Snow Pattern Delineation Using Ground Observations, Remote Sensing, and Modeling
Abstract
Regardless of the precipitation received, snow depth patterns tend to repeat on landscapes year after year (Sturm and Wagner, 2010). For example, windswept ridges with sparse vegetation have shallow snow while water tracks and swales are deeper. If snow patterns can be consistently identified, understood, and classified using ground observations, remote sensing, models, or some combination thereof, an untapped potential exists to expand and improve snow assessments and predictions. Pattern detection, repeatability, and efficacy have been demonstrated for images and data from a nested study area located on Alaska's North Slope. As a part of the SnowNet project, well over 200,000 snow depths and hundreds of snow densities have been measured during spring measurement campaigns from 2010-2013. Most of the measurements were collected at the core 1km2 Imnavait Creek watershed (where snow measurements have occurred since the early 1980s), with sparser (but still high volume) data collected from the outer 6km2 and 21km2 areas. Imagery collected for the same areas include snow cover from Landsat (30 m) from 1982-present and fine-resolution commercial imagery (0.5-3 m) from 2002-present. While winter imagery is useful for delineating snow-free ridges and windswept areas, of more value were the 12 mid-melt images which allowed us to identify deeper snowpack areas. We also simulated snow distributions from 2010-2013 using SnowModel, which uses topography, land cover, and meteorological data to realistically simulate snow accumulation and ablation over our domains. The time series of over 200,000 individual observations, over 40 images, and four years of model simulations show striking repeatability in snow depth patterns and among years. The spatial agreements among ground observations, satellite-derived snow cover, and SnowModel are remarkable. Our results show a strong fidelity to patterns appearing in three different snow cover and depth estimate approaches, and suggest the minimum required data to delineate such patterns.
- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2013
- Bibcode:
- 2013AGUFM.C41B0610H
- Keywords:
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- 0736 CRYOSPHERE Snow;
- 0772 CRYOSPHERE Distribution;
- 0758 CRYOSPHERE Remote sensing;
- 0798 CRYOSPHERE Modeling