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 central Chugach Mountains and consists of numerous ocean-terminating glaciers, with rapid deglaciation increasingly exposing oversteepened bedrock walls of fiords. Deglaciation may accelerate the occurrence of rapidly moving rock avalanches (RAs), which 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

Steep glacial and paraglacial landscapes often exhibit evidence of gravitationally-driven slope deformation. In recently deglaciated coastal environments, catastrophic failures of these bedrock instabilities as rapid landslides have the potential to generate tsunamis that may pose hazards for communities, infrastructure, mariners, and important natural and cultural resources. We present a first inventory of manually mapped bedrock instabilities in western Prince William Sound and nearby locations in the Chugach Mountains. Slope instabilities included in this inventory are defined as large areas (> 0.01 km2) that exhibit evidence of slope deformation, including scarps, tension cracks, and signs of recent smaller-scale landslides. Areas of repeat rock avalanches, that may indicate areas of larger deformation, are also included. All instabilities in this inventory were identified from a combination of field observations, satellite and airborne optical imagery, InSAR (interferometric synthetic aperture radar)-derived elevation models, and limited local lidar elevation models. This inventory is not exhaustive or complete, nor should it be considered a statistically representative sample of instabilities in this region. Furthermore, it is our intention to continue to add records to this inventory as previously unidentified instabilities are discovered or develop in changing conditions. The data presented here represent bedrock instabilities in western Prince William Sound identified prior to February 2023.

Steep glacial and paraglacial landscapes often exhibit evidence of gravitationally-driven slope deformation. In recently deglaciated coastal environments, catastrophic failures of these bedrock instabilities as rapid landslides have the potential to generate tsunamis that may pose hazards for communities, infrastructure, mariners, and important natural and cultural resources. We present a first inventory of manually mapped bedrock instabilities in western Prince William Sound and nearby locations in the Chugach Mountains. Slope instabilities included in this inventory are defined as large areas (> 0.01 km2) that exhibit evidence of slope deformation, including scarps, tension cracks, and signs of recent smaller-scale landslides. Areas of repeat rock avalanches, that may indicate areas of larger deformation, are also included. All instabilities in this inventory were identified from a combination of field observations, satellite and airborne optical imagery, InSAR (interferometric synthetic aperture radar)-derived elevation models, and limited local lidar elevation models. This inventory is not exhaustive or complete, nor should it be considered a statistically representative sample of instabilities in this region. Furthermore, it is our intention to continue to add records to this inventory as previously unidentified instabilities are discovered or develop in changing conditions. The data presented here represent bedrock instabilities in western Prince William Sound identified prior to February 2023.

