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

In-situ measurements and numerical models were used to quantify and improve understanding of the processes governing snowpack dynamics in mountainous terrain. Three studies were conducted in Sequoia National Park in the southern Sierra Nevada, California. The first two studies evaluated and simulated the variability of observed melt rates at the point-scale in a mixed conifer forest. The third study evaluated the accuracy of a distributed snow model run over 1800 km2; a 3600 m elevation gradient that includes ecosystems ranging from semi-arid grasslands to massive sequoia stands to alpine tundra. In the first study, a network of 24 automated snow depth sensors and repeated monthly snow density surveys in a conifer forest were used to measure snow ablation rates for three years. A model was developed to estimate the direct beam solar radiation beneath the forest canopy from upward-looking hemispherical photos and above-canopy measurements. Sub-canopy solar beam irradiance and the bulk canopy metric sky view factor explained the most (58% and 87%, respectively) of the observed ablation rates in years with the least and most cloud cover, respectively; no single metric could explain> 41% of the melt rate variability for all years. In the second study, the time-varying photo-derived direct beam canopy transmissivity and the sky view factor canopy parameter were incorporated into a one-dimensional physically based snowmelt model. Compared to a bulk parameterization of canopy radiative transfer, when the model was modified to accept the time-varying canopy transmissivity, errors in the simulated snow disappearance date were reduced by one week and errors in the timing of soil water fluxes were reduced by 11 days, on average. In the third study, a distributed land surface model was used to simulate snow depth and SWE dynamics for three years. The model was evaluated against data from regional automated SWE measurement stations, repeated catchment-scale depth and density surveys, and airborne LiDAR snow depth data. In general, the model accurately simulated the seasonal maximum snow depth and SWE at lower and middle elevation forested areas. The model tended to overestimate SWE at upper elevations where no precipitation measurements were available. The SWE errors could largely be explained (R2/super” 0.80, p0.01) by distance of the SWE measurement from the nearest precipitation gauge. The results suggest that precipitation uncertainty is a critical limitation on snow model accuracy. Finally, an analysis of seasonal and inter-annual snowmelt patterns highlighted distinct melt differences between lower, middle, and upper elevations. Snowmelt was generally most frequent (70% - 95% of the snow-covered season) at the lower elevations where snow cover was ephemeral and seasonal mean melt rates computed on days when melt was simulated were generally low (3 mm daysuper-1). At upper elevations, melt occurred during less than 65% of the snow-covered period, it occurred later in the season, and mean melt rates were the highest of the region ( 6 mm daysuper-1/super). Middle elevations remained continuously snow covered throughout the winter and early spring, were prone to frequent but intermittent melt, and provided the most sustained period of seasonal mean snowmelt (~ 5 mm day

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.

Snowpacks in the southwestern United States melt intermittently throughout the winter. At some mid-elevation locations, between 7,000 and 7,500 ft, snowpacks appear and disappear, depending on the distribution of storms during relatively dry winters. Some winter precipitation can occur as rain during warm storms and is not reflected in the snow course data. The USDA Natural Resources Conservation Service (NRCS) maintains a system of measuring stations to index snow conditions and predict snowmelt runoff. The three Workman Creek watersheds in the Sierra Ancha Experimental Forest north of Globe were instrumented in late 1938 to study the hydrology of southwestern mixed conifer forests and to determine changes in streamflow and sedimentation resulting from manipulating the forest cover. The watersheds were deactivated in 1983, but they were re-instrumented in June 2000 after the Coon Creek wildfire to measure fire effects on forest hydrology and sediment dynamics. The Rocky Mountain Research Station would like to use NRCS data from the Middle Fork of Workman Creek to reinforce its hydrologic data acquisition and interpretation efforts. Snow water equivalent data can be used to characterize past winter runoff volumes and peak mean daily runoff. Significant regressions were developed between the data sets with coefficients of determination values ranging from 0.40 to 0.77. The relationships defined by these regressions will allow researchers and managers to ascertain the impacts of fire on snowmelt-related hydrologic processes and to estimate winter flows for the years when the installations were closed. They also provide an insight into the snowpackrunoff relationships for intermittent snowpacks that are common at intermediate elevations throughout Arizona.