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.

这项快速反应研究（RAPID）奖将有助于增加我们对寒冷地区融雪引发的山体滑坡及其随时间变化的了解，重点关注2020年12月阿拉斯加海恩斯的山体滑坡事件。 这次山体滑坡发生在一次极端天气事件之后，该事件创下了降雨量和24英寸积雪迅速融化的记录。 随着气候变暖，预计会出现更多的极端事件，包括更大和更高强度的降水，以及在一年中的某些时候会有更多的雨而不是雪。 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，以及随后春季融雪的重新激活。 从海恩斯滑坡中捕获易腐烂的数据将有助于了解其起源，运动和相对于住宅结构的位置。 这项研究的结果将有助于滑坡检测、灾害地图绘制、建模和风险分析，所有这些都是社区规划和适应长期恢复能力的关键，同时考虑到极端天气事件的增加。这个RAPID项目的主要目标是了解为什么这个特定的斜坡在这次破纪录的事件中失败，它将如何应对春季融雪，以及它的表面将如何随时间演变。研究小组将通过收集易腐烂的现场数据来实现这些目标;比较多个时期滑坡表面的数字高程模型;将滑坡形态变化的时间与降雨，冻结，融雪和地下水相关联;并使用历史和基线数据进行长期变化检测分析和滑坡测绘。 现场数据工作将包括：1）收集重复的航空光探测和测距数据以及滑坡表面的高分辨率图像，2）描述滑坡区的地质特征，3）捕捉社区的应急响应、修复和重建工作，以及4）收集当地环境数据进行分析。 这次滑坡是一个独特的机会，调查融雪引起的落石触发，可能引发破坏性的泥石流。 对重复数据收集的分析将有助于了解： 滑坡表面如何演变，其风险如何随时间变化，特别是在数据很少的寒冷天气环境中发生的事件;以及社区和基础设施恢复的时间，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进行评估，被认为值得支持审查标准。

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