In order to compare the effect of snow ephemerality on soil moisture patterns, we first investigated snow and soil moisture response for SNOTEL and SCAN stations within the Great Basin. To evaluate how soil moisture varies based on snowpack parameters during a drought year (water year 2015) and a non-drought year (water year 2016), we chose two SNOTEL stations – Porter Canyon (ID 2170, elevation 2191 m) and Big Creek Summit (ID 337, elevation 2647 m) – that differ in elevation but are in close proximity. We used average snow water equivalent (SWE) data from snow pillows to determine snow cover. We categorized each day as snow covered if continuous SWE was greater than 0.1 cm. We then designated site years as seasonal or ephemeral depending on if continuous snow cover was greater or less than 60 days, respectively. For these stations, we compared percent soil moisture, at 5 and 50 cm soil depth along with snow depth, and SWE. We then also acquired soil moisture and SWE data at 5 and 50 cm for all the SNOTEL and SCAN stations in the Great Basin in water years 2014–2016 and categorized site years from those stations as ephemeral or seasonal. We discarded years and stations containing more than 7 days of continuous missing data or soil moisture values that were 0 %. To compare the timing of snow and peak soil moisture, we then took the difference between the day of last snow and the day with peak median 10-day soil moisture for each year at each site. It should be noted that ablation on the snow pillow may be impacted by differences in ground heat flux and co-location issues with the soil moisture sensors. We also calculated the coefficient of variation (CV; 1 standard deviation divided by the mean) of soil moisture for each year at each station.

We mapped ephemeral snow across the Great Basin using two methods: spectral remote sensing with MODIS data and modeled Snow Data Assimilation System (SNODAS) data. We used Google Earth Engine to analyze the data, which is a cloud-based computing platform optimized for mapping large datasets (Gorelick et al., 2017). The MODIS dataset used was the 2010 MODIS/Terra Snow Cover Daily L3 Global 500 m Grid (MOD10A) and we used the normalized difference snow index (NDSI) with parameters outlined in Hall et al. (2006) to find fractional snow-covered data. The equation for calculating NDSI in MOD10 is

(1) NDSI = Band 4 - Band 6 Band 4 + Band 6 .

A pixel is then mapped as containing fractional snow using the NDSI value, as long as the reflectance in Band 2 is >10 % (Hall et al., 2001). We classified all pixels with a snow fraction of 30–100 as snow, pixels with snow fractions between 0 and 30 as no snow, and pixels that had all other designations as other. We also used an algorithm derived from Thompson and Lees (2014) to minimize the impact of cloud cover in our MODIS data. The algorithm “grows” the boundaries of all areas containing snow and reclassifies pixels that were classified as other to snow if the corresponding pixels in the previous image were classified as snow. It also reclassifies pixels that were classified as other to no snow if the corresponding pixels in the previous image were no snow.

To determine the number of ephemeral and seasonal snow events, we used a Google Earth Engine function to note the day of the water year when snow appeared (when a pixel went from being classified as no snow in the previous day to classified as snow in the current day) and when snow disappeared (a pixel went from being classified as snow in the previous day to being classified as no snow in the current day), and we determined the length of snow cover by subtracting the day of snow appearance from the day of snow disappearance. If the length of snow cover was <60 days, then the snow event was classified as ephemeral. Otherwise, if the length of snow cover was ≥60 days, the snow event was categorized as seasonal. In addition to these metrics, we derived a snow seasonality metric (SSM) to quantify a MODIS pixel's tendency to have ephemeral or seasonal snow, rather than a binary metric like <60 days. The SSM is depicted in Eq. (2) and it works by classifying every day where there was seasonal snow present as 1 and every day where there was ephemeral snow present as −1, and then averaging all −1 and +1 values. This created a −1 to 1 scale, where −1 signifies that all the snow-covered days in a given pixel within 1 water year were ephemeral and +1 signifies that they were all seasonal.

(2) SSM = Days Seasonal - Days Ephemeral Days Total

Additionally, we discarded all instances where snow was absent for 1 day only from the overall record of snow disappearance and appearance because there were numerous artifacts from the MOD10A NDSI processing that lead to single-day snow disappearance during long stretches of snow cover. The 1-day snow events were also removed from the SNODAS algorithm to make both algorithms more consistent. For each water year from 2005 to 2014, we recorded the maximum total number of days where snow was present (to be referred to as the maximum snow duration).

To determine the relationship between elevation and snow seasonality, we took the average maximum snow duration across water years 2005–2014 and used elevation and aspect as measured by a digital elevation model (DEM) obtained from the Shuttle Radar Topography Mission resampled to the same resolution with bilinear sampling (Farr et al., 2007). To calculate northness, we used the following equation:

(3) Northness = cos aspect ⋅ π 180 .

We then categorized each MODIS pixel based on five 500 m elevation bins from a range of 1000 to >3000 m. Then, to remove bias based on the size of each bin, we used random sampling to make each bin contain the same number of points as the least full bin (13 548 points that were >3000 m). Then we combined each resampled bin into one dataset and created heat maps to compare the elevation vs. the average maximum snow duration. We also use the same method to compare aspect to average maximum snow duration using eight 45∘ bins from a range of 0 to 360∘. We randomly sampled 195 163 points from each bin (with the size of the bin ranging from 315 to 360∘). After resampling, we combined all the bins together and split them into three elevation categories: low elevation (elevation <1500 m), medium elevation (1500≥ elevation <2500), and high elevation (elevation ≥ 2500 m). Then, we resampled again to 82 823 points per bin (the size of the high-elevation bin).

We used SNODAS data to differentiate the mechanisms that cause snow to become ephemeral. The four mechanisms were assigned if the net ablation (or rain) exceeded 50 % of the total winter precipitation (Fig. 2): (1) a mixture of rain and snow limiting snow accumulation (the rain–snow transition), (2) snowpack loss due to sublimation, (3) snowpack loss due to melt, and (4) snowpack loss due to wind scour. We determined the prevailing mechanism in each 1000 m SNODAS pixel in each year. We used Google Earth Engine to execute the modeled algorithm on each 1000 m SNODAS pixel in the Great Basin. We then chose 6 years (2009–2014) and created histograms of each mechanism by elevation for each year.