HR: 0800h
AN: C21B-0460    [Abstracts]
TI: Embedded sensor network design for spatial snowcover
AU: * Rice, R
EM: rrice@ucmerced.edu
AF: University of California, Merced, P.O. Box 2039, Merced, CA 95344, United States
AU: Molotch, N
EM: molotch@seas.ucla.edu
AF: University of California, Los Angeles, Dept of Civil Eng., Los Angeles, ca 90095-1593, United States
AU: Bales, R C
EM: rbales@ucmerced.edu
AF: University of California, Merced, P.O. Box 2039, Merced, CA 95344, United States
AB: Scaling point observations of snow water equivalent (SWE) to model grid-element scales is particularly challenging given the considerable sub-grid variability in snow accumulation over complex terrain. In an effort to capture this sub-grid variability and provide spatially explicit ground-truth snow data an embedded snow sensor network was designed and installed in Yosemite and Sequoia National Park, and in the Valles Caldera National Preserve. Extensive snow surveys were used to guide the installation of the network and to relate the observations to more detailed spatial SWE fields. Three years of continuous spatial and temporal data from both Yosemite National Park and the Valles Caldera indicate that accumulation and ablation rates can vary as much as 50% based on variability in topography and vegetation. These snow distribution patterns are especially apparent in the open forests of Yosemite and Sequoia National Parks and the Valles Caldera where vegetation structure largely controls variability in snow distribution. Comparisons of SWE estimates with historical snow course data shows that a single point measurement is a poor estimator of snow depth over a homogeneous area, but 4 or more measurement points can reduce the uncertainty by 50%. Further analyzes indicated that an optimal snow depth network consists of 7 to 10 snow depth sensors. These spatial and temporal measurement arrays will improve remotely sensed and modeled SWE estimates by providing robust, spatially explicit ground- truth values of snowpack states.
DE: 0736 Snow (1827, 1863)
DE: 0740 Snowmelt
DE: 0758 Remote sensing
DE: 1879 Watershed
DE: 1880 Water management (6334)
SC: Cryosphere [C]
MN: 2007 Fall Meeting