Reconstructing snow water equivalent in the Rio Grande headwaters using remotely sensed snow cover data and a spatially distributed snowmelt model

✍ Scribed by Noah P. Molotch

• Publisher: John Wiley and Sons
• Year: 2009
• Tongue: English
• Weight: 1019 KB
• Volume: 23
• Category: Article
• ISSN: 0885-6087
• DOI: 10.1002/hyp.7206

✦ Synopsis

Abstract

Snow covered area (SCA) observations from the Landsat Enhanced Thematic Mapper (ETM+) were used in combination with a distributed snowmelt model to estimate snow water equivalent (SWE) in the headwaters of the Rio Grande basin (3,419 km^2^) ‐ 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 study. Basin‐wide mean SWE was 2·6 times greater in April 2001 versus 2002. Despite these climatological differences, the model adequately recovered SWE at intensive study areas (ISAs); mean absolute SWE error was 23% relative to observed SWE. Reconstruction model SWE errors were within one standard deviation of the mean observed SWE over 37 and 55% of the four 16‐km^2^ intensive field campaign study sites in 2001 and 2002, respectively; a result comparable to previous works at much smaller scales. A key strength of the technique is that spatially distributed SWE estimates are not dependent upon ground‐based observations of SWE. Moreover, the model was relatively insensitive to the location of forcing observations relative to commonly used statistical SWE interpolation models. Hence, the reconstruction technique is a viable approach for obtaining high‐resolution SWE estimates at larger scales (e.g. >1000 km^2^) and in locations where detailed hydrometeorological observations are scarce. Copyright © 2009 John Wiley & Sons, Ltd.