An independent technical study evaluating the use of snowpack depth measurements to estimate snow water equivalent (SWE) of shallow and ephemeral snowpacks in the Great Salt Lake Desert Basin, located in Utah, Nevada, and Idaho. A parameterized bulk snow density model was combined with mean air temperature measurements to predict snow water equivalent in the Great Salt Lake Desert Basin using only snowpack depth measurements and prior 10-day average daily mean air temperatures. The model was developed using historic snowpack data obtained from a limited number of automated snowpack telemetry (SNOTEL) and weather stations within and near the Basin. Model results from lower-elevation, shallow and ephemeral snowpacks may be used to supplement data obtained from existing SNOTEL stations, sparsely located in the higher elevations of the Basin, to create a more-complete and accurate prediction of the Basin’s snow water equivalent, which may be used to better-manage the water demands of the Basin’s surrounding populations.

Estimating the spatial distribution of snow water equivalent (SWE) in mountainous terrain is currently the most important unsolved problem in snow hydrology. Several methods can estimate the amount of snow throughout a mountain range: (1) Spatial interpolation from surface sensors constrained by remotely sensed snow extent provides a consistent answer, with uncertainty related to extrapolation to unrepresented locations. (2) The remotely sensed date of disappearance of snow is combined with a melt calculation to reconstruct the SWE back to the last significant snowfall. (3) Passive microwave sensors offer real‐time global SWE estimates but suffer from several problems like subpixel variability in the mountains. (4) A numerical model combined with assimilated surface observations produces SWE at 1‐km resolution at continental scales, but depends heavily on a surface network. (5) New methods continue to be explored, for example, airborne LiDAR altimetry provides direct measurements of snow depth, which are combined with modelled snow density to estimate SWE. While the problem is aggressively addressed, the right answer remains elusive. Good characterization of the snow is necessary to make informed choices about water resources and adaptation to climate change and variability. WIREs Water 2016, 3:461–474. doi: 10.1002/wat2.1140

This report was prepared to provide generalized information for planning and design purposes in connection with Soil Conservation Service watershed protection and flood prevention programs.

Accurately estimating basin‐wide snow water equivalent (SWE) is the most important unsolved problem in mountain hydrology. Models that rely on remotely sensed inputs are especially needed in ranges with few surface measurements. The NASA Airborne Snow Observatory (ASO) provides estimates of SWE at 50 m spatial resolution in several basins across the Western U.S. during the melt season. Primarily, water managers use this information to forecast snowmelt runoff into reservoirs; another impactful use of ASO measurements lies in validating and improving satellite‐based snow estimates or models that can scale to whole mountain ranges, even those without ground‐based measurements. We compare ASO measurements from 2013 to 2015 to four methods that estimate spatially distributed SWE: two versions of a SWE reconstruction method, spatial interpolation from snow pillows and courses, and NOAA's Snow Data Assimilation System (SNODAS). SWE reconstruction downscales energy forcings to compute potential melt, then multiplies those values by satellite‐derived estimates of fractional snow‐covered area to calculate snowmelt. The snowpack is then built in reverse from the date the snow is observed to disappear. The two SWE reconstruction models tested include one that employs an energy balance calculation of snowmelt, and one that combines net radiation and degree‐day approaches to estimate melt. Our full energy balance model, without ground observations, performed slightly better than spatial interpolation from snow pillows, having no systematic bias and 26% mean absolute error when compared to SWE from ASO. Both reconstruction models and interpolation were more accurate than SNODAS.

Since 1982, under an agreement between the California Department of Water Resources and the USDA Forest Service, snow sensors have been installed and operated in Forest Service-administered wilderness areas in the Sierra Nevada of California. The sensors are to be removed by 2005 because of the premise that sufficient data will have been collected to allow "correlation" and, by implication, prediction of wilderness snow data by nonwilderness sensors that are typically at a lower elevation. Because analysis of snow water equivalent (SWE) data from these wilderness sensors would not be possible until just before they are due to be removed, "surrogate pairs" of high- and low-elevation snow sensors were selected to determine whether correlation and prediction might be achieved. Surrogate pairs of sensors with between 5 and 15 years of concurrent data were selected, and correlation and regression were used to examine the statistical feasibility of SWE prediction after "removal" of the wilderness sensors. Of the 10 pairs analyzed, two pairs achieved a correlation coefficient of 0.95 or greater. Four more had a correlation of 0.94 for the accumulation period after the snow season was split into accumulation and melt periods. Standard errors of estimate for the better fits ranged from 15 to 25 percent of the mean April 1 snow water equivalent at the high-elevation sensor. With the best sensor pairs, standard errors of 10 percent were achieved. If this prediction error is acceptable to water supply forecasters, sensor operation through 2005 in the wilderness may produce predictive relationships that are useful after the wilderness sensors are removed