Spatiotemporal Characteristics of Snowpack Density in the Mountainous Regions of the Western United States

Naoki Mizukami Department of Civil and Environmental Engineering, University of Utah, Salt Lake City, Utah

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Sanja Perica Department of Civil and Environmental Engineering, University of Utah, Salt Lake City, Utah

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Abstract

Snow density is calculated as a ratio of snow water equivalent to snow depth. Until the late 1990s, there were no continuous simultaneous measurements of snow water equivalent and snow depth covering large areas. Because of that, spatiotemporal characteristics of snowpack density could not be well described. Since then, the Natural Resources Conservation Service (NRCS) has been collecting both types of data daily throughout the winter season at snowpack telemetry (SNOTEL) sites located in the mountainous areas of the western United States. This new dataset provided an opportunity to examine the spatiotemporal characteristics of snowpack density.

The analysis of approximately seven years of data showed that at a given location and throughout the winter season, year-to-year snowpack density changes are significantly smaller than corresponding snow depth and snow water equivalent changes. As a result, reliable climatological estimates of snow density could be obtained from relatively short records. Snow density magnitudes and densification rates (i.e., rates at which snow densities change in time) were found to be location dependent. During early and midwinter, the densification rate is correlated with density. Starting in early or mid-March, however, snowpack density increases by approximately 2.0 kg m−3 day−1 regardless of location. Cluster analysis was used to obtain qualitative information on spatial patterns of snowpack density and densification rates. Four clusters were identified, each with a distinct density magnitude and densification rate. The most significant physiographic factor that discriminates between clusters was proximity to a large water body. Within individual mountain ranges, snowpack density characteristics were primarily dependent on elevation.

Corresponding author address: Naoki Mizukami, Office of Hydrologic Development, National Weather Service, 1325 East–West Highway, Silver Spring, MD 20910. Email: mizukami@eng.utah.edu

Abstract

Snow density is calculated as a ratio of snow water equivalent to snow depth. Until the late 1990s, there were no continuous simultaneous measurements of snow water equivalent and snow depth covering large areas. Because of that, spatiotemporal characteristics of snowpack density could not be well described. Since then, the Natural Resources Conservation Service (NRCS) has been collecting both types of data daily throughout the winter season at snowpack telemetry (SNOTEL) sites located in the mountainous areas of the western United States. This new dataset provided an opportunity to examine the spatiotemporal characteristics of snowpack density.

The analysis of approximately seven years of data showed that at a given location and throughout the winter season, year-to-year snowpack density changes are significantly smaller than corresponding snow depth and snow water equivalent changes. As a result, reliable climatological estimates of snow density could be obtained from relatively short records. Snow density magnitudes and densification rates (i.e., rates at which snow densities change in time) were found to be location dependent. During early and midwinter, the densification rate is correlated with density. Starting in early or mid-March, however, snowpack density increases by approximately 2.0 kg m−3 day−1 regardless of location. Cluster analysis was used to obtain qualitative information on spatial patterns of snowpack density and densification rates. Four clusters were identified, each with a distinct density magnitude and densification rate. The most significant physiographic factor that discriminates between clusters was proximity to a large water body. Within individual mountain ranges, snowpack density characteristics were primarily dependent on elevation.

Corresponding author address: Naoki Mizukami, Office of Hydrologic Development, National Weather Service, 1325 East–West Highway, Silver Spring, MD 20910. Email: mizukami@eng.utah.edu

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  • Akitaya, E., 1974: Studies on Depth Hoar. Series A, Vol. 26, Institute of Low Temperature Science, Hokkaido University, 1–67.

  • Anderson, E. A., 1973: National Weather Service River Forecast System—Snow Accumulation and Ablation Model. NOAA Tech. Memo. NWS HYDRO-17, 217 pp.

  • Anderson, E. A., 1976: A point energy and mass balance model of a snow cover. NOAA Tech. Rep. NWS 19, 150 pp.

