Holocene History of Deep-Seated Landsliding in the North Fork Stillaguamish River Valley From Surface Roughness Analysis, Radiocarbon Dating, and Numerical Landscape Evolution Modeling
Journal of Geophysical Research: Earth Surface
Documenting spatial and temporal patterns of past landsliding is a challenging step in quantifying the effect of landslides on landscape evolution. While landslide inventories can map spatial distributions, lack of dateable material, landslide reactivations, or time, access, and cost constraints generally limit dating large numbers of landslides to analyze temporal patterns. Here we quantify the record of the Holocene history of deep-seated landsliding along a 25 km stretch of the North Fork Stillaguamish River valley, Washington State, USA, including the 2014 Oso landslide, which killed 43 people. We estimate the ages of more than 200 deep-seated landslides in glacial sediment by defining an empirical relationship between landslide deposit age from radiocarbon dating and landslide deposit surface roughness. We show that roughness systematically decreases with age as a function of topographic wavelength, consistent with models of disturbance-driven soil transport. The age-roughness model predicts a peak in landslide frequency at ~1000 calibrated (cal) years B.P., with very few landslide deposits older than 7000 cal years B.P. or younger than 100 cal years B.P., likely reflecting a combination of preservation bias and a complex history of changing climate, base level, and seismic shaking in the study area. Most recent landslides have occurred where channels actively interact with the toes of hillslopes composed of glacial sediments, suggesting that lateral channel migration is a primary control on the location of large deep-seated landslides in the valley.
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Booth A.M., LaHusen S.R., Duvall A.R., Montgomery D.R. 2017. Holocene History of Deep-Seated Landsliding in the North Fork Stillaguamish River Valley From Surface Roughness Analysis, Radiocarbon Dating, and Numerical Landscape Evolution Modeling. Journal of Geophysical Research: Earth Surface, 122(2):456-472.