Modernizing the US National Fire Danger Rating System (version 4): Simplified fuel models and improved live and dead fuel moisture calculations

IF 4.8 2区 环境科学与生态学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Environmental Modelling & Software Pub Date : 2024-08-13 DOI:10.1016/j.envsoft.2024.106181
W. Matt Jolly , Patrick H. Freeborn , Larry S. Bradshaw , Jon Wallace , Stuart Brittain
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Abstract

The US National Fire Danger Rating System (USNFDRS) supports wildfire management decisions nationwide, but it has not been updated since 1988. Here we implement new fuel moisture models, and we simplify the fuel models while maintaining the overall USNFDRS structure. Modeled and measured live fuel moisture content values were highly correlated (r2=0.629 with defaults and r2=0.693 when species and location optimized). We also consolidated fuel models to five fuel types that eliminated significant index cross-correlation. Index seasonality compared between old (V2) and new USNFDRS models (v4) across six US National Forests was very similar (ρ= 0.97). V4 was as good or better than V2 at predicting fire days in 92% of the cases tested and V4 effectively predicted wildfire days and large fire ignition days (AUCs 0.647 to 0.915). USNFDRS V4 can adequately depict spatial and temporal wildland fire potential and it can be adapted for worldwide use.

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更新美国国家火灾危险分级系统(第 4 版):简化燃料模型,改进活燃料和死燃料湿度计算
美国国家火灾危险性分级系统(USNFDRS)为全国范围内的野火管理决策提供支持,但自 1988 年以来一直没有更新过。在此,我们采用了新的燃料水分模型,并在保持 USNFDRS 整体结构的前提下简化了燃料模型。模型和测量的活燃料含水量值高度相关(默认值 r2=0.629,物种和位置优化后 r2=0.693)。我们还将燃料模型合并为五种燃料类型,从而消除了显著的指数交叉相关性。在六个美国国家森林中,新旧 USNFDRS 模型(v4)之间的指数季节性比较非常相似(ρ= 0.97)。在 92% 的测试案例中,V4 在预测火灾日方面与 V2 一样好或更好,并且 V4 能有效预测野火日和大火点火日(AUC 为 0.647 至 0.915)。USNFDRS V4 可以充分描述空间和时间上的野地火灾隐患,可在全球范围内使用。
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来源期刊
Environmental Modelling & Software
Environmental Modelling & Software 工程技术-工程:环境
CiteScore
9.30
自引率
8.20%
发文量
241
审稿时长
60 days
期刊介绍: Environmental Modelling & Software publishes contributions, in the form of research articles, reviews and short communications, on recent advances in environmental modelling and/or software. The aim is to improve our capacity to represent, understand, predict or manage the behaviour of environmental systems at all practical scales, and to communicate those improvements to a wide scientific and professional audience.
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