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.
