The processes controlling snowpack mass balance are highly variable in time and space, requiring remote sensing to observe regional processes and intensive field observations to observe hilislope-scale phenomena. This research aims to further understanding of the processes controlling snowpack mass balance through innovative applications of remotely sensed data and statistical interpolations of ground observations. Four advancements were obtained: 1) the sensitivity of regression tree snow distribution models to digital elevation data and independent variables was quanitified; 2) improved ability to upscale point snow water equivalent (SWE) measurements at snow telemetry (SNOTEL) stations was obtained by quantifying the small-scale SWE variability surrounding these stations; 3) spatially distributed snowmelt algorithms were improved by incorporating remotely sensed snow-surface albedo data into snowmelt modeling; and (4) the temporal and spatial continuity of regional-scale estimates of snow covered area (SCA) and SWE were improved by combining remotely sensed data and air temperature data to extend estimates beneath the cloud cover.
| Identifer | oai:union.ndltd.org:arizona.edu/oai:arizona.openrepository.com:10150/191275 |
| Date | January 2004 |
| Creators | Molotch, Noah P. |
| Contributors | Bales, Roger, Bales, Roger C., Davis, Robert E., Nijssen, Bart, Guertin, Phillip, Shuttleworth, William J. |
| Publisher | The University of Arizona. |
| Source Sets | University of Arizona |
| Language | English |
| Detected Language | English |
| Type | Dissertation-Reproduction (electronic), text |
| Rights | Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author. |
Page generated in 0.0072 seconds