Hourly mass and snow energy balance measurements from Mammoth Mountain, CA USA, 2011–2017

Bair, Edward H.; Davis, Robert E.; Dozier, Jeff

doi:10.5194/essd-2017-114

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References 12 publications

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“…Interpolation and SNODAS produce more total SWE volume (5.40 and 5.13 km 3 ) than reconstruction (3.44 km 3 ) while AMSR2 estimates much less (1.41 km 3 ). Preliminary comparisons with ASO suggest that SNODAS may overestimate SWE in spring, possibly because of a reliance on snow pillow measurements, which late in the season can hold more SWE than the average of the nearby surrounding terrain because they impede drainage to the underlying soil …”

Section: Comparison Between Interpolation Reconstruction Passive MImentioning

confidence: 99%

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

2016

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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.

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Section: Comparison Between Interpolation Reconstruction Passive MImentioning

confidence: 99%

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

2016

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View full text Add to dashboard Cite

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Hourly mass and snow energy balance measurements from Mammoth Mountain, CA USA, 2011–2017

Abstract: Abstract:The mass and energy balance of the snowpack govern its evolution. Direct measurement of these fluxes is essential for modeling the snowpack, yet there are few sites where all the relevant measurements are taken. Mammoth Mountain CA USA is home to the 10 Cold

Cited by 1 publication

References 12 publications

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

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

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