We used Google Earth Pro 7.1.7 (Google Earth, 2019) and ESRI ArcMap 10.4 software (ESRI, 2017) to search for active rock glaciers. Google Earth Pro provides imagery acquired at multiple dates from the early 1990s to the present, orthorectified to accurate and easily manipulated three-dimensional surfaces. Quick access to multiple images of the same location, captured at different times of day, during different seasons, and across multiple years facilitated active rock glacier identification certainty. We relied on Google Earth Pro and the three-dimensional elevation models it provides for most identifications, supplementing this with National Agricultural Imagery Program (NAIP; NAIP, 2012) plan-view imagery imported into ArcMap 10.4 when Google Earth Pro imagery was unsuitable due to cloud cover, snow cover, or other issues.

We initially began evaluating all montane regions of the contiguous United States but failed to find any evidence of active rock glaciers east of the Rocky Mountain states. Therefore, we focused our efforts on the 11 westernmost states: Arizona (AZ), California (CA), Colorado (CO), Idaho (ID), Montana (MT), New Mexico (NM), Nevada (NV), Oregon (OR), Utah (UT), Washington (WA), and Wyoming (WY). Climatologically, this study region is defined by four zones of the NOAA US climate region system (Karl and Koss, 1984): the Northwest Climate Region (hereafter “NW Region”) of ID, OR, and WA; the Southwest Climate Region (hereafter “SW Region”) of AZ, CO, NM, and UT; the West Climate Region (hereafter “W Region”) of CA and NV; and the West North Central Climate Region (hereafter “WNC Region”) of MT and WY. The major mountain ranges in each of the four regions are the Cascades, Southern Rockies, Sierra Nevada, and Northern Rockies, respectively.

Because glaciers, snowfields, and active rock glaciers are often co-located (Jones et al., 2019a; Knight et al., 2019; Millar and Westfall, 2019), we used two geographic information system (GIS) inventories that identify relevant features to inform target areas for our initial search for active rock glaciers; the Randolph Glacier Inventory (RGI) v6.0 (Fountain et al., 2017; RGI Consortium, 2017) and the National Land Cover Database (NLCD) 2011 (Homer et al., 2015). The RGI is focused only on glaciers, whereas the NLCD identifies any perennial snow or ice feature. From this initial effort and our growing expertise in locating active rock glaciers, we expanded our search areas to explore alpine regions far from any inventoried glaciers or perennial snow or ice features but that could potentially host active rock glaciers.

Active rock glaciers were identified manually by their distinct surface characteristics (Aoyama, 2005; Haeberli et al., 2006). These characteristics include ridge and swale surface banding resulting from differential flow rates and terminal and lateral slopes over-steepened beyond the angle of repose, presumably cemented by interstitial ice. Common mass wasting processes responsible for individual fragments of regolith traveling downslope result in accumulations at or below the angle of repose. Similar approaches to active rock glacier identification, focusing on surface topography characteristics identified from aerial and satellite imagery, have been applied in other previous research (Eztelmuller et al., 2007; Janke, 2007; Degenhardt, 2009; Janke et al., 2015; Millar and Westfall, 2019).

We focused our inventory efforts on identifying active rock glaciers that, surfacely, appear to contain appreciable internal ice fractions and are presently or were recently flowing downslope. We follow previous studies that omit features with expansive bare glacial ice in their accumulation zones or obvious supraglacial lakes and/or streams as those are clearly debris-covered glaciers but make no further attempt to discriminate active rock glaciers from fully mantled debris-covered glaciers (Bodin et al., 2010; Berthling, 2011; Perucca and Angillieri, 2011). After the exponentially larger study area than any previously investigated, a second major distinction between our active rock glacier inventory and classification system and other previous US rock glacier inventory efforts is that we intentionally attempt to exclude inactive rock glaciers. We ignored potential candidate features lacking over-steepened terminal slopes and/or present evidence of advanced surficial soil development, such as expansive vegetation growth, both of which imply the rock glacier has a small internal ice fraction and has not flowed downslope recently.

When identifying a candidate active rock glacier, plan-view images were initially viewed at 1:2000 scale or better. Once suspected ridge and swale flow banding and over-steepened terminal and lateral slopes were identified, image scale was greatly increased. All available clear sky images of the same scene were then evaluated, with plan views being replaced by oblique views from multiple angles and multiple scales and three-dimensional topography exaggerated by 50 %. The perimeter of individual active rock glaciers were manually delineated using Google Earth Pro. Usually, sharp changes in slope were evident, indicating a perimeter boundary between the thickened ice-bound regolith of the active rock glacier and the surrounding unconsolidated talus of the adjacent slope. Additionally, lower active rock glacier margins often abut well-vegetated terrain. The upper margins are often defined by a change in slope, from the steep slopes of exposed bedrock and unconsolidated talus in the rock glacier accumulation zone to the more gentle slope of the main body of the ice-thickened active rock glacier. Generally, active rock glacier boundary confidence is highest along sharp terminal and lateral margins and lowest along accumulation zones where exposed bedrock is not present. When considering multi-lobate active rock glaciers, we focused on distinct accumulation zones to ascribe individual lobes to a given active rock glacier. While every effort was made to apply these guidelines consistently, we readily concede that identifying and delineating rock glaciers remotely is technically challenging and subject to individual interpretation and best professional judgment. Past evaluation of remote rock glacier inventory methods has shown high degrees of variability between even well-trained image analysts, particularly with regard to rooting zones (Brardinoni et al., 2019), and we support ongoing efforts to standardize methods for rock glacier inventories within the research community.

