SNOTEL Representativeness in the Rio Grande Headwaters on the Basis of Physiographics and Remotely Sensed Snow Cover Persistence
|Title||SNOTEL Representativeness in the Rio Grande Headwaters on the Basis of Physiographics and Remotely Sensed Snow Cover Persistence|
|Year of Publication||2006|
|Authors||Molotch, N, Bales, R|
|Publisher||John Wiley & Sons, Ltd.|
|Keywords||binary regression tree, observation network design, SNOTEL, snow cover, snow water equivalent|
In this study we identify the physiographic and snowpack conditions currently represented by snowpack telemetry (SNOTEL) stations in the Rio Grande headwaters. Based on 8 years of advanced very high-resolution radiometer data (1995–2002) a snow cover persistence index was derived. Snow cover persistence values at the seven SNOTEL sites ranged from 3·9 to 4·75, with an average 14% greater than the mean persistence of the watershed. Using elevation, western barrier distance, and vegetation density, a 32-node binary classification tree model explained 75% of the variability in average snow cover persistence. Terrain classes encompassing the Lily Pond, Middle Creek, and Slumgullion SNOTEL sites represented 4·1%, 6·4%, and 4·0% of the watershed area respectively. SNOTEL stations do not exist in the spatially extensive (e.g. 11% of the watershed) terrain classes located in the upper elevations above the timberline. The results and techniques presented here will be useful for spatially distributed hydrologic analyses, in that we have identified the physiographic conditions currently represented by SNOTEL stations (i.e. the snowpack regimes at which snow water equivalent estimation uncertainty can be determined). Further, we outlined a statistically unbiased approach for designing future observation networks tailored for spatially distributed applications. Copyright © 2006 John Wiley & Sons, Ltd.