RAPID: Investigating the Triggers of the 2023 Wrangell, Alaska Landslides

RAPID：调查 2023 年阿拉斯加兰格尔山体滑坡的诱因

• 批准号: 2421234
• 负责人: Margaret Darrow
• 金额: $ 4.8万
• 依托单位: University of Alaska Fairbanks Campus
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-03-01 至 2025-02-28
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2421234&HistoricalAwards=false
• 关键词: RAPID; Investigating; Triggers; 2023; Wrangell

The community of Wrangell, Alaska experienced two landslides on the night of November 20, 2023, during a storm event accompanied by high winds (~70 mph) at higher elevations. Both landslides initiated on steep, forested slopes; the 11-Mile Slide killed six people and cut off power, telephone landlines, and access to about 75 homes, while the Middle Ridge Slide initiated at a switchback of a gravel road. The team of investigators on this RAPID project hypothesize that 1) the high winds combined with moderate rainfall of the November 2023 storm triggered the Wrangell landslides, and 2) the presence of a gravel road affected the extent and trajectory of the Middle Ridge Slide. The team's primary objective is to capture the ephemeral data from these landslides to determine their initiation mechanisms and processes, soil thickness, and the role that trees and infrastructure played. What is learned from analysis of the data will inform the development of early warning systems for communities throughout Southeast Alaska. A post-data collection workshop will provide the Wrangell community with a greater understanding of local geology and landslide mechanisms, and an opportunity to guide the direction of future early warning systems and/or landslide event response.The investigators will capture field data that can be incorporated into landslide models, thereby constraining input parameters with on-the-ground measurements to allow for back analysis of these landslide events. While previous research has established correlations among landslides, forest cover, slope angle, and weather events, the work was done at lower spatial resolution; this RAPID research will collect site-specific data (including surficial and bedrock geology, forest characteristics, landslide geometry, and storm characteristics) necessary to: evaluate existing landslide models; understand initiation mechanisms; and determine if wind variables should be incorporated with precipitation and hydrologic observations for landslide prediction models. Results from field work will be incorporated into a statewide landslide inventory, and provide high-resolution data on local surficial and bedrock geology. Cost data on the response to these landslides will be combined with similar data obtained during the December 2020 landslides in Haines, Alaska, providing necessary information for emergency managers in small communities throughout Southeast Alaska.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.

2023年11月20日晚，阿拉斯加州的兰格尔社区经历了两次山体滑坡，当时的风暴事件伴随着高海拔地区的大风（约70英里/小时）。两次山体滑坡都发生在陡峭的森林斜坡上; 11英里的滑坡造成6人死亡，切断了电力、电话固定电话和大约75户家庭的通道，而中岭滑坡则发生在一条砾石路的折返处。这个RAPID项目的调查人员团队假设：1）2023年11月风暴的强风和中等降雨引发了兰格尔滑坡，2）砾石路的存在影响了中脊滑坡的范围和轨迹。该团队的主要目标是从这些山体滑坡中捕获短暂的数据，以确定其启动机制和过程，土壤厚度以及树木和基础设施所发挥的作用。 从数据分析中学到的知识将为整个阿拉斯加东南部社区的早期预警系统的发展提供信息。数据收集后的研讨会将提供兰格尔社区与当地地质和滑坡机制的更好的理解，并有机会指导未来的预警系统和/或滑坡事件的反应方向。调查人员将捕捉现场数据，可以纳入滑坡模型，从而限制输入参数与地面测量，允许这些滑坡事件的反分析。虽然以前的研究已经建立了滑坡，森林覆盖，坡度和天气事件之间的相关性，但这项工作是在较低的空间分辨率下完成的;这项RAPID研究将收集特定地点的数据（包括地表和基岩地质、森林特征、滑坡几何形状和风暴特征），以便：评估现有滑坡模型;了解启动机制;并决定是否应将风变量与降雨和水文观测结合起来，用于滑坡预测模型。现场工作的结果将纳入全州范围的滑坡清单，并提供有关当地地表和基岩地质的高分辨率数据。这些山体滑坡响应的成本数据将与2020年12月阿拉斯加海恩斯山体滑坡期间获得的类似数据相结合，为整个阿拉斯加东南部小社区的应急管理人员提供必要的信息。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。