Understandably, there can be some disagreement between analysts regarding rock glacier classification (Brardinoni et al., 2019). To partially address this ambiguity all features identified as active rock glaciers were subsequently assigned to a three-tier classification system based on surface characteristics known to correlate with downslope movement motivated by deformation of the internal ice-rock matrix (Fig. 2), particularly the presence and extent of ridge and swale flow banding (Haeberli et al., 2006; Brenning et al., 2012; Liu et al., 2013). Class 1 rock glaciers appear to be highly active, exhibit unambiguous, complex, and extensive ridge and swale flow banding, and have substantially over-steepened terminal and lateral boundaries. Class 2 rock glaciers appear to be intermediately active, exhibit some pronounced ridge and swale flow banding, and have somewhat over-steepened terminal and lateral boundaries. Class 3 rock glaciers appear to be minimally active, exhibit sparse ridge and swale flow banding, and have intermittently over-steepened terminal and lateral boundaries.

To characterize the topographic characteristics of the individual active rock glaciers identified, elevation data were extracted from the USGS National Elevation Dataset (NED) 1/3 arcsec (≈ 10 m) digital elevation model (USGS, 2017). Topographic variables of elevation, slope, aspect, and insolation were determined using Spatial Analyst tools in ArcMap 10.4 (ESRI, 2017). Active rock glacier area was calculated in square kilometers, while slope and aspect were calculated in degrees. Aspect was decomposed to an eastness and northness component (Nussear et al., 2009), and solar insolation was calculated in watt hours per square meter. To characterize the climate of the active rock glaciers, climate data, including air temperature and precipitation, were also extracted from PRISM 1981–2010 climate normals (PRISM, 2017) using Spatial Analyst tools in ArcMap 10.4. PRISM data were also used to calculate several derivative atmospheric variables, such as fraction of precipitation falling as snow and mean vapor pressure deficit, using the Raster Calculator tool in ArcMap 10.4. These publicly available climate data have a spatial resolution of 800 m, with an average daily accumulated total precipitation bias of less than 2.5 % in the western United States for 1961–2001 (DiLuzio et al., 2008). Active rock glacier classification and area clustering analysis using Moran's I statistics helped further describe active rock glacier spatial distributions (Cliff and Ord, 1971; Senn, 1976; Tiefelsdorf, 2002).

We identified 10 332 active rock glaciers (Class 1 = 7042, Class 2 = 2415, Class 3 = 875) across the western United States (Fig. 3, Table 2), after removing 146 small (< 0.01 km2) Class 3 rock glaciers following glaciological convention of area thresholds (Navarro and Magnusson, 2017). This minimum area threshold was also selected due to decreased confidence in extremely small rock glacier identification, as well as an attempt to ensure all features included in the inventory were active rock glaciers exhibiting downslope movement modulated by internal deformation of ice, something that would be exceedingly rare in any rock glaciers smaller than 0.01 km2. Average active rock glacier area is 0.10 km2, and the average distance between each active rock glacier and its nearest neighbor is 0.69 km. Contiguous US active rock glaciers have an average elevation of 3144.3 m, an average slope of 20.51∘, an average eastness of -0.007, and an average northness of 0.066 (Fig. 4). Climatically, the average annual active rock glacier precipitation is 350.2 mm, the average air temperature is 0.19 ∘C, the average dew point temperature is -8.37 ∘C, and the average vapor pressure deficit is 4.52 hPa (Fig. 4). Differences were noted in rock glacier topographic and climatic attributes between NOAA climate regions (Fig. 5). The overall active rock glacier centroid (41.5332, -110.7083) is located in the southwest corner of the WNC Region (Fig. 3). The centroids of each of the three active rock glacier classes – Class 1 = (41.5112, -110.5556), Class 2 = (41.7012, -111.0141), Class 3 = (41.2470, -111.0942) – can be contained by a minimum bounding area circle with a diameter of 57.7 km. Moran's I analysis shows active rock glacier classifications and areas are significantly clustered (Tables 3 and 4).