  • Armstrong, R. L., 1980: An analysis of compressive strain in adjacent temperature-gradient and equi-temperature layers in a natural snow cover. J. Glaciol., 26 , 283289.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Balk, B., and Elder K. , 2000: Combining binary decision tree and geostatistical methods to estimate snow water equivalence distribution in a mountain watershed. Water Resour. Res., 36 , 1326.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Brown, R. D., 2000: Northern Hemisphere snow cover variability and change, 1915–97. J. Climate, 13 , 23392355.

  • Brown, R. D., Brasnett B. , and Robinson D. , 2003: Gridded North American monthly snow depth and snow water equivalent for GCM evaluation. Atmos.–Ocean, 41 , 114.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cayan, D. R., 1996: Interannual climate variability and snowpack in the western United States. J. Climate, 9 , 928948.

  • Colbeck, S. C., 1982: An overview of seasonal snow metamorphism. Rev. Geophys. Space Phys., 20 , 4561.

  • Colbeck, S. C., 1997: A review of sintering in seasonal snow. CRREL Rep. 97-10, Cold Regions Research and Engineering Laboratory, 17 pp.

  • Erickson, T. A., Williams M. W. , and Winsral A. , 2005: Persistence of topographic controls on the spatial distribution of snow in rugged mountain terrain, Colorado, United States. Water Resour. Res., 41 .W04014, doi:10.1029/2003WR002973.

    • Search Google Scholar
    • Export Citation
  • Erxleben, J., Elder K. , and Davis R. , 2002: Comparison of spatial interpolation methods for estimating snow distribution in the Colorado Rocky Mountains. Hydrol. Processes, 168 , 36273649.

    • Search Google Scholar
    • Export Citation
  • Everitt, B. S., Landau S. , and Leese M. , 2001: Cluster Analysis. 4th ed. Edward Arnold Publishers, 229 pp.

  • Fassnacht, S. R., Dressler K. A. , and Bales R. C. , 2003: Snow water equivalent interpolation for the Colorado River Basin from snow telemetry (SNOTEL) data. Water Resour. Res., 39 .1208, doi:10.1029/2002WR001512.

    • Search Google Scholar
    • Export Citation
  • Flanner, M. G., and Zender C. S. , 2006: Linking snowpack microphysics and albedo evolution. J. Geophys. Res., 111 .D12208, doi:1029/2005JD006834.

    • Search Google Scholar
    • Export Citation
  • Fovell, R. G., 1997: Consensus clustering of U.S. temperature and precipitation data. J. Climate, 10 , 14051427.

  • Fovell, R. G., and Fovell M. Y. , 1993: Climate zones of the conterminous United States defined using cluster analysis. J. Climate, 6 , 21032135.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Garen, D. C., and Marks D. , 2005: Spatially distributed energy balance snowmelt modeling in a mountainous river basin: Estimation of meteorological inputs and verification of model results. J. Hydrol., 315 , 126153.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Grant, L. O., and Rhea J. O. , 1974: Elevation and meteorological controls on the density of snow. Proc. An Interdisciplinary Symp. onAdvanced Concepts and Techniques in the Study of Snow and Ice Resource, Monterey, CA, National Academy of Science, 169–181.

  • Hall, D. K., Chang A. T. C. , and Foster J. L. , 1986: Detection of the depth hoar layer in the snowpack of the arctic coastal plain of Alaska using satellite data. J. Glaciol., 32 , 8794.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jordan, R., 1991: A one-dimensional temperature model for a snow cover: Technical documentation for SNTHERM.89, CRREL Special Rep. 91-16. Cold Regions Research and Engineering Laboratory, 64 pp.

  • Josberger, E. G., Gloersen P. , Chang A. T. C. , and Rango A. , 1996: The effects of snowpack grain size on satellite passive microwave observations from the Upper Colorado River Basin. J. Geophys. Res., 101 , C3. 66796688.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Judson, A., and Doesken N. , 2000: Density of fresh fallen snow in the central Rocky Mountains. Bull. Amer. Meteor. Soc., 81 , 15771587.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kaempfer, T. U., and Schneebeli M. , 2007: Observation of isothermal metamorphism of new snow and interpretation as a sintering processes. J. Geophys. Res., 112 .D24101, doi:10.1029/2007JD009047.

