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.

The availability and variability of snowmelt has become a serious concern because of increased water demand, and because of the high degree of uncertainty related to climate variability posing a threat to the magnitude and timing of this precious resource. Understanding the geophysical controls and interannual variability of the spatial patterns of seasonal montane snowpacks are critical for understanding the effects of a warmer climate on the snowpack water storage. To explicitly resolve snow hydrological controls in complex montane environments, it is necessary to provide high resolution spatially and temporally distributed estimates of snow water equivalent, while also taking into consideration the uncertainties in the system. Toward this end, this dissertation developed a retrospective data assimilation technique (SWE reanalysis) that aimed to optimally merge VIS-NIR remote sensing data into a snow prediction model, and at the same time, account for the limitations of measurements, forcings, and model errors. The SWE reanalysis was: first developed and implemented over a small region, in order to investigate the performance of the methods under their nominal scenarios; second implemented for the full Landsat-5 record (27 year) over a regional scale domain in order to test accuracy and gain insight on the spatial and interannual controls on the SWE patterns; third extended to the entire Sierra Nevada in order to benchmark the reanalysis for its application to the full Sierra Nevada and to preliminarly [i.e. preliminarily] understand what are the spatial controls on SWE patterns. The key findings of this dissertation can be summarized as follows: 1) The SWE reanalysis approach provided accurate spatially and continuous estimates of SWE and of its uncertainties due to measurement, forcings, and model errors. 2) The methods were found to be robust to input errors such as biases in solar radiation and precipitation, and robust to the number of available VIS-NIR observations. 3) The application of the methods over the Kern watershed for the full Landsat-5 record suggested that SWE accumulation patterns were in general not interannually consistent and that the interannual variability was dependent on whether a dry or wet year was analyzed. 4) The trend test analysis showed that peak-SWE and day-of-peak have not drastically changed over the analyzed 27 years for the Kern River watershed, but suggested that the lower elevations may be more susceptible to climate variability and change. 5) Elevation was found to be the primary control on spatial patterns of peak-SWE and day-of-peak for the entire Sierra Nevada range; however different patterns were found across the watersheds of the Sierra Nevada depending on their location. Ultimately, the methods can be applied to the full Sierra Nevada and other montane regions over the modern remote sensing record to generate a dataset that should be useful to scientists and practitioners not only in hydrology, but other fields where seasonal snow processes are a key driver such as biogeochemistry, mountain meteorology, and water resource management.