LEAP-HI: Fighting Wildfires: A Data-Informed, Physics-Based Computational Framework for Probabilistic Risk Assessment and Mitigation and Emergency Response Management

LEAP-HI：扑灭野火：基于数据的、基于物理的概率风险评估、缓解和应急响应管理计算框架

• 批准号: 1953333
• 负责人: Hamed Ebrahimian
• 金额: $ 200万
• 依托单位: Board of Regents, NSHE, obo University of Nevada, Reno
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1953333&HistoricalAwards=false
• 关键词: LEAP; HI; Fighting; Wildfires; Data

Destruction caused by wildfires in the US has significantly increased in the past two decades. While the federal government’s spending on wildfire fighting has been steadily increasing, wildfire severity has also been on the rise. The focus of this Leading Engineering for America's Prosperity, Health, and Infrastructure (LEAP-HI) project is the creation an overarching computational platform for wildfire risk management at multiple space and time scales. This vision will be accomplished by creating and integrating transdisciplinary scientific techniques in the fields of data analytics, computational modeling, and model-based inference. The objective is to develop scientific foundations for a live digital platform that evolves with new data and dynamically updates the long-term (seasons/months ahead) to short-term (weeks/days ahead) pre-ignition fire risks at regional and community scales, and predicts the post-ignition fire behavior in near-real-time at the fire front. Once developed, the computational platform will increase the efficiency of wildfire management process by providing actionable information to decision-makers for pre-ignition risk mitigation and post-ignition emergency response management. Involvement of key stakeholders and utility companies, preparation of future workforce, and K-12 outreach programs are integral parts of the project.The research promises to provide a rigorous computational approach to quantifying and predicting wildfire risk. Scientific advancements that are anticipated include: (i) an overarching computational platform for probabilistic wildfire loss assessment at different spatial and temporal scales that evolves with data as they become available; (ii) an integrated simulation framework including a wildfire model, urban-fire model, and socioeconomic model to predict the wildfire loss in terms of economic and social losses; (iii) a novel data-driven modeling approach for urban-fire simulation, and a new empirical model for change in quality-of-life (QoL) due to wildfire; (iv) new data collection modules and advanced data processing techniques to collect refined data, process and infuse different sources of data, and quantify uncertainty in the measurement data; and, (v) a Bayesian model inference framework to quantify modeling uncertainties and update the fire spread in ear-real-time by integrating new measurement data modules with the wildfire model. In perspective, the project aims to lay the scientific foundations of a holistic new computational platform to predict and monitor wildfire risk. The resulting technology has the potential to positively influence the wildfire management process, including the development of accurate actuarial strategies.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.

在过去的二十年中，美国野火造成的破坏已大大增加。尽管联邦政府在野火战斗的支出一直在稳步增加，但野火严重性也在上升。这项领先的工程对美国繁荣，健康和基础设施（LEAP-HI）项目的重点是创建在多个时空和时间尺度上野火风险管理的总体计算平台。该愿景将通过在数据分析，计算建模和基于模型的推理领域中创建和整合跨学科科学技术来实现。目的是为实时数字平台开发科学基础，该平台随着新数据的发展，并在区域和社区范围内动态更新长期（季节/几个月）为短期（未来的几周/天）前发生火灾风险，并预测火灾近距离现实时期的近距离火灾行为。一旦开发，计算平台将通过向决策者提供可行的信息来提高野火管理流程的效率，以减轻预期风险和点火后应急响应管理。主要利益相关者和公用事业公司的参与，未来劳动力的准备以及K-12外展计划是该项目不可或缺的一部分。该研究有望提供一种严格的计算方法来量化和预测野火风险。预期的科学进步包括：（i）在不同的空间和临时尺度上，随着数据的可用数据，在不同的空间和临时量表上进行概率野火损失评估的总体计算平台； （ii）一个综合模拟框架，包括野火模型，城市火灾模型和社会经济模型，以预测经济和社会损失方面的野火损失； （iii）一种用于城市火灾模拟的新型数据驱动的建模方法，以及由于野火而导致的新经验模型（QOL）的新经验模型； （iv）新的数据收集模块和高级数据处理技术，以收集精致的数据，过程并注入不同的数据源，并量化测量数据中的不确定性； （v）贝叶斯模型推理框架，以量化建模不确定性并通过将新的测量数据模块与野火模型整合在一起，并在较早时间内更新火灾。从角度来看，该项目旨在奠定整体新计算平台的科学基础，以预测和监视野火风险。最终的技术有可能积极影响野火管理流程，包括制定准确的实际策略。该奖项反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的影响来评估，被认为是宝贵的支持。

项目成果

Coupled fire-atmosphere simulation of the 2018 Camp Fire using WRF-Fire

使用 WRF-Fire 对 2018 年营火进行火灾-大气耦合模拟

• DOI: 10.1071/wf22013
• 发表时间: 2023
• 期刊: International Journal of Wildland Fire
• 影响因子: 3.1
• 作者: Shamsaei, Kasra;Juliano, Timothy W.;Roberts, Matthew;Ebrahimian, Hamed;Kosovic, Branko;Lareau, Neil P.;Taciroglu, Ertugrul
• 通讯作者: Taciroglu, Ertugrul

A Computationally Efficient Method for Updating Fuel Inputs for Wildfire Behavior Models Using Sentinel Imagery and Random Forest Classification

• DOI: 10.3390/rs14061447
• 发表时间: 2022-03-01
• 期刊: REMOTE SENSING
• 影响因子: 5
• 作者: DeCastro, Amy L.;Juliano, Timothy W.;Balch, Jennifer K.
• 通讯作者: Balch, Jennifer K.

Tracking Wildfires With Weather Radars

使用天气雷达追踪野火

• DOI: 10.1029/2021jd036158
• 发表时间: 2022
• 期刊: Journal of Geophysical Research: Atmospheres
• 作者: Lareau, Neil P.;Donohoe, Amanda;Roberts, Matthew;Ebrahimian, Hamed
• 通讯作者: Ebrahimian, Hamed

Characterizing the Role of Moisture and Smoke on the 2021 Santa Coloma de Queralt Pyroconvective Event Using WRF‐Fire

使用 WRF–Fire 表征水分和烟雾对 2021 年圣科洛马德奎拉尔特火对流事件的作用

• DOI: 10.1029/2022ms003288
• 发表时间: 2023
• 期刊: Journal of Advances in Modeling Earth Systems
• 影响因子: 6.8
• 作者: Eghdami, Masih;Juliano, Timothy W.;Jiménez, Pedro A.;Kosovic, Branko;Castellnou, Marc;Kumar, Rajesh;Vila‐Guerau de Arellano, Jordi
• 通讯作者: Vila‐Guerau de Arellano, Jordi

The Role of Fuel Characteristics and Heat Release Formulations in Coupled Fire-Atmosphere Simulation

燃料特性和放热配方在火焰-大气耦合模拟中的作用

• DOI: 10.3390/fire6070264
• 发表时间: 2023
• 期刊: Fire
• 作者: Shamsaei, Kasra;Juliano, Timothy W.;Roberts, Matthew;Ebrahimian, Hamed;Lareau, Neil P.;Rowell, Eric;Kosovic, Branko
• 通讯作者: Kosovic, Branko