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Estimating the spatial distribution of snow water equivalent in the world's mountains

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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 article is categorized under: Science of Water > Methods
Snow water equivalent (SWE) over the Sierra Nevada on April 1, 2014, estimated by different methods: (a) interpolation from snow pillows and satellite measurements of snow covered area; (b) reconstruction using snow‐covered area from MODIS and snowmelt calculated with data from NLDAS; (c) calculated with brightness temperatures from the passive microwave sensor AMSR2; (d) modelled by the snow data assimilation system (SNODAS). The SWE images are overlaid on a MODIS image. Although clouds obscure parts of each daily MODIS image, the snow extent is interpolated and smoothed over the daily observations. Images are projected at 500 m resolution.
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Comparison of three methods to estimate snow water equivalent (SWE) on Mt. Shasta, California, on April 1, 2014, in a small area within a small region (28 × 30 km) that Figure covers. The top row identifies the SWE values: (a) interpolation; (b) snow data assimilation system (SNODAS); and (c) reconstruction. The bottom row identifies the differences: (d) interpolation minus reconstruction; and (e) SNODAS minus reconstruction.
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Snow pillow locations (the red stars) superimposed on two ‘July 2011’ images of fractional snow cover—estimated by a spectral unmixing model applied to Landsat 7 data at 30 m resolution—in the Tuolumne and Merced River basins in the Sierra Nevada, California. Significant snow remains in the basin after all pillows are bare. Table lists the coordinates of the pillows and their 2011 melt‐out date.
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