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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  17 Apr 2020

17 Apr 2020

Review status
A revised version of this preprint is currently under review for the journal HESS.

Snow Water Equivalents exclusively from Snow Heights and their temporal Changes: The ΔSNOW.MODEL

Michael Winkler1,, Harald Schellander1,2,, and Stefanie Gruber1 Michael Winkler et al.
  • 1ZAMG – Zentralanstalt für Meteorologie und Geodynamik, Innsbruck, Austria
  • 2Department of Atmospheric and Cryospheric Sciences, University of Innsbruck, Austria
  • These authors contributed equally to this work.

Abstract. Snow heights have been manually observed for many years, sometimes decades, at various places. These records are often of good quality. In addition, more and more data from automatic stations and remote sensing are available. On the other hand, records of snow water equivalent SWE – synonymous for snow load or mass – are sparse, although it might be the most important snowpack feature in fields like hydrology, climatology, agriculture, natural hazards research, etc. SWE very often has to be modeled, and those models either depend on meteorological forcing or are not intended to simulate individual SWE values, like the substantial seasonal peak SWE.

The ΔSNOW.MODEL is presented as a new method to simulate local-scale SWE. It solely needs snow heights as input, though a gapless record thereof. Temporal resolution of the data series is no restriction per se. The ΔSNOW.MODEL is a semi-empirical multi-layer model and freely available as R-package. Snow compaction is modeled following the rules of Newtonian viscosity. The model considers measurement errors, treats overburden loads due to fresh snow as additional unsteady compaction, and melted mass is stepwise distributed top-down in the snowpack.

Seven model parameters are subject to calibration, which was performed using 71 winters from 14 stations, well-distributed over different altitudes and climatic regions of the Alps. Another 73 rather independent winters act as validation data. Results are very promising: Median bias and root mean squared error for SWE are only −4.0 kg m−2 and 23.9 kg m−2, and +2.3 kg m−2 and 23.1 kg m−2 for peak SWE, respectively. This is a major advance compared to snow models relying on empirical regressