PostBP: A Python library to analyze outputs from wildfire growth models.

Autor:	Liu N; Natural Resources Canada, Canadian Forest Service, Great Lakes Forestry Centre, 1219 Queen Street East, Sault Ste. Marie, ON, Canada., Yemshanov D; Natural Resources Canada, Canadian Forest Service, Great Lakes Forestry Centre, 1219 Queen Street East, Sault Ste. Marie, ON, Canada., Parisien MA; Natural Resources Canada, Canadian Forest Service, Northern Forestry Centre, 5320 122 Street Northwest, Edmonton, AB, Canada., Stockdale C; Natural Resources Canada, Canadian Forest Service, Northern Forestry Centre, 5320 122 Street Northwest, Edmonton, AB, Canada., Moore B; Natural Resources Canada, Canadian Forest Service, Northern Forestry Centre, 5320 122 Street Northwest, Edmonton, AB, Canada., Koch FH; USDA Forest Service, Southern Research Station, Eastern Forest Environmental Threat Assessment Center, 3041 East Cornwallis Road, Research Triangle Park, NC 27709, USA.
Jazyk:	angličtina
Zdroj:	MethodsX [MethodsX] 2024 Jun 21; Vol. 13, pp. 102816. Date of Electronic Publication: 2024 Jun 21 (Print Publication: 2024).
DOI:	10.1016/j.mex.2024.102816
Abstrakt:	Wildfire is an important natural disturbance agent in Canadian forests, but it has also caused significant economic damage nationwide. Spatial fire growth models have emerged as important tools for representing wildfire dynamics across diverse landscapes, enabling the mapping of key wildfire hazard metrics such as location-specific burn probabilities or likelihoods of fire ignition. While these summary metrics have gained popularity, they often fall short in capturing the directional spread of wildfires and their potential spread distances. The metrics depicting the directional spread of wildfire can be derived from raw outputs generated with fire growth models, such as the perimeters and ignition locations of individual fires, but extracting this information requires complex data processing. To address this data gap, we present PostBP, an open-source Python package designed for post-processing the raw outputs of fire growth models - the ignition locations and perimeters of individual fires simulated over multiple stochastic iterations - into a matrix of fire spread likelihoods between all pairs of forest patches in a landscape. The PostBP also generates several other summary outputs, such as the source-sink ratio and the fire spread rose diagram. We provide an overview of PostBP 's capabilities and demonstrate its practical application to a forested landscape.•Wildfire growth models generate large amounts of outputs, which are hard to summarize for practical decision-making.•The PostBP package calculates the summary metrics characterizing the directional spread of wildfires.•The fire risk summaries generated with PostBP can support the assessments of wildfire risk and mitigation measures. Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. (Crown Copyright © 2024 Published by Elsevier B.V.)
Databáze:	MEDLINE
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