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.

大型野外大火威胁着野生世界和城市环境边缘的社区。例如，2017年10月，北加州野外大火摧毁了8900多个建筑物，并杀死了44名平民，预计损失超过94亿美元。在过去的十年中，这类火灾的破坏率有所提高。调查人员将使用数值模型来模拟野外大火向社区的传播。他们将使用国家标准技术研究所（NIST）和加利福尼亚森林和消防部（Calfire）的数据测试这些模型。这些发现将与研究界，联邦事件经理，当地社区计划者和公众共享。该项目将鼓励人们通过外展活动和本科研究机会从事野外消防研究。调查人员将通过在国际会议上介绍结果，例如欧洲地球物理联盟（EGU），国际燃烧研讨会（澳大利亚阿德莱德，2020年）和国际消防安全科学研讨会（加拿大加拿大滑铁卢，2020年，2020年）。这个项目将对Fires of Fires interface建模。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，风以及在特定位置的品牌堆积。该研究计划是使用最近的文献中合成的信息来修改现有的随机野生火灾模型（Firefly）。研究人员将检查火灾周围的观察数据并观察到火灾传播，并使用这些观察结果来验证模型改进。萤火虫模型的提高预测能力在实际火灾事件期间的计划和应急响应社会中可能很有用。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的影响评估标准通过评估来获得支持的。