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Proceedings of the International Association of Hydrological Sciences An open-access publication for refereed proceedings in hydrology
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Volume 371
Proc. IAHS, 371, 131–136, 2015
https://doi.org/10.5194/piahs-371-131-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
Proc. IAHS, 371, 131–136, 2015
https://doi.org/10.5194/piahs-371-131-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.

  12 Jun 2015

12 Jun 2015

Snowpack variability and trends at long-term stations in northern Colorado, USA

S. R. Fassnacht1,2,3 and M. Hultstrand1,4 S. R. Fassnacht and M. Hultstrand
  • 1ESS-Watershed Science, Colorado State University, Fort Collins, 80523-1476 Colorado, USA
  • 2Cooperative Institute for Research in the Atmosphere, Fort Collins, 80523-1375 Colorado, USA
  • 3Geospatial Centroid at Colorado State University, Fort Collins, 80523-1019 Colorado, USA
  • 4Natural Resources Conservation Service Snow Survey Office, Denver Federal Center Building 56, Denver, 80225-0462 Colorado, USA

Abstract. The individual measurements from snowcourse stations were digitized for six stations across northern Colorado that had up to 79 years of record (1936 to 2014). These manual measurements are collected at the first of the month from February through May, with additional measurements in January and June. This dataset was used to evaluate the variability in snow depth and snow water equivalent (SWE) across a snowcourse, as well as trends in snowpack patterns across the entire period of record and over two halves of the record (up to 1975 and from 1976).

Snowpack variability is correlated to depth and SWE. The snow depth variability is shown to be highly correlated with average April snow depth and day of year. Depth and SWE were found to be significantly decreasing over the entire period of record at two stations, while at another station the significant trends were an increase over the first half of the record and a decrease over the second half. Variability tended to decrease with time, when significant.

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Short summary
Snowpack properties vary over distance. Water resources managers use operational data to estimate streamflow, while scientists use snow data models to understand climate and hydrology. We suggest that there is the individual measurements in a snowcourse be used to address uncertainty. Further, over the long term trends may not be obvious but increasing and decreasing trends can exist over shorter time periods, as seen in Northern Colorado. Such trends mirror global temperature patterns.
Snowpack properties vary over distance. Water resources managers use operational data to...
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