Multiple subaerial landslides adjacent to Prince William Sound, Alaska (for example, Dai and others, 2020; Higman and others, 2023; Schaefer and others, 2024) pose a threat to the public because of their potential to generate ocean waves (Barnhart and others, 2021, 2022; Dai and others, 2020) that could affect towns and marine activities. One bedrock landslide on the west side of Barry Arm fjord drew international attention in 2020 because of its large size (~500M m3) and tsunamigenic potential (Dai and others, 2020). As part of the U.S. Geological Survey response to the detection of the potentially tsunamigenic landslide at Barry Arm, as well as a broader effort to evaluate bedrock landslide and tsunamigenic potential throughout Prince William Sound (for example, Schaefer and others, 2024), we continued rock mass quality assessments and collection of structural geology data in southwest and eastern Prince William Sound in August and September, 2023 (see associated data from 2021-2022 in Coe and others, 2024 and Belair and others, 2025). The quality (strength) of a rock mass depends on the properties of intact rock and the characteristics of discontinuities (for example, bedding, fractures, cleavage) that cut the rock. Rock mass quality can be estimated in the field using a variety of classification schemes. In 2023, we accessed sites by boat. At each field site, we made our measurements at rock outcrops, which were typically found at the base of cliffs, along ridge lines, in flat areas in coastal zones, and in areas recently scoured and plucked by glaciers. In two dimensions, outcrops ranged in size from about 30 m2 to 100 m2. We visited a total of 79 sites in the field. Most sites were in metamorphosed Cretaceous flysch, but a few were in intrusive and extrusive igneous rocks (Nelson and others, 1985; Wilson and others, 2015; Winkler, 1992). We collected data that we later used to classify rock mass quality according to four commonly used classification schemes: (1) Rock Mass Quality (Q, for example, Barton and others, 1974; Coe and others, 2005) (2) Rock Mass Rating (RMR, for example, Bieniawski, 1989) (3) Slope Mass Rating (SMR, for example, Moore and others, 2009; Romana, 1995) (4) Geologic Strength Index (GSI, for example, P. Marinos and Hoek, 2000; V. Marinos and others, 2005) We also determined Rock Quality Designation (RQD, for example, Deere and Deere, 1989; Palmström, 1982) and estimated intact rock strength using a Proceq Rock Schmidt Type L Hammer (see RatingsReadMe2023.pdf for details). Schmidt Hammer rebound values were converted to Uniaxial Compressive Strength (UCS) using equations developed for the same rock types that we observed in the field, but at different locations. For flysch, rebound values from the Type L Schmidt Hammer were converted to UCS by the equation shown in Table 3 and Figure 3 of Morales and others (2004). For intrusive igneous rocks, rebound values were converted to UCS by the equation shown in Figure 3 of Aydin and Basu (2005). For extrusive igneous rocks, rebound values were converted to UCS by the Equation 4 in Karaman and Kesimal (2015). Additionally, we collected strike and dip measurements of any observed bedding, fractures, and cleavage. All four rock mass quality classification schemes use data from characteristics of discontinuities present in the rock. Discontinuity data that we collected in the field included: total number of discontinuities, roughness of the surface of the discontinuities, number of sets of discontinuities, type of filling or alteration on the surface of discontinuities, aperture or “openness” of discontinuities, and the amount of water present. Numerical ratings for each of these factors are assigned based on the correlation of field measurements and observations with descriptive rankings. The rankings and any additional details used for Q, RMR, SMR, and GSI classification schemes are shown in Tables 1-3 and Figures 1-2 in the RatingsReadMe2023.pdf. A file of a

Multiple subaerial landslides adjacent to Prince William Sound, Alaska (for example, Dai and others, 2020; Higman and others, 2023; Schaefer and others, 2024) pose a threat to the public because of their potential to generate ocean waves (Barnhart and others, 2021, 2022; Dai and others, 2020) that could affect towns and marine activities. One bedrock landslide on the west side of Barry Arm fjord drew international attention in 2020 because of its large size (~500M m3) and tsunamigenic potential (Dai and others, 2020). As part of the U.S. Geological Survey response to the detection of the potentially tsunamigenic landslide at Barry Arm, as well as a broader effort to evaluate bedrock landslide and tsunamigenic potential throughout Prince William Sound (for example, Schaefer and others, 2024), we continued rock mass quality assessments and collection of structural geology data in southwest and eastern Prince William Sound in August and September, 2023 (see associated data from 2021-2022 in Coe and others, 2024 and Belair and others, 2025). The quality (strength) of a rock mass depends on the properties of intact rock and the characteristics of discontinuities (for example, bedding, fractures, cleavage) that cut the rock. Rock mass quality can be estimated in the field using a variety of classification schemes. In 2023, we accessed sites by boat. At each field site, we made our measurements at rock outcrops, which were typically found at the base of cliffs, along ridge lines, in flat areas in coastal zones, and in areas recently scoured and plucked by glaciers. In two dimensions, outcrops ranged in size from about 30 m2 to 100 m2. We visited a total of 79 sites in the field. Most sites were in metamorphosed Cretaceous flysch, but a few were in intrusive and extrusive igneous rocks (Nelson and others, 1985; Wilson and others, 2015; Winkler, 1992). We collected data that we later used to classify rock mass quality according to four commonly used classification schemes: (1) Rock Mass Quality (Q, for example, Barton and others, 1974; Coe and others, 2005) (2) Rock Mass Rating (RMR, for example, Bieniawski, 1989) (3) Slope Mass Rating (SMR, for example, Moore and others, 2009; Romana, 1995) (4) Geologic Strength Index (GSI, for example, P. Marinos and Hoek, 2000; V. Marinos and others, 2005) We also determined Rock Quality Designation (RQD, for example, Deere and Deere, 1989; Palmström, 1982) and estimated intact rock strength using a Proceq Rock Schmidt Type L Hammer (see RatingsReadMe2023.pdf for details). Schmidt Hammer rebound values were converted to Uniaxial Compressive Strength (UCS) using equations developed for the same rock types that we observed in the field, but at different locations. For flysch, rebound values from the Type L Schmidt Hammer were converted to UCS by the equation shown in Table 3 and Figure 3 of Morales and others (2004). For intrusive igneous rocks, rebound values were converted to UCS by the equation shown in Figure 3 of Aydin and Basu (2005). For extrusive igneous rocks, rebound values were converted to UCS by the Equation 4 in Karaman and Kesimal (2015). Additionally, we collected strike and dip measurements of any observed bedding, fractures, and cleavage. All four rock mass quality classification schemes use data from characteristics of discontinuities present in the rock. Discontinuity data that we collected in the field included: total number of discontinuities, roughness of the surface of the discontinuities, number of sets of discontinuities, type of filling or alteration on the surface of discontinuities, aperture or “openness” of discontinuities, and the amount of water present. Numerical ratings for each of these factors are assigned based on the correlation of field measurements and observations with descriptive rankings. The rankings and any additional details used for Q, RMR, SMR, and GSI classification schemes are shown in Tables 1-3 and Figures 1-2 in the RatingsReadMe2023.pdf. A file of a

