EAR-PF: Closing the gap between analogue studies and numerical lava flow models using insights from the 2018 Kilauea eruption

EAR-PF：利用 2018 年基拉韦厄火山喷发的见解缩小模拟研究和数值熔岩流模型之间的差距

• 批准号: 1952646
• 负责人: Elisabeth Gallant
• 金额: $ 17.4万
• 依托单位: Gallant, Elisabeth
• 依托单位国家: 美国
• 项目类别: Fellowship Award
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-06-01 至 2023-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1952646&HistoricalAwards=false
• 关键词: EAR; PF; Closing; gap; between

Dr. Elisabeth Gallant has been granted an NSF EAR Postdoctoral Fellowship to carry out research and educational activities at the Hawaiian Volcano Observatory, USGS Alaska Volcano Observatory, alongside Dr Matt Patrick, Dr Hannah Dietterich, and Dr Janine Kavanagh at the University of Liverpool (UK). This project seeks to advance numerical lava flow models by using data collected during the 2018 eruption of Kilauea and novel analogue modeling techniques to better understand controls on lava flow emplacement. Lava flows, although generally less deadly than other volcanic hazards, can be extremely disruptive because they displace populations, destroy property, and damage vital infrastructure. The 2018 lower East Rift Zone (LERZ) eruption of Kilauea Volcano, Hawaii, covered a residential area two-thirds the size of Manhattan, destroyed 716 structures over a period of four months, and cost $800 million to recover from. One way to help mitigate negative impacts of lava flows is by forecasting the potential paths of future eruptions to help guide evacuation planning and land use decisions. Computational modeling has become the established method to forecast the impact of lava flow hazards. The computer models developed during this fellowship will take into account factors we know are critical for determining inundation patterns, specifically how changes in the amount of lava erupting at the vent over time (effusion rate) determines what happens further down in the flow (e.g., the ability of the lava flow to forge a new inundation path by channelizing or breaking out up-flow). The 2018 LERZ eruption provides an unprecedented opportunity to improve our knowledge of the relationships between effusion rate, flow channelization, and breakouts due to extensive monitoring and data collection. New techniques in analogue modeling, developed at the University of Liverpool, will quantify these relationships and help address problems of scaling between laboratory models and the real world. Insights from these analogue models will then be used to improve computational lava flow models. This project will advance the fundamental understanding of lava inundation through the use of multi-scale models to investigate hazards, addressing one of the grand challenges in volcano science established by the National Academy of Science. This work also aligns with the goals of the US Volcano Science Center. Outputs from this project will be used to help design accessible educational materials to increase hazard communication through the USGS Volcanic Hazards Program and would complement existing USGS Geonarratives for volcanic landscapes.This study will advance the development of lava flow forecasting tools by using new analogue modeling techniques and high-resolution data from the 2018 LERZ eruption to further quantify the impact of dynamic eruption source parameters (ESPs) on lava flow emplacement patterns. The 2018 LERZ eruption of Kilauea Volcano provides an unprecedented opportunity to improve our knowledge of the processes that govern lava flow emplacement and advance our ability to model flow field evolution. Specifically, it will focus on the impact that variations in ESPs have on channel evolution and breakouts, which is notably absent in the current computational forecasting landscape. Channels help control where the lava goes and how fast it arrives, which are important factors in assessing inundation hazards. Approaching this problem from the analogue, numerical, and real world scale of the problem provides a robust foundation for advancing the field of lava flow inundation forecasting. This work will combine insights from the robust suite of data acquired by the USGS during the 2018 LERZ eruption and new techniques pioneered by the MagmaLab at the University of Liverpool that allow for 4D imaging of analogue flow models to develop open-access numerical lava flow models. Educational materials will be developed to augment hazard communication materials at the USGS Volcano Hazards Program.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.

Elisabeth Gallant 博士获得了 NSF EAR 博士后奖学金，与英国利物浦大学的 Matt Patrick 博士、Hannah Dietterich 博士和 Janine Kavanagh 博士一起在夏威夷火山观测站、USGS 阿拉斯加火山观测站开展研究和教育活动。该项目旨在利用 2018 年基拉韦厄火山喷发期间收集的数据和新颖的模拟建模技术来推进数值熔岩流模型，以更好地了解熔岩流安置的控制。熔岩流虽然通常不如其他火山灾害致命，但具有极大的破坏性，因为它们会导致人口流离失所、破坏财产并损坏重要基础设施。 2018 年夏威夷基拉韦厄火山东裂谷下部 (LERZ) 喷发覆盖了曼哈顿三分之二大小的住宅区，在四个月的时间内摧毁了 716 栋建筑，恢复成本达 8 亿美元。帮助减轻熔岩流负面影响的一种方法是预测未来喷发的潜在路径，以帮助指导疏散规划和土地使用决策。计算模型已成为预测熔岩流灾害影响的既定方法。在本次研究期间开发的计算机模型将考虑我们所知道的对于确定淹没模式至关重要的因素，特别是随着时间的推移，在喷口处喷发的熔岩量的变化（流出率）如何决定进一步向下流动的情况（例如，熔岩流通过引导或突破向上流来形成新的淹没路径的能力）。由于广泛的监测和数据收集，2018 年 LERZ 喷发为我们提供了前所未有的机会，以提高我们对喷发率、流动通道化和喷发之间关系的了解。利物浦大学开发的模拟建模新技术将量化这些关系，并帮助解决实验室模型与现实世界之间的缩放问题。这些模拟模型的见解将用于改进计算熔岩流模型。该项目将通过使用多尺度模型来调查灾害，增进对熔岩淹没的基本了解，解决美国国家科学院提出的火山科学领域的重大挑战之一。这项工作也符合美国火山科学中心的目标。该项目的成果将用于帮助设计易于理解的教育材料，以通过 USGS 火山灾害计划加强危险沟通，并补充现有的 USGS 火山景观地理叙述。这项研究将通过使用新的模拟建模技术和 2018 年 LERZ 喷发的高分辨率数据来进一步量化动态喷发源参数 (ESP) 对熔岩的影响，从而推进熔岩流预测工具的开发 流动安置模式。 2018 年基拉韦厄火山 LERZ 喷发提供了前所未有的机会，可以提高我们对控制熔岩流就位过程的了解，并提高我们模拟流场演化的能力。具体来说，它将重点关注 ESP 的变化对通道演变和突破的影响，这在当前的计算预测领域中是明显不存在的。通道有助于控制熔岩的去向和到达速度，这是评估淹没危险的重要因素。从问题的模拟、数值和现实世界规模来处理这个问题，为推进熔岩流淹没预测领域提供了坚实的基础。这项工作将结合美国地质调查局在 2018 年 LERZ 喷发期间获得的大量数据的见解以及利物浦大学 MagmaLab 首创的新技术，这些技术允许对模拟流模型进行 4D 成像，以开发开放获取的数值熔岩流模型。将开发教育材料，以增强 USGS 火山灾害计划中的危险通报材料。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力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

Volcanologists—who are we and where are we going?

火山学家——我们是谁，我们要去哪里？

• DOI: 10.1007/s00445-022-01547-7
• 发表时间: 2022
• 期刊: Bulletin of Volcanology
• 影响因子: 3.5
• 作者: Kavanagh, Janine L.;Annen, Catherine J.;Burchardt, Steffi;Chalk, Caitlin;Gallant, Elisabeth;Morin, Julie;Scarlett, Jazmin;Williams, Rebecca
• 通讯作者: Williams, Rebecca