用反事实概率分析避免了野火影响建模

IF 2.7 3区 农林科学 Q2 ECOLOGY Frontiers in Forests and Global Change Pub Date : 2023-11-08 DOI:10.3389/ffgc.2023.1266413
Matthew P. Thompson, John F. Carriger
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引用次数: 0

摘要

评估燃料处理和其他减轻野火风险工作的有效性和绩效是一项具有挑战性的工作。也许最复杂的是量化可避免的影响。在本研究中,我们展示了概率反事实分析如何帮助绩效评估。我们借鉴了灾害风险缓解和气候事件归因文献中的见解来说明一个反事实框架,并提供了使用集合野火模拟的示例。具体来说,我们重新分析了以前发表的美国新墨西哥州火灾多发地区的火灾模拟数据,并展示了对燃料处理场景的事后分析和事前评估的应用。该方法发现,与未处理的场景相比,处理过的景观可能会降低火灾风险。最后,我们在理论和方法上为未来的扩展提供了思路。
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Avoided wildfire impact modeling with counterfactual probabilistic analysis
Assessing the effectiveness and measuring the performance of fuel treatments and other wildfire risk mitigation efforts are challenging endeavors. Perhaps the most complicated is quantifying avoided impacts. In this study, we show how probabilistic counterfactual analysis can help with performance evaluation. We borrow insights from the disaster risk mitigation and climate event attribution literature to illustrate a counterfactual framework and provide examples using ensemble wildfire simulations. Specifically, we reanalyze previously published fire simulation data from fire-prone landscapes in New Mexico, USA, and show applications for post-event analysis as well as pre-event evaluation of fuel treatment scenarios. This approach found that treated landscapes likely would have reduced fire risk compared to the untreated scenarios. To conclude, we offer ideas for future expansions in theory and methods.
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来源期刊
CiteScore
4.50
自引率
6.20%
发文量
256
审稿时长
12 weeks
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