DOI: 10.5066/p14mqpgo
title: Inventory of rock avalanches in the central Chugach Mountains, northern Prince William Sound, Alaska, 1984-2024

description: In the Prince William Sound region of Alaska, recent glacier retreat started in the mid-1800s and began to accelerate in the mid-2000s in response to warming air temperatures (Maraldo and others, 2020). Prince William Sound is surrounded by the Chugach Mountains and has numerous ocean-terminating glaciers, with rapid deglaciation increasingly exposing oversteepened bedrock walls of fiords. Rapidly moving rock avalanches (RAs) have the potential to generate tsunamis and adversely impact maritime vessels, marine activities, and coastal infrastructure and populations in the Prince William Sound region. RAs have been documented in the Chugach Mountains in the past (Post, 1967; McSaveney, 1978; Uhlmann and others, 2013), but a time series of RAs in the Chugach Mountains is not currently available. A systematic inventory of RAs in the Chugach is needed as a baseline to evaluate any future changes in RA frequency, magnitude, and mobility. 

This data release presents a comprehensive historical inventory of RAs in a 4600 km2 area of the Prince William Sound. The inventory was generated from: (1) visual inspection of 30-m resolution Landsat satellite images collected between July 1984 and August 2024; and (2) the use of an automated image classification script (Google earth Engine supRaglAciaL Debris INput dEtector (GERALDINE, Smith and others, 2020)) designed to detect new rock-on-snow events from repeat Landsat images from the same time period. RAs were visually identified and mapped in a Geographic Information System (GIS) from the near-infrared (NIR) band of Landsat satellite images. This band provides significant contrast between rock and snow to detect newly deposited rock debris. A total of 252 Landsat images were visually examined, with more images available in recent years compared to earlier years (Figure 1). Calendar year 1984 was the first year when 30-m resolution Landsat data were available, and thus provided a historical starting point from which RAs could be detected with consistent certainty. By 2017, higher resolution (<5-m) daily Planet satellite images became consistently available and were used to better constrain RA timing and extent. 


Figure 1. Diagram showing the number of usable Landsat images per year.
 
This inventory reveals 118 RAs ranging in size from 0.1 km2 to 2.3 km2. All of these RAs occurred during the months of May through September (Figure 2). The data release includes three GIS feature classes (polygons, points, and polylines), each with its own attribute information. The polygon feature class contains the entire extent of individual RAs and does not differentiate the source and deposit areas. The point feature class contains headscarp and toe locations, and the polyline feature class contains curvilinear RA travel distance lines that connect the headscarp and toe points. Additional attribute information includes the following: location of headscarp and toe points, date of earliest identified occurrence, if and when the RA was sequestered into the glacier, presence and delineation confidence levels (see Table 1 for definition of A, B, and C confidence levels), identification method (visual inspection versus automated detection), image platform, satellite, estimated cloud cover, if the RA is lobate, image ID, image year, image band, affected area in km2, length, height, length/height, height/length, notes, minimum and maximum elevation, aspect at the headscarp point, slope at the headscarp point, and geology at the headscarp point. Topographic information was derived from 5-m interferometric synthetic aperture radar (IfSAR) Digital Elevation Models (DEMs) that were downloaded from the USGS National Elevation Dataset website (U.S. Geological Survey, 2015) and were mosaicked together in ArcGIS Pro. The aspect and slope layers were generated from the downloaded 5-m DEM with the “Aspect” and “Slope” tools in ArcGIS Pro. Aspect and slope at the headscarp mid-point were then recorded in the attribute table. A shapefile of Alaska state geology was downloaded from Wilson and others (2015) and was used to determine the geology at the headscarp location. The 118 identified RAs have the following confidence level breakdown for presence: 66 are A-level, 51 are B-level, and 1 is C-level. The 118 identified RAs have the following confidence level breakdown for delineation: 39 are A-level and 79 are B-level. Please see the provided attribute table spreadsheet for more detailed information. 

Figure 2. Diagram showing seasonal timing of mapped rock avalanches.  
 
Table 1. Rock avalanche presence and delineation confidence levels



Category
Grade
  Justification


Presence
    A
Feature is clearly visible in one or more satellite images. 


    B
 
			Feature is clearly visible in one or more satellite images but has low contrast with the surroundings and may be surficial debris from rock fall, rather than from a rock avalanche. 


    C
 
			Feature presence is possible but uncertain due to poor quality of imagery (e.g., heavy cloud cover or shadows) or lack of multiple views.


Delineation
   
			    A

			Exact outline of the feature from headscarp to toe is clear. 


    B

			General shape of the feature is clear but the exact headscarp or toe location is unclear (e.g., due to clouds or shadows).




Disclaimer: Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government.
 
References 

Maraldo, D.R., 2020, Accelerated retreat of coastal glaciers in the Western Prince William Sound, Alaska: Arctic, Antarctic, and Alpine Research, v. 52, p. 617-634, https://doi.org/10.1080/15230430.2020.1837715

McSaveney, M.J., 1978, Sherman glacier rock avalanche, Alaska, U.S.A. in Voight, B., ed., Rockslides and Avalanches, Developments in Geotechnical Engineering, Amsterdam, Elsevier, v. 14, p. 197–258.

Post, A., 1967, Effects of the March 1964 Alaska earthquake on glaciers: U.S. Geological Survey Professional Paper 544-D, Reston, Virgina, p. 42, https://pubs.usgs.gov/pp/0544d/
 
Smith, W. D., Dunning, S. A., Brough, S., Ross, N., and Telling, J., 2020, GERALDINE (Google Earth Engine supRaglAciaL Debris INput dEtector): A new tool for identifying and monitoring supraglacial landslide inputs: Earth Surface Dynamics, v. 8, p. 1053-1065, https://doi.org/10.5194/esurf-8-1053-2020

Uhlmann, M., Korup, O., Huggel, C., Fischer, L., and Kargel, J. S., 2013, Supra-glacial deposition and flux of catastrophic rock-slope failure debris, south-central Alaska: Earth Surface Processes and Landforms, v. 38, p. 675–682, https://doi.org/10.1002/esp.3311

U.S. Geological Survey, 2015, USGS NED Digital Surface Model AK IFSAR-Cell37 2010 TIFF 2015: U.S. Geological Survey, https://elevation.alaska.gov/#60.67183:-147.68372:8

Wilson, F.H., Hults, C.P., Mull, C.G, and Karl, S.M, compilers, 2015, Geologic map of Alaska: U.S. Geological Survey Scientific Investigations Map 3340, pamphlet p. 196, 2 sheets, scale 1:1,584,000, https://pubs.usgs.gov/publication/sim334