Multiple subaerial landslides adjacent to Prince William Sound, Alaska (for example, Dai and others, 2020; Higman and others, 2023; Schaefer and others, 2024) pose a threat to the public because of their potential to generate ocean waves (Barnhart and others, 2021, 2022; Dai and others, 2020) that could affect towns and marine activities. One bedrock landslide on the west side of Barry Arm fjord drew international attention in 2020 because of its large size (~500M m3) and tsunamigenic potential (Dai and others, 2020). As part of the U.S. Geological Survey response to the detection of the potentially tsunamigenic landslide at Barry Arm, as well as a broader effort to evaluate bedrock landslide and tsunamigenic potential throughout Prince William Sound (for example, Schaefer and others, 2024), we continued rock mass quality assessments and collection of structural geology data in northwest and northcentral Prince William Sound in July, 2022 (see associated data from 2021 in Coe and others, 2024). The quality (strength) of a rock mass depends on the properties of intact rock and the characteristics of discontinuities (for example, bedding, fractures, cleavage) that cut the rock. Rock mass quality can be estimated in the field using a variety of classification schemes. In 2022, we accessed sites by boat, helicopter, and hiking. At each field site, we made our measurements at rock outcrops, which were typically found at the base of cliffs, along ridge lines, in flat areas in coastal zones, and in areas recently scoured and plucked by glaciers. In two dimensions, outcrops ranged in size from about 30 m2 to 100 m2. We visited a total of 49 sites in the field. Most sites were in metamorphosed Cretaceous flysch, but a few were in Tertiary granitic rocks (Nelson and others, 1985; Wilson and others, 2015; Winkler, 1992). Of the 49 sites, we collected rock mass quality data and structural data at 39 sites (site names beginning in “rmc”), and only sparse structural data at 10 sites (site names beginning in “srl”). At the 39 full sites, we collected data that were later used to classify rock mass quality according to four commonly used classification schemes: (1) Rock Mass Quality (Q, for example, Barton and others, 1974; Coe and others, 2005) (2) Rock Mass Rating (RMR, for example, Bieniawski, 1989) (3) Slope Mass Rating (SMR, for example, Moore and others, 2009; Romana, 1995) (4) Geologic Strength Index (GSI, for example, P. Marinos and Hoek, 2000; V. Marinos and others, 2005) We also determined Rock Quality Designation (RQD, for example, Deere and Deere, 1989; Palmström, 1982) and estimated intact rock strength using a Proceq Rock Schmidt Type L Hammer (see RatingsReadMe.pdf for details). Schmidt Hammer rebound values were converted to Uniaxial Compressive Strength (UCS) using equations developed for the same rock types that we observed in the field, but at different locations. For flysch, rebound values from the Type L Schmidt Hammer were converted to UCS by the equation shown in Table 3 and Figure 3 of Morales and others (2004). For granitic rocks, rebound values from the Type L Schmidt Hammer were converted to UCS by the equation shown in Figure 3 of Aydin and Basu (2005). Additionally, we collected strikes and dips of any observed bedding, fractures, and cleavage. All four rock mass quality classification schemes use data from characteristics of discontinuities present in the rock. Discontinuity data that we collected in the field included: total number of discontinuities, roughness of the surface of the discontinuities, number of sets of discontinuities, type of filling or alteration on the surface of discontinuities, aperture or “openness” of discontinuities, and the amount of water present. Numerical ratings for each of these factors are assigned based on the correlation of field measurements and observations with descriptive rankings. The rankings and any additional details used for Q, RMR, SMR, and GSI classification schemes are

