AccelNet-Design: iFireNet: An international network of networks for prediction and management of wildland fires

AccelNet-Design：iFireNet：用于预测和管理荒地火灾的国际网络

• 批准号: 2114740
• 负责人: Tirtha Banerjee
• 金额: $ 25万
• 依托单位: University of California-Irvine
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2114740&HistoricalAwards=false
• 关键词: AccelNet; Design; iFireNet; international; network

Changes in temperature and rainfall patterns, increasingly complex wildland-urban interfaces, and human-induced land-use changes have led to unprecedented, enhanced wildfire activities across the world. This design project, iFireNet, will connect clusters of expertise nationally and internationally to accelerate interdisciplinary actionable research on wildfires among academia, agencies, non-profit organizations and stakeholders. The drivers of wildfire behavior are local in nature, yet there are predictive models and management practices that can be applied in different geographic settings. iFireNet will act as a catalyst to integrate the scientific expertise and unique facilities and resources from the U.S., Australia, Canada, Netherlands, and the United Kingdom to address knowledge gaps in the science, engineering, as well as management aspects of wildfires. The international coordination of wildfire researchers will advance capacities to predict and manage wildland fires, thereby minimizing damage to life, nature, and property. This network of networks will bring an approach currently missing in the wildfire research community that is needed to advance understanding of wildfire behavior, integrated fire risk management, design of prescribed fires, canopy-to-landscape scale forest disturbance, and fire-weather/climate coupling. iFireNet will create (1) synergy among complementary experiments in different geographical settings, (2) a standardized protocol to collect, interpret and share data from these experiments, (3) the basis of model intercomparisons in the context of multi-fidelity fire modeling, (4) a protocol for benchmarking wildfire simulations across multiple scales, and (5) a unified training module that adapts the best practices of local knowledge, yet can be translated in the international setting. The iFireNet web portal will host state-of-the-art predictive models, a comprehensive global database of protocols and standards for multiscale fire experiments, a catalogue of equipment, infrastructure and models, and a knowledge platform providing mentors, collaborators, training tools and opportunities for students and practitioners. iFireNet will train the next generation of wildland fire managers and scientists through the organization of student exchange programs, summer schools, conference chapters and joint mentoring of graduate and professional students. It is envisioned that the diversity of expertise and cross-disciplinary collaborations across academic researchers, federal agencies, non-profit organizations and industries of the participating countries will help solve some of the long-standing questions in the science of wildland fires.The Accelerating Research through International Network-to-Network Collaborations (AccelNet) program is designed to accelerate the process of scientific discovery and prepare the next generation of U.S. researchers for multiteam international collaborations. The AccelNet program supports strategic linkages among U.S. research networks and complementary networks abroad that will leverage research and educational resources to tackle grand scientific challenges that require significant coordinated international efforts.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.

温度和降雨模式的变化、日益复杂的荒地-城市界面以及人类引起的土地利用变化导致了世界各地前所未有的野火活动。 这个设计项目iFireNet将连接国内和国际的专业知识集群，以加速学术界、机构、非营利组织和利益攸关方之间关于野火的跨学科可行动研究。 野火行为的驱动因素本质上是本地的，但有预测模型和管理实践可以应用于不同的地理环境。 iFireNet将作为一种催化剂，整合来自美国的科学专业知识和独特的设施和资源，澳大利亚、加拿大、荷兰和联合王国，以解决野火的科学、工程和管理方面的知识差距。 野火研究人员的国际协调将提高预测和管理野火的能力，从而最大限度地减少对生命，自然和财产的损害。这个网络的网络将带来一个方法，目前缺乏在野火研究界，需要推进野火行为的理解，综合火灾风险管理，设计规定的火灾，冠层到景观规模的森林干扰，火灾天气/气候耦合。 iFireNet将创建（1）不同地理环境中互补实验之间的协同作用，（2）收集，解释和共享这些实验数据的标准化协议，（3）多保真度火灾建模背景下模型相互比较的基础，（4）跨多个尺度对野火模拟进行基准测试的协议，以及（5）一个统一的培训模块，该模块采用当地知识的最佳实践，但可以在国际环境中翻译。 iFireNet门户网站将提供最先进的预测模型、多尺度火灾实验协议和标准的全球综合数据库、设备、基础设施和模型目录以及为学生和从业人员提供导师、合作者、培训工具和机会的知识平台。 iFireNet将通过组织学生交流项目、暑期学校、会议分会以及对研究生和专业学生的联合指导，培训下一代荒地火灾管理人员和科学家。 据设想，专业知识的多样性和跨学科合作的学术研究人员，联邦机构，参与国的非营利组织和行业将帮助解决野火科学中一些长期存在的问题。通过国际网络对网络合作加速研究该计划旨在加速科学发现的进程，并为下一代美国研究人员进行多团队国际合作做好准备。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。

项目成果

Historical spatiotemporal changes in fire danger potential across biomes

生物群落潜在火灾危险的历史时空变化

• DOI: 10.1016/j.scitotenv.2023.161954
• 发表时间: 2023
• 期刊: Science of The Total Environment
• 影响因子: 9.8
• 作者: Baijnath-Rodino, Janine A.;Le, Phong V.V.;Foufoula-Georgiou, Efi;Banerjee, Tirtha
• 通讯作者: Banerjee, Tirtha

Quantifying the effectiveness of shaded fuel breaks from ground-based, aerial, and spaceborne observations

• DOI: 10.1016/j.foreco.2023.121142
• 发表时间: 2023-09
• 期刊: Forest Ecology and Management
• 影响因子: 3.7
• 作者: J. Baijnath-Rodino;Alexandre Martinez;R. York;E. Foufoula‐Georgiou;A. Aghakouchak;T. Banerjee

Historical seasonal changes in prescribed burn windows in California

加利福尼亚州规定燃烧窗口的历史季节变化

• DOI: 10.1016/j.scitotenv.2022.155723
• 发表时间: 2022
• 期刊: Science of The Total Environment
• 影响因子: 9.8
• 作者: Baijnath-Rodino, Janine A.;Li, Shu;Martinez, Alexandre;Kumar, Mukesh;Quinn-Davidson, Lenya N.;York, Robert A.;Banerjee, Tirtha
• 通讯作者: Banerjee, Tirtha

Examining the existing definitions of wildland‐urban interface for California

检查加州荒地与城市界面的现有定义

• DOI: 10.1002/ecs2.4306
• 发表时间: 2022
• 期刊: Ecosphere
• 影响因子: 2.7
• 作者: Kumar, Mukesh;Li, Shu;Nguyen, Phu;Banerjee, Tirtha
• 通讯作者: Banerjee, Tirtha

A scale-wise analysis of intermittent momentum transport in dense canopy flows

• DOI: 10.1017/jfm.2022.414
• 发表时间: 2021-10
• 期刊: Journal of Fluid Mechanics
• 影响因子: 3.7
• 作者: S. Chowdhuri;K. Ghannam;T. Banerjee