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

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

Seasonal snowpack is a vital water resource that impacts downstream water availability. However, accurately estimating snow storage and characterizing its spatiotemporal distribution remain challenging, in particular for data-scarce regions such as High Mountain Asia (HMA). In this dissertation, a newly developed snow reanalysis method is used to estimate snow water equivalent (SWE) over the HMA region, assessing its spatiotemporal distribution and quantifying the regional snow storage. The method assimilates fractional snow-covered area (fSCA) from the Landsat and MODIS platforms, over the joint Landsat-MODIS record (Water Year (WY) 2000 - 2017). A fine resolution (16 arc-second, ~480 m) and daily High Mountain Asia snow reanalysis (HMASR) dataset is derived and analyzed in the dissertation. The key conclusions are summarized as follows: 1) Snowfall precipitation is found underestimated in most precipitation products with sizeable uncertainty when evaluated in sub-domains of HMA. The research shows the potential of using satellite snow observations as a constraint, to infer biases and uncertainties in snowfall precipitation in remote regions and complex terrain where in-situ stations are very scarce. 2) Through examining the HMASR dataset, the domain-wide peak seasonal snow storage is quantified as 163 km3 when aggregated across the full HMA domain and averaged across WYs 2000-2017, with notable interannual variations between 114 km3 and 227 km3. 3) Existing global snow products over HMA on average underestimate the peak snow storage by 33% 52% over the entire HMA, and the uncertainty in peak snow storage estimates is primarily explained by accumulation season snowfall (88%) over HMA, partly due to a wide range (uncertainty) in precipitation (snowfall). Ultimately, the snow storage and its spatiotemporal variations characterized in this work can be used to understand the role of seasonal snowpack in the regional climate and water cycle over this region.

This book presents the prevailing state of snow-climate science for researchers and advanced students.

There is an undisputed need to increase accuracy of snow cover estimation in regions comprised of complex terrain, especially in areas dependent on winter snow accumulation for a substantial portion of their annual water supply, such as the Western United States, Central Asia, and the Andes. Presently, the most pertinent monitoring and research needs related to alpine snow cover area (SCA) are: (1) to improve SCA monitoring by providing detailed fractional snow cover (FSC) products which perform well in temporal/spatial heterogeneous forested and/or alpine terrains; and (2) to provide accurate measurements of FSC at the watershed scale for use in snow water equivalent (SWE) estimation for regional water management. To address the above, the presented research approach is based on Landsat Fractional Snow Cover (Landsat-FSC), as a measure of the temporal/spatial distribution of alpine SCA. A fusion methodology between remotely sensed multispectral input data from Landsat TM/ETM+, terrain information, and IKONOS are utilized at their highest respective spatial resolutions. Artificial Neural Networks (ANNs) are used to capture the multi-scale information content of the input data compositions by means of the ANN training process, followed by the ANN extracting FSC from all available information in the Landsat and terrain input data compositions. The ANN Landsat-FSC algorithm is validated (RMSE ̃0.09; mean error ̃0.001-0.01 FSC) in watersheds characterized by diverse environmental factors such as: terrain, slope, exposition, vegetation cover, and wide-ranging snow cover conditions. ANN input data selections are evaluated to determine the nominal data information requirements for FSC estimation. Snow/non-snow multispectral and terrain input data are found to have an important and multi-faced impact on FSC estimation. Constraining the ANN to linear modeling, as opposed to allowing unconstrained function shapes, results in a weak FSC estimation performance and therefore provides evidence of non-linear bio-geophysical and remote sensing interactions and phenomena in complex mountain terrains. The research results are presented for rugged areas located in the San Juan Mountains of Colorado, and the hilly regions of Black Hills of Wyoming, USA.