Linking Landslide Occurrence with Atmospheric Rivers along the Northern Pacific Coast of North America

Landslides are an important mechanism of sediment transfer in steep landscapes, acting as an ecosystem disturbance agent and posing hazards to human populations. Slope failures that lead to landslides are often triggered by heavy precipitation events, such as those during atmospheric rivers (ARs), known for transporting vast amounts of moisture landward. However, our ability to predict landslide occurrence based on hydroclimatic (i.e., precipitation), topographic, and other environmental parameters has been limited by the spatiotemporal availability of reliable landslide databases. Here, we focus on landslide occurrence modeling across the coastal British Columbia and Southeast Alaska, where ARs are known to bring extraordinarily heavy pulses of precipitation. To this end, we mapped landslide boundaries across the region using Landsat (TM, ETM+, and OLI) surface reflectance data collected between 1985 and 2022, within the Landtrendr algorithm in Google Earth Engine (GEE). Annual landslide boundaries were used to model the drivers of regional landslide frequency and determine the potential impact ARs had on historical landslide occurrence. The accuracy of remotely detected landslides was evaluated using hundreds of delineated landslide boundaries (via aerial and ground surveys conducted between 1920 to 2020) within the Tongass National Forest, Alaska, and drone-based field surveys in British Columbia. Preliminary results indicate that: (1) our remotely sensed landslide mapping had high correspondence with the reference datasets; and (2) machine learning-derived variable importance values support evidence that landslide occurrence along the Northwestern Pacific Coast is indeed driven by high precipitation during ARs, as well as a suite of topographic and environmental parameters. This ongoing research will contribute to our ability to anticipate the frequency and magnitude of natural disasters in response to changing climatic conditions.