Individually, contiguous US active rock glaciers are found across widely disparate montane environments, but their overall distribution unambiguously favors relatively high, arid mountain ranges with sparse vegetation. Active rock glacier populations in those regions are denser, and the individual active rock glaciers making up those populations are larger and exhibit surficial evidence of higher activity than those of active rock glaciers found in humid mountain ranges with copious vegetation. Active rock glaciers of the NW Region are largest and most densely concentrated in the Sawtooth Mountains of Idaho. Active rock glaciers of the SW Region are largest and most densely concentrated in the Front Range and San Juan Mountains of Colorado and the Uinta Mountains of Utah. Active rock glaciers of the W Region are largest and most densely concentrated in the Sierra Nevada of California. Active rock glaciers of the WNC Region are largest and most densely concentrated in the Beartooth Mountains of Montana and the Absaroka Range of Wyoming.

The completeness and accuracy of the active rock glacier inventory were qualitatively and quantitatively supported by numerous field observations and remote sensing classification verification by multiple GIS analysts familiar with the alpine cryosphere generally and rock glaciers specifically. The lead author personally visited more than 50 active rock glaciers during field campaigns for related research, and more than 150 individual active rock glaciers with precise coordinates listed in past peer-reviewed research were examined remotely when developing our classification criteria. While developing the inventory, dozens of test areas measuring 500 km2 or greater in all 11 western states were checked by two other well-trained GIS analysts familiar with the alpine cryosphere for “missing” active rock glaciers not originally identified by the lead author, and none were found. When considering the three-class active rock glacier activity classification scheme, a test subset of 60 randomly selected active rock glaciers were classified in isolation using the qualitative classification rules previously described by five GIS analysts familiar with the alpine cryosphere generally and rock glaciers specifically. Individual analyst classifications were then compared using Tukey's HSD (honestly significant difference) test (α=0.05), yielding no significant differences between analyst interpretations. Class 1 rock glaciers showed a 92 % agreement between analysts, Class 2 rock glaciers an 87 % agreement between analysts, and Class 3 rock glaciers a 79 % agreement between analysts.

As this active rock glacier inventory is of unprecedented spatial extent, no analogous previous inventories exist for us to make direct and detailed GIS comparisons to over the entire study region. While smaller regional-scale US rock glacier inventories have been compiled in the past, none of these inventories are publicly available as geospatial data sets. Coarse-scale comparisons, however, were completed based on reported findings and figures published in previous studies presenting the aforementioned smaller regional US rock glacier inventories. To compare our active rock glacier inventory and previous regional US rock glacier inventories, we created polygons using the corner coordinates of low-resolution regional study maps from peer-reviewed articles highlighting one Colorado rock glacier inventory (Janke, 2007) and two California rock glacier inventories (Millar and Westfall, 2008; Liu et al., 2013). Polygons representing the extents of maps from the smaller regional inventories were then used to select simple counts of active rock glaciers identified in our inventory and to compare them to counts of rock glaciers reported in the aforementioned studies. The 2007 Colorado inventory reported 28 “active” rock glaciers, the category in that study which was defined most similarly to our Class 1 classification criteria, in and around Rocky Mountain National Park, while we identified 29 Class 1 rock glaciers in the same region. The 2008 California study reported 184 rock glaciers in the central Sierra Nevada but used a more inclusive “rock-ice feature” definition that deliberately includes inactive rock glaciers than our active rock glacier classification criteria, while we identified 116 active rock glaciers of any class in the same region. The 2013 California study (Liu et al., 2013) reported 67 “active” rock glaciers, a subset of features identified in the 2008 study and the category in that study most similar to our Class 1 classification criteria, while we identified 88 active rock glaciers in largely the same study region. These three comparisons, and the agreement between the aforementioned inventories and our findings, greatly bolster our confidence in the overall accuracy of the PSUARGI.

Though our classification system and deliberate omission of inactive rock glaciers due to limitations in the analysis techniques (Brardinoni et al., 2019) and data sets available will undoubtedly preclude some desired applications of this active rock glacier inventory such as validating permafrost extent models (Boeckli et al., 2012; Schmid et al., 2015), we believe it represents an import step towards a fuller understanding of rock glaciers of the contiguous United States regardless. Several potential uses of this active rock glacier inventory are readily apparent, and we hope all will be explored by the research community in due time. Most immediately, this inventory will allow for the rapid identification of potential field sites for researchers interested in direct study of individual rock glaciers. Many researchers likely do not appreciate just how close their universities or labs already are to active rock glaciers, and this inventory would also offer powerful insights for any researchers eager to inventory inactive rock glaciers. Water resource managers in the arid western United States should also take note of active rock glaciers as the sizes and locations of these features are likely to play an increasingly important role in changing water supplies (Wagner et al., 2020a, b). Finally, we hope this inventory will aid ongoing refinement and future implementation of truly automated rock glacier detection methods. The ability to quickly, accurately, and objectively identify rock glaciers from presently available remote sensing imagery without relying on skilled visual image analysts or needing to address the inevitable interpretation disagreements between those analysts would be an invaluable tool for climatologists, ecologists, and many others (Brenning, 2009).