RAPID: Characterizing the Trigger and Evolution of the December 2020 Haines, Alaska Landslide

RAPID：描述 2020 年 12 月阿拉斯加海恩斯山体滑坡的触发因素和演变过程

• 批准号: 2114015
• 负责人: Margaret Darrow
• 金额: $ 6.42万
• 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: 2021
• 资助国家: 美国
• 起止时间: 2021-02-01 至 2024-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2114015&HistoricalAwards=false
• 关键词: RAPID; Characterizing; Trigger; Evolution; December

This Rapid Response Research (RAPID) award will serve to increase our understanding of snowmelt-induced landslides in cold regions and their changes over time, with a focus on the December 2020 landslide event in Haines, Alaska. This landslide occurred following an extreme weather event that set records for rainfall and rapidly melted 24 inches of snow. More extreme events should be expected with a warming climate, including greater and higher intensity precipitation, and more rain instead of snow during certain times of the year. Through rapid data collection, we will document the extreme nature of the event, including amount and duration of rainfall and their effects on the mass movement, and subsequent reactivation with spring snowmelt. Capturing perishable data from the Haines landslide will help to understand its origin, motion, and location relative to residential structures. Results from this study will contribute to landslide detection, hazard mapping, modeling, and risk analysis, all of which are essential in community planning and adapting for long-term resilience while accounting for an increase in extreme weather events.The primary objectives of this RAPID project are to understand why this particular slope failed during this record-breaking event, how it will respond to spring snowmelt, and how its surface will evolve with time. The research team will accomplish these objectives by collecting perishable field data; comparing digital elevation models of the landslide surface from multiple epochs; correlating timing of changes in landslide morphology to rainfall, freezing, snowmelt, and groundwater; and using historic and baseline data for long-term change detection analysis and landslide mapping. Field data efforts will include: 1) collecting repeat aerial Light Detection and Ranging data and high-resolution images of the landslide surface, 2) characterizing the geology of the slide area, 3) capturing the emergency response, repair, and rebuilding efforts of the community, and 4) collecting local environmental data for analysis. This landslide represents a unique opportunity to investigate the snowmelt-induced rockfall trigger that potentially initiated the destructive debris flow. Analysis of repeated data collections will provide understanding of: how landslide surfaces evolve and how their risk changes with time, especially for events in cold-weather settings for which there is little data; and the timing of community and infrastructure recovery, which will contribute to our understanding of geohazard interaction with the built environment.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.

这项快速响应研究（快速）奖将旨在提高我们对冰雪诱导的寒冷地区滑坡及其随着时间的变化的理解，重点是2020年12月在阿拉斯加海因斯举行的压倒性活动。 这种滑坡发生在一次极端天气事件之后，为降雨的记录设定了记录，并迅速融化了24英寸的降雪。 在温暖的气候下，应该预期更多的极端事件，包括更高和更高的降水量，在一年中的某些时候降雨，而不是降雪。 通过快速数据收集，我们将记录事件的极端性质，包括降雨量和持续时间及其对质量运动的影响以及随后与春天融雪的重新激活。 从海因斯滑坡捕获易腐烂的数据将有助于了解其起源，运动和位置相对于住宅结构。 这项研究的结果将有助于滑坡检测，危险映射，建模和风险分析，所有这些对于社区计划和适应长期弹性的适应性至关重要，同时考虑到极端天气事件的增加，这一快速项目的主要目标是要了解为什么在这次破纪录的事件中，这种特定的坡度在这次破纪录的事件中失败了，如何响应春季的雪橇，其表面效果会如何表面表现出来，以及时间的表面以及时间的表面效果。研究团队将通过收集易腐烂的现场数据来实现这些目标；比较来自多个时期的滑坡表面的数字高程模型；将滑坡形态变化的时机与降雨，冰冻，融雪和地下水相关联；并使用历史和基线数据进行长期变更检测分析和滑坡映射。 现场数据工作将包括：1）收集重复的航空灯检测和范围的数据和高分辨率图像，2）表征幻灯片区域的地质，3）捕获社区的紧急响应，修复和重建工作，以及4）收集本地环境数据进行分析。 这种滑坡是研究融雪引起的岩石fall触发的独特机会，该摇摆触发可能引发破坏性的碎屑流。 对重复数据收集的分析将提供：洋滑落表面如何发展以及它们的风险如何随着时间而变化，尤其是在寒冷天气中几乎没有数据的事件中；社区和基础设施恢复的时机将有助于我们理解与建筑环境的地质扎华互动。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子和更广泛影响的评估评估标准来通过评估来支持的。

项目成果

Geomorphology and initiation mechanisms of the 2020 Haines, Alaska landslide

2020 年阿拉斯加海恩斯山体滑坡的地貌和引发机制

• DOI: 10.1007/s10346-022-01899-3
• 发表时间: 2022
• 期刊: Landslides
• 影响因子: 6.7
• 作者: Darrow, Margaret M.;Nelson, Victoria A.;Grilliot, Michael;Wartman, Joseph;Jacobs, Aaron;Baichtal, James F.;Buxton, Cindy
• 通讯作者: Buxton, Cindy