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

University Of Alaska Fairbanks Campus, Fairbanks AK

Investigators

Abstract

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.