PREEVENTS Track 2: A fast-response wildland fire modeling framework for prediction and risk assessment

预防措施轨道 2：用于预测和风险评估的快速响应荒地火灾建模框架

• 批准号: 1664175
• 负责人: Steven Krueger
• 金额: $ 202.45万
• 依托单位: University of Utah
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-08-01 至 2022-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1664175&HistoricalAwards=false
• 关键词: PREEVENTS; Track; fast; response; wildland

Wildfires are increasingly common throughout the US and have significant impacts on populated areas. Current rapid-response management is based on empirical or semi-empirical models developed more than 40 years ago. The focus of this project is to create a Multistage Wildfire Research and Prediction System (MWRPS) that links several existing community, open source models developed. This new model has the potential to change how fire is studied, and significantly improve operational wildfire and smoke forecasting. MWRPS will serve as tool for fire professionals, urban and environmental planners, and disaster managers who need to determine the societal and ecological impacts of wildfire and smoke. As a community model, MWRPS will be available to the public to examine a variety of hazard-related issues. In addition to graduate student and postdoctoral training, the project includes a plan for K-12 outreach through the Hi-GEAR program.Sudden changes in flow at the fire line are crucial to extreme fire behavior and associated hazards. Because flow at the fire line can significantly impact fire spread and subsequently all wildfire behavior, prediction and simulation of coupled atmosphere-fire flow must be accurate from synoptic down to fire line scales. Accurate prediction requires the ability to model rapid synoptically-driven changes in local winds, realistically render flow in complex terrain, and capture the impacts of the fire itself on local weather. To meet this challenge, the Multistage Wildfire Research and Prediction System (MWRPS), a multi-scale model, 3D model will be developed based on fundamental fluid dynamical principles. MWRPS will have the ability to resolve buildings, trees, and land cover, to incorporate the effects of complex terrain, different vegetation types and geometries, to disperse smoke, and to represent radiation, sensible, and latent heating in the wildfire environment. Predicted wildfire properties from this model will include high resolution temporal and spatial evolution of the fire perimeter and intensity; behavior for both surface and crown fires; smoke production and dispersion in the Wildland Urban Interface (WUI) or through tree canopies (crucial for planning a proposed prescribed burn); and impacts of smoke concentrations and of heat flux in safety zones and on WUI structures. In addition to the model development, three aspects of extreme wildfire behavior will be addressed: the roles of (1) fuel heterogeneity, (2) complex topography, and (3) fire interactions. A data-driven system will be developed for atmosphere-fire models to steer simulations from a multitude of sources including: weather data, sensors, airborne fire images, and satellite remote sensing in a statistically sound manner.

野火在美国各地越来越普遍，对人口稠密地区产生重大影响。目前的快速反应管理是基于40多年前开发的经验或半经验模型。该项目的重点是创建一个多阶段野火研究和预测系统（MWRPS），该系统将现有的几个社区开发的开源模型连接起来。这种新模型有可能改变火灾的研究方式，并显着改善野火和烟雾的预测。MWRPS将作为消防专业人员、城市和环境规划人员以及需要确定野火和烟雾的社会和生态影响的灾害管理人员的工具。作为一个社区模式，MWRPS将向公众提供，以审查各种与灾害有关的问题。 除了研究生和博士后培训外，该项目还包括通过Hi-GEAR计划进行K-12推广的计划。火线流量的突然变化对极端火灾行为和相关危害至关重要。由于在火线的流量可以显着影响火灾蔓延，随后所有的野火行为，预测和模拟耦合的大气-火流必须是准确的，从天气到火线规模。准确的预测需要能够模拟当地风的快速天气驱动变化，真实地渲染复杂地形中的流动，并捕捉火灾本身对当地天气的影响。为了应对这一挑战，多级野火研究和预测系统（MWRPS），多尺度模型，三维模型将开发基于基本流体动力学原理。MWRPS将有能力解决建筑物，树木和土地覆盖，将复杂地形的影响，不同的植被类型和几何形状，分散烟雾，并代表野火环境中的辐射，显热和潜热。从这个模型预测野火属性将包括高分辨率的时间和空间演变的火灾周长和强度;行为的表面和冠火灾;烟雾的生产和扩散在荒地城市界面（WUI）或通过树冠（规划一个拟议的规定燃烧至关重要）;和烟雾浓度和热通量的影响在安全区和WUI结构。除了模型的发展，极端野火行为的三个方面将得到解决：（1）燃料异质性，（2）复杂的地形，（3）火的相互作用的作用。将为大气火灾模型开发一个数据驱动系统，以从多种来源引导模拟，包括：天气数据，传感器，空中火灾图像和卫星遥感，以统计上合理的方式。

项目成果

Semi-direct tree reconstruction using terrestrial LiDAR point cloud data

• DOI: 10.1016/j.rse.2018.02.013
• 发表时间: 2018-04
• 期刊: Remote Sensing of Environment
• 影响因子: 13.5
• 作者: B. Bailey;M. Ochoa

Multilevel maximum likelihood estimation with application to covariance matrices

应用于协方差矩阵的多级最大似然估计

• DOI: 10.1080/03610926.2017.1422755
• 发表时间: 2018
• 期刊: Communications in Statistics - Theory and Methods
• 作者: Turčičová, Marie;Mandel, Jan;Eben, Kryštof
• 通讯作者: Eben, Kryštof

QES-Fire: a dynamically coupled fast-response wildfire model

QES-Fire：动态耦合快速响应野火模型

• DOI: 10.1071/wf21057
• 发表时间: 2022
• 期刊: International Journal of Wildland Fire
• 影响因子: 3.1
• 作者: Moody, Matthew J.;Gibbs, Jeremy A.;Krueger, Steven;Mallia, Derek;Pardyjak, Eric R.;Kochanski, Adam K.;Bailey, Brian N.;Stoll, Rob
• 通讯作者: Stoll, Rob

Fire behaviour and smoke modelling: model improvement and measurement needs for next-generation smoke research and forecasting systems

火灾行为和烟雾建模：下一代烟雾研究和预测系统的模型改进和测量需求

• DOI: 10.1071/wf18204
• 发表时间: 2019
• 期刊: International Journal of Wildland Fire
• 影响因子: 3.1
• 作者: Liu, Yongqiang;Kochanski, Adam;Baker, Kirk R.;Mell, William;Linn, Rodman;Paugam, Ronan;Mandel, Jan;Fournier, Aime;Jenkins, Mary Ann;Goodrick, Scott
• 通讯作者: Goodrick, Scott

Optimizing Smoke and Plume Rise Modeling Approaches at Local Scales

优化局部尺度的烟雾和烟羽上升建模方法

• DOI: 10.3390/atmos9050166
• 发表时间: 2018
• 期刊: Atmosphere
• 影响因子: 2.9
• 作者: Mallia, Derek;Kochanski, Adam;Urbanski, Shawn;Lin, John
• 通讯作者: Lin, John