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Kattelmann Kattelmann, McGurk McGurk, Berg Berg, Bergman Bergman, Baldwin Baldwin, Hannaford Hannaford (1983)
The isotope profiling snow gage: Twenty years of experienceProc. West. Snow Conf., 51
J. Dozier, J. Melack, D. Marks, K. Elder, R. Kattelmann (1987)
Snow deposition, melt, runoff, and chemistry in a small alpine watershed, Emerald Lake Basin, Sequoia National Park. Final report, 1 July 1984-31 March 1987
(1978)
pp., Forest Service, U.S
J. Richards (1986)
Remote Sensing Digital Image Analysis
J. Zuzel, L. Cox (1975)
Relative importance of meteorological variables in snowmeltWater Resources Research, 11
T. Dunne, L. Leopold (1978)
Water In Environmental Planning
R. Kattelmann, K. Elder (1991)
Hydrologic characteristics and water balance of an Alpine Basin in the Sierra NevadaWater Resources Research, 27
R. Renka (1984)
Algorithm 624: Triangulation and Interpolation at Arbitrarily Distributed Points in the PlaneACM Trans. Math. Softw., 10
R. Schmidt (1982)
Properties of blowing snowReviews of Geophysics, 20
W. Adams (1976)
Areal differentiation of snow cover in east central OntarioWater Resources Research, 12
S. Colbeck, M. Ray (1979)
Proceedings of a Meeting on Modeling of Snow Cover Runoff Held on 26-28 September 1978 at Hanover, New Hampshire.
Distribution of snow water equivalence (SWE) was measured in the Emerald Lake watershed located in Sequoia National Park, California, by taking hundreds of depth measurements and density profiles at six locations during the 1986, 1987 and 1988 water years. A stratified sampling scheme was evaluated by identifying and mapping zones of similar snow properties on the basis of topographic parameters that account for variations in both accumulation and ablation. Elevation, slope, and radiation values calculated from a digital elevation model were used to determine the zones. Of the variables studied, net radiation was of primary importance. Field measurements of SWE were combined with the physical attributes of the watershed and clustered to identify similar classes of SWE. The entire basin was then partitioned into zones for each survey date. Statistical analysis showed that partitioning the watershed on the basis of topographic and radiation variables does produce superior results over a simple random sample.
Water Resources Research – Wiley
Published: Jul 1, 1991
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