Estimating the spatial distribution of snow water equivalence in a montane watershed

✍ Scribed by Kelly Elder; Walter Rosenthal; Robert E. Davis

• Publisher: John Wiley and Sons
• Year: 1998
• Tongue: English
• Weight: 478 KB
• Volume: 12
• Category: Article
• ISSN: 0885-6087
• DOI: 10.1002/(sici)1099-1085(199808/09)12:10/11<1793::aid-hyp695>3.0.co;2-k

✦ Synopsis

An approach to model distributed snow water equivalence (SWE) that merges ®eld measurements of depth and density with remotely sensed snow-covered area (SCA) is described. In 1993, two teams conducted an intensive snow survey in the 92 . 8 km 2 Blackcap Basin of the Kings River. Snow depth was measured at 709 points and density in ®ve snow pits and along ®ve transects using a Federal Sampler. Sample locations were chosen to be representative of the range of elevation, slope and aspect of the basin. Regression tree models showed that net radiation, elevation and slope angle account for 60±70% of the variance in the depth measurements. Density was distributed over the basin on a 30 m grid with a multiple linear regression model that explained 70% of the observed variance as a function of the same three variables. The gridded depth estimates, combined with modelled density, produced spatially distributed estimates of SWE. An unsupervised spectral unmixing algorithm estimated snow cover fractions from Landsat-5 Thematic Mapper data acquired at the time as the snow survey. This method provides a snow cover fraction estimate for every pixel. This subpixel map was used as the best estimate for SCA and, combining it with the SWE map, allowed computation of the SWE volume. The estimated volume using the subpixel SCA map was compared with several SCA maps produced with simulations of binary SCA mapping techniques. Thresholds of 40, 50 and 60% fractional cover were used to map binary cases of full snow cover or no snow cover. The dierence in basin SWE volume was up to 13% depending on the threshold used to classify snow-covered versus snow-free areas. The percentage dierences in volumes show a signi®cant correlation to the percentage dierences in SCA between the methods.