PREEVENTS Track 2: Fire Spread at the Wildland-Urban Interface (WUI) Modeling and Data Assimilation for Prediction and Risk assessment (WUI MAPR)

预防轨道 2：荒地与城市交界处的火灾蔓延 (WUI) 用于预测和风险评估的建模和数据同化 (WUI MAPR)

• 批准号: 1854952
• 负责人: Arnaud Trouve
• 金额: $ 137.77万
• 依托单位: University of Maryland, College Park
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-07-01 至 2024-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1854952&HistoricalAwards=false
• 关键词: PREEVENTS; Track; Fire; Spread; Wildland

Large wildland fires threaten communities at the margins of wildlands and the urban environment. For example, the Northern California wildland fires in October 2017 destroyed over 8900 structures and killed 44 civilians with projected losses exceeding $9.4 billion. The rate of destruction from these types of fires has increased over the last decade. The investigators will use numerical models to simulate the spread of wildland fires into communities. They will test these models using data from the National Institute for Standards and Technology (NIST) and the California Department of Forest and Fire Protection (CalFIRE). The findings will be shared with the research community, federal incident managers, local community planners, and the public. The project will encourage people to engage in wildland fire research through outreach events and undergraduate research opportunities. The investigators will promote international collaboration by presenting their results at international meetings, such as the European Geophysical Union (EGU), the International Symposium on Combustion (Adelaide, Australia, 2020) and International Symposium on Fire Safety Science (Waterloo, Canada, 2020).This project will model brands or embers and how they contribute to the spread of fires at the Wildland-Urban Interface. This is a challenging research problem because there are different fuels, different types of brands, winds, and the pile up of brands in specific locations. The research plan is to modify an existing stochastic wildland fire model (FIREFLY) using information synthesized from the recent literature. The investigators will examine observational data of fire perimeters and observed fire spread, and use these observations to validate model improvements. The improved predictive capability of the FIREFLY model could be useful in society for planning and emergency response during real fire events.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

大规模的野火威胁着荒地边缘的社区和城市环境。例如，2017年10月的北方加州野火摧毁了8900多座建筑物，造成44名平民死亡，预计损失超过94亿美元。在过去十年中，这类火灾造成的破坏率有所增加。研究人员将使用数值模型来模拟野火向社区的蔓延。他们将使用来自美国国家标准与技术研究所（NIST）和加州森林和消防部门（CalFIRE）的数据来测试这些模型。调查结果将与研究界、联邦事故管理人员、当地社区规划人员和公众分享。该项目将鼓励人们通过外联活动和本科生研究机会从事荒地火灾研究。研究人员将通过在欧洲地球物理联盟（EGU）、国际燃烧研讨会（阿德莱德，澳大利亚，2020年）和国际消防安全科学研讨会（滑铁卢，加拿大，2020年）等国际会议上展示他们的研究成果来促进国际合作。该项目将对品牌或余烬进行建模，以及它们如何在荒地-城市界面促进火灾的蔓延。这是一个具有挑战性的研究问题，因为有不同的燃料，不同类型的品牌，风，以及在特定位置堆积的品牌。研究计划是修改现有的随机荒地火灾模型（FIREFLY），使用最近的文献中合成的信息。研究人员将检查火灾周边的观测数据和观测到的火灾蔓延，并使用这些观测结果来验证模型的改进。FIREFLY模型改进后的预测能力在真实的火灾事件中可用于社会规划和应急响应。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。