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

这一快速反应研究(RAPID)奖将有助于增进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，重点是2020年12月阿拉斯加海恩斯的山体滑坡事件。这场山体滑坡发生在一次极端天气事件之后，这场事件创下了降雨量的纪录，并迅速融化了24英寸的积雪。随着气候变暖，预计会发生更多极端事件，包括更大和更高强度的降水，以及在一年中的某些时间段出现更多降雨而不是降雪。通过快速数据收集，我们将记录这次事件的极端性质，包括降雨量和持续时间及其对质量运动的影响，以及随后春季融雪时的重新激活。从海恩斯滑坡中捕捉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、危险地图绘制、建模和风险分析，所有这些都是社区规划和适应长期弹性的关键，同时考虑到极端天气事件的增加。这个快速项目的主要目标是了解这个特殊的斜坡在这次破纪录的事件中发生故障的原因，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；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、冰冻、融雪和地下水相关联；以及使用历史和基线数据进行长期变化检测分析和滑坡地图绘制，以实现这些目标。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)捕获社区的应急、修复和重建工作；以及4)收集当地环境数据以供分析。这场滑坡是一个独特的机会，可以调查可能引发破坏性泥石流的融雪引发的崩塌触发因素。对重复收集的数据的分析将有助于理解：滑坡表面如何演变以及它们的风险如何随时间变化，特别是对于数据很少的寒冷天气环境中的事件；以及社区和基础设施恢复的时间，这将有助于我们了解地质灾害与建成环境的相互作用。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.