Multiple subaerial landslides adjacent to Prince William Sound, Alaska (for example, Dai and others, 2020; Higman and others, 2023; Schaefer and others, 2024) pose a threat to the public because of their potential to generate ocean waves (Barnhart and others, 2021, 2022; Dai and others, 2020) that could affect towns and marine activities. One bedrock landslide on the west side of Barry Arm fjord drew international attention in 2020 because of its large size (~500M m3) and tsunamigenic potential (Dai and others, 2020). As part of the U.S. Geological Survey response to the detection of the potentially tsunamigenic landslide at Barry Arm, as well as a broader effort to evaluate bedrock landslide and tsunamigenic potential throughout Prince William Sound (for example, Schaefer and others, 2024), we continued rock mass quality assessments and collection of structural geology data in northwest and northcentral Prince William Sound in July, 2022 (see associated data from 2021 in Coe and others, 2024). The quality (strength) of a rock mass depends on the properties of intact rock and the characteristics of discontinuities (for example, bedding, fractures, cleavage) that cut the rock. Rock mass quality can be estimated in the field using a variety of classification schemes. In 2022, we accessed sites by boat, helicopter, and hiking. At each field site, we made our measurements at rock outcrops, which were typically found at the base of cliffs, along ridge lines, in flat areas in coastal zones, and in areas recently scoured and plucked by glaciers. In two dimensions, outcrops ranged in size from about 30 m2 to 100 m2. We visited a total of 49 sites in the field. Most sites were in metamorphosed Cretaceous flysch, but a few were in Tertiary granitic rocks (Nelson and others, 1985; Wilson and others, 2015; Winkler, 1992). Of the 49 sites, we collected rock mass quality data and structural data at 39 sites (site names beginning in “rmc”), and only sparse structural data at 10 sites (site names beginning in “srl”). At the 39 full sites, we collected data that were later used to classify rock mass quality according to four commonly used classification schemes: (1) Rock Mass Quality (Q, for example, Barton and others, 1974; Coe and others, 2005) (2) Rock Mass Rating (RMR, for example, Bieniawski, 1989) (3) Slope Mass Rating (SMR, for example, Moore and others, 2009; Romana, 1995) (4) Geologic Strength Index (GSI, for example, P. Marinos and Hoek, 2000; V. Marinos and others, 2005) We also determined Rock Quality Designation (RQD, for example, Deere and Deere, 1989; Palmström, 1982) and estimated intact rock strength using a Proceq Rock Schmidt Type L Hammer (see RatingsReadMe.pdf for details). Schmidt Hammer rebound values were converted to Uniaxial Compressive Strength (UCS) using equations developed for the same rock types that we observed in the field, but at different locations. For flysch, rebound values from the Type L Schmidt Hammer were converted to UCS by the equation shown in Table 3 and Figure 3 of Morales and others (2004). For granitic rocks, rebound values from the Type L Schmidt Hammer were converted to UCS by the equation shown in Figure 3 of Aydin and Basu (2005). Additionally, we collected strikes and dips of any observed bedding, fractures, and cleavage. All four rock mass quality classification schemes use data from characteristics of discontinuities present in the rock. Discontinuity data that we collected in the field included: total number of discontinuities, roughness of the surface of the discontinuities, number of sets of discontinuities, type of filling or alteration on the surface of discontinuities, aperture or “openness” of discontinuities, and the amount of water present. Numerical ratings for each of these factors are assigned based on the correlation of field measurements and observations with descriptive rankings. The rankings and any additional details used for Q, RMR, SMR, and GSI classification schemes are

