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The Chaos Canyon landslide, which collapsed on the afternoon of 28 June 2022 in Rocky Mountain National Park, presents an opportunity to evaluate instabilities within alpine regions faced with a warming and dynamic climate. Video documentation of the landslide was captured by several eyewitnesses and motivated a rapid field campaign. Initial estimates put the failure area at 66 630 m2, with an average elevation of 3555 m above sea level. We undertook an investigation of previous movement of this landslide, measured the volume of material involved, evaluated the potential presence of interstitial ice and snow within the failed deposit, and examined potential climatological impacts on the collapse of the slope. Satellite radar and optical measurements were used to calculate deformation of the landslide in the 5 years leading up to collapse. From 2017 to 2019, the landslide moved ∼5 m yr−1, accelerating to 17 m yr−1 in 2019. Movement took place through both internal deformation and basal sliding. Climate analysis reveals that the collapse took place during peak snowmelt, and 2022 followed 10 years of higher than average positive degree day sums. We also made use of slope stability modeling to test what factors controlled the stability of the area. Models indicate that even a small increase in the water table reduces the factor of safety to <1, leading to failure. We posit that a combination of permafrost thaw from increasing average temperatures, progressive weakening of the basal shear zone from several years of movement, and an increase in pore-fluid pressure from snowmelt led to the 28 June collapse. Material volumes were estimated using structure from motion (SfM) models incorporating photographs from two field expeditions on 8 July 2022 – 10 d after the slide. Detailed mapping and SfM models indicate that ∼1 258 000 ± 150 000 m3 of material was deposited at the slide toe and ∼1 340 000 ± 133 000 m3 of material was evacuated from the source area. The Chaos Canyon landslide may be representative of future dynamic alpine topography, wherein slope failures become more common in a warming climate. 

Lakes in direct contact with glaciers (ice-marginal lakes) are found across alpine and polar landscapes. Many studies characterize ice-marginal lake behavior over multi-decadal timescales using either episodic ~annual images or multi-year mosaics. However, ice-marginal lakes are dynamic features that experience short-term (i.e., day to year) variations in area and volume superimposed on longer-term trends. Through aliasing, this short-term variability could result in erroneous long-term estimates of lake change. We develop and implement an automated workflow in Google Earth Engine to quantify monthly behavior of ice-marginal lakes between 2013 and 2019 across south-central Alaska using Landsat 8 imagery. We employ a supervised Mahalanobis minimum-distance land cover classifier incorporating three datasets found to maximize classifier performance: shortwave infrared imagery, the normalized difference vegetation index (NDVI), and spatially filtered panchromatic reflectance. We observe physically-meaningful ice-marginal lake area variance on sub-annual timescales, with the median area fluctuation of an ice-marginal lake found to be 10.8% of its average area. The median signal (slow lake growth) to noise (physically-meaningful short-term area variability) ratio is 1.5:1, indicating that short-term variability is responsible for ~33% of observed area change in the median ice-marginal lake. The magnitude of short-term area variability is similar for ice-marginal and nonglacial lakes, suggesting that the cause of observed variations is not of glacial origin. These data provide a new context for interpreting behaviors observed in multi-decadal studies and encourage attention to sub-annual behavior of ice-marginal lakes even in long-term studies.