Evaluation of the representativeness of automated = snow water equivalent sensors in the Rio Grande headwaters using = intensive field observations, remotely sensed snow cover data, and = distributed snowme

From: Subject: Evaluation of the representativeness of automated snow water equivalent sensors in the Rio Grande headwaters using intensive field observations, remotely sensed snow cover data, and distributed snowme Date: Sun, 22 May 2005 21:00:27 -0700 MIME-Version: 1.0 Content-Type: multipart/related; type="text/html"; boundary="----=_NextPart_000_0044_01C55F11.481F9800" X-MimeOLE: Produced By Microsoft MimeOLE V6.00.2900.2180 This is a multi-part message in MIME format. ------=_NextPart_000_0044_01C55F11.481F9800 Content-Type: text/html; charset="Windows-1252" Content-Transfer-Encoding: quoted-printable Content-Location: http://www.agu.org/cgi-bin/SFgate/SFgate?language=English&verbose=0&listenv=table&application=fm04&convert=&converthl=&refinequery=&formintern=&formextern=&transquery=an%3d%22U53A-0701%22&_lines=&multiple=0&descriptor=%2fdata%2fepubs%2fwais%2findexes%2ffm04%2ffm04%7c1000%7c3534%7cEvaluation%20of%20the%20representativeness%20of%20automated%20snow%20water%20equivalent%20sensors%20in%20the%20Rio%20Grande%20headwaters%20using%20intensive%20field%20observations%2c%20remotely%20sensed%20snow%20cover%20data%2c%20and%20distributed%20snowme%7cHTML%7clocalhost:0%7c%2fdata%2fepubs%2fwais%2findexes%2ffm04%2ffm04%7c36563219%2036566753%20%2fdata2%2fepubs%2fwais%2fdata%2ffm04%2ffm04.txt Evaluation of the representativeness of automated = snow water equivalent sensors in the Rio Grande headwaters using = intensive field observations, remotely sensed snow cover data, and = distributed snowme

2004 Fall=20 = Meeting
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Cite = abstracts as=20 Author(s) (2004), Title, Eos Trans. AGU,
85(47), = Fall=20 Meet. Suppl., Abstract = xxxxx-xx

Your query was:: an=3D"U53A-0701"

HR: 1340h
AN: U53A-0701
TI: Evaluation of = the=20 representativeness of automated snow water equivalent sensors in = the Rio=20 Grande headwaters using intensive field observations, remotely = sensed snow=20 cover data, and distributed snowmelt models
AU: *=20 Molotch, N P
EM: molotch@cires.colorado.edu
AF: Cooperative Institute for Research in Environmental = Sciences,=20 University of Colorado, Boulder, 216 UCB, Boulder, CO 80309-0216 = United=20 States
AU: Bales, R C
AF: = Division of Engineering, University of California, = Merced, P.O.=20 Box 2039 , Merced, CA 95344 United States
AB: In=20 spring 2001 and 2002 monthly snow surveys (i.e. April, May, and = June) were=20 undertaken to assess the spatial and temporal representativeness = of snow=20 water equivalent (SWE) values recorded at six snow telemetry = (SNOTEL)=20 stations in the Rio Grande headwaters. Snow depth data were = interpolated=20 using binary regression tree models and combined with snow density = data=20 and remotely sensed snow covered area to estimate the spatial = distribution=20 of SWE surrounding the SNOTEL sites. A physically based energy and = mass=20 balance snowmelt model was used to simulate the depletion of snow = cover=20 throughout the snowmelt season. Relative to the entire watershed, = SNOTEL=20 site locations are not representative of physiographic variables = known to=20 control snow distribution (i.e. elevation, slope, and incident = solar=20 radiation). At the watershed scale (3419 km$^{2}$) SNOTEL sites = are=20 located toward the western boundary of the watershed, an area of = high snow=20 cover persistence. Even relative to the 16, 4 and 1 km$^{2}$ areas = that=20 surround them, SNOTEL stations are not representative of the = physiographic=20 variables known to control snow distribution. These physiographic = biases=20 vary from site-to-site, with five of six sites located on = relatively flat=20 terrain and hence having a positive solar radiation bias. For the = two=20 water years studied, certain sites showed consistent overestimates = of SWE=20 relative to the surrounding 16, 4, and 1-km$^{2}$ areas. Other = sites=20 showed variability in SWE bias during the two years, as = regression-tree=20 model results suggested that different physiographic variables = controlled=20 snow distribution during the two water years. The results = presented here=20 will improve the ability to upscale SNOTEL data for evaluating and = calibrating remote sensing algorithms and initializing, = evaluating, and=20 updating modeling efforts at the regional scale.
DE: = 1800 HYDROLOGY
DE: 1836 = Hydrologic=20 budget (1655)
DE: 1863 Snow and ice=20 (1827)
DE: 1878 Water/energy=20 interactions
DE: 1884 Water = supply
SC:=20 Union [U]
MN: 2004 AGU = Fall=20 Meeting

New=20 Search

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