,Carril, Dennis,et al. Estimating the influence of field inventory sampling intensity on forest landscape model performance for determining high-severity wildfire risk[J],2024,14(1).
APA	Hecht, Hagar,Krofcheck, Dan J.,Carril, Dennis,&Hurteau, Matthew D..(2024).Estimating the influence of field inventory sampling intensity on forest landscape model performance for determining high-severity wildfire risk.SCIENTIFIC REPORTS,14(1).
MLA	Hecht, Hagar,et al."Estimating the influence of field inventory sampling intensity on forest landscape model performance for determining high-severity wildfire risk".SCIENTIFIC REPORTS 14.1(2024).