87th AMS Annual Meeting

Tuesday, 16 January 2007: 4:30 PM
Reconstructing snow water equivalent in the Rio Grande headwaters using remotely sensed snow cover data and a spatially distributed snowmelt model
211 (Henry B. Gonzalez Convention Center)
Noah P. Molotch, Univ. of California, Los Angeles, CA
Time series of fractional snow covered area (SCA) estimates from Landsat Enhanced Thematic Mapper (ETM+) data were combined with a spatially distributed snowmelt model to reconstruct snow water equivalent (SWE) in the Rio Grande headwaters (3,419 km2) a spatial scale that is an order of magnitude greater than previous reconstruction model applications. In this reconstruction approach, modeled snowmelt over each pixel is integrated over the time of ETM+ observed snow cover to estimate SWE. Considerable differences in the magnitude of SWE were simulated during the two-snowmelt season study; basin-wide mean SWE was 2.6 times greater in April 2001 versus 2002. During the draught year of 2002, spatial variability in maximum SWE was twice that of 2001. Basin-wide mean SWE was highly sensitive to decreases in SCA in 2002; shallow snow persisted over large portions of the watershed. Despite the climatological differences in 2001 versus 2002, model performance was robust with a mean absolute SWE error of 23% relative to observed SWE from field campaigns. Reconstruction model SWE errors were within one standard deviation of the mean observed SWE over 37 and 55% of the four 16-km2 intensive field campaign study sites in 2001 and 2002, respectively; a result comparable to previous works performed at much smaller scales. This illustrates the utility of the technique for obtaining high-resolution SWE estimates at larger scales (e.g. > 1000 km2) and in locations where detailed hydrometeorological observations are scarce.

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