    • Search Google Scholar
    • Export Citation
  • Kalkstein, S. L., Tan G. , and Skindlov J. A. , 1987: An evaluation of three clustering procedures for use in synoptic climatological classification. J. Climate Appl. Meteor., 26 , 717730.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kelly, R. J., Chang A. T. C. , Tsang L. , and Foster J. L. , 2003: A prototype AMSR-E global snow area and snow depth algorithm. IEEE Trans. Geosci. Remote Sens., 41 , 230242.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kojima, K., 1967: Densification of seasonal snow cover. Physics of Snow and Ice: Proc. Int. Conf. on Low Temperature Science, Sapporo, Japan, Institute of Low Temperature Science, Hokkaido University, 929–952.

  • LaChapelle, E. R., 1962: The density distribution of new snow. Progress Rep. 2, USDA Forest Service, Wasatch National Forest, Alta Avalanche Study Center, Project F, 13 pp.

  • Langham, E. J., 1981: Physics and properties of snowcover. Handbook of Snow: Principles, Processes, Management and Use, D. M. Gray and D. H. Male, Eds., Blackburn Press, 275–337.

    • Search Google Scholar
    • Export Citation
  • MacQueen, J., 1967: Some methods for classification and analysis of multivariate observations. Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, Vol. 1, L. M. Le Cam and J. Neyman, University of California Press, 281–297.

  • Maeno, N., and Ebinuma T. , 1983: Pressure sintering of ice and its implication to the densification of snow at polar glaciers and ice sheets. J. Phys. Chem., 87 , 41034110.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Massom, R. A., and Coauthors, 2001: Snow on Antarctic sea ice. Rev. Geophys., 39 , 413445.

  • McClung, D., and Schaerer P. , 1993: The Avalanche Handbook. Mountaineers, 271 pp.

  • McGurk, B., Azuma D. , and Kattelmann R. , 1988: Density of new snow in the central Sierra Nevada. Proc. 56th Western Snow Conf., Kalispell, MT, Western Snow Conference, 158–161.

  • Mote, L. T., Grundstein A. J. , Leathers D. J. , and Robinson D. A. , 2003: A comparison of modeled, remotely sensed, and measured snow water equivalent in the northern Great Plains. Water Resour. Res., 39 .1209, doi:10.1029/2002WR001782.

    • Search Google Scholar
    • Export Citation
  • Mote, P. W., Hamlet A. F. , Clark M. P. , and Lettenmaier D. P. , 2005: Declining mountain snowpack in western North America. Bull. Amer. Meteor. Soc., 86 , 3949.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schaefer, G. L., and Johnson D. E. , 1992: Development and operation of the SNOTEL system in the western United States. Proc. United States/People’s Republic of China Flood Forecasting Symp., Portland, Oregon, Office of Hydrology, National Weather Service, 29–48.

  • Serreze, M. C., Clark M. P. , Armstrong R. L. , McGinnis D. A. , and Pulwarty R. S. , 1999: Characteristics of the western United States snowpack telemetry (SNOTEL) data. Water Resour. Res., 35 , 21452160.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Thyer, M., Beckers J. , Spittlehouse D. , Alila Y. , and Winkler R. , 2004: Diagnosing a distributed hydrologic model for two high-elevation forested catchments based on detailed stand- and basin-scale data. Water Resour. Res., 40 .W01103, doi:10.1029/2003WR002414.

    • Search Google Scholar
    • Export Citation
  • Xue, Y., Sun S. , Kahan D. S. , and Jiao Y. , 2003: Impact of parameterizations in snow physics and interface processes on the simulation of snow cover and runoff at several cold region sites. J. Geophys. Res., 108 .8859, doi:10.1029/2002JD003174.

    • Search Google Scholar
    • Export Citation
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