Reconstructing snow water equivalent in the Rio Grande headwaters using remotely sensed snow cover data and a spatially distributed snowmelt model
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. .
Session 2, Hydrometeorological Remote Sensing
Tuesday, 16 January 2007, 8:30 AM-5:00 PM, 211
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