Glacial retreat and mountain-permafrost degradation resulting from rising global temperatures have the potential to impact the frequency and magnitude of landslides in glaciated environments. In the Saint Elias Mountains of southeast Alaska, the presence of weak sedimentary and metamorphic rocks and active uplift resulting from the collision of the Yakutat and North American tectonic plates create landslide-prone conditions (Winkler et al., 2000). We used Landsat imagery to create an inventory of large (>0.1 square km) rock avalanches that occurred along the south flank of the Saint Elias Mountains between 1984 and 2019 as a baseline for present and future changes in landslide magnitude and frequency. This data release presents geographic information system (GIS) and attribute data for 220 rock avalanches in a 3700 square km area of the Saint Elias Mountains, Alaska. Map data consist of polygons delineating total rock avalanche areas (StEliasRockAvalanches.shp), headscarp points (StEliasRockAvHS.shp), and travel distance lines (StEliasRockAvTD.shp). Attribute data for mapped rock avalanches include area (undifferentiated source and deposit areas), travel distance (L), fall height (H), ratio of H/L, and headscarp location (latitude, longitude), elevation, slope, and aspect. Attribute data also include the event date range and information on the Landsat images used to identify and map each rock avalanche. Data are provided as point, line, and polygon shape files (.shp). We also include information on the Landsat images that were used for rock avalanche identification and mapping (LandsatImagery.csv). References: Winkler, G.R., MacKevett, E.M., Plafker, G. Jr., Richter, D.H., Rosenkrans, D.S., and Schmoll, H.R. (2000). A geologic guide to Wrangell-Saint Elias National Park and Preserve, Alaska, A tectonic collage of northbound terranes. U.S. Geological Survey Professional Paper 1616. Reston: U.S. Geological Survey, 166 p.

Glacial retreat and mountain-permafrost degradation resulting from rising global temperatures have the potential to impact the frequency and magnitude of landslides in glaciated environments. In the Saint Elias Mountains of southeast Alaska, the presence of weak sedimentary and metamorphic rocks and active uplift resulting from the collision of the Yakutat and North American tectonic plates create landslide-prone conditions (Winkler et al., 2000). We used Landsat imagery to create an inventory of large (>0.1 square km) rock avalanches that occurred along the south flank of the Saint Elias Mountains between 1984 and 2019 as a baseline for present and future changes in landslide magnitude and frequency. This data release presents geographic information system (GIS) and attribute data for 220 rock avalanches in a 3700 square km area of the Saint Elias Mountains, Alaska. Map data consist of polygons delineating total rock avalanche areas (StEliasRockAvalanches.shp), headscarp points (StEliasRockAvHS.shp), and travel distance lines (StEliasRockAvTD.shp). Attribute data for mapped rock avalanches include area (undifferentiated source and deposit areas), travel distance (L), fall height (H), ratio of H/L, and headscarp location (latitude, longitude), elevation, slope, and aspect. Attribute data also include the event date range and information on the Landsat images used to identify and map each rock avalanche. Data are provided as point, line, and polygon shape files (.shp). We also include information on the Landsat images that were used for rock avalanche identification and mapping (LandsatImagery.csv). References: Winkler, G.R., MacKevett, E.M., Plafker, G. Jr., Richter, D.H., Rosenkrans, D.S., and Schmoll, H.R. (2000). A geologic guide to Wrangell-Saint Elias National Park and Preserve, Alaska, A tectonic collage of northbound terranes. U.S. Geological Survey Professional Paper 1616. Reston: U.S. Geological Survey, 166 p.

Starting in 2003, the U.S. Geological Survey (USGS) Northern Rocky Mountain Science Center in West Glacier, MT, in collaboration with the National Park Service, collected avalanche observations along the Going to the Sun Road during the spring road-clearing operations. The spring road-clearing along Going to the Sun Road utilized a team of avalanche specialists from the USGS and Glacier National Park to communicate the potential avalanche hazard to crews working to clear the road of snow in preparation for summer visitation. The operations typically begin around April 1st and continue through mid-June each year. The dataset includes all of the specific details collected for each avalanche occurrence and conforms to SWAG (American Avalanche Association, 2016. Snow, Weather and Avalanches: Observation Guidelines for Avalanche Programs in the United States (3rd ed). Victor, ID). The records should be viewed as estimates of avalanche characteristics due to the fact that many of the avalanches are too distant or are too dangerous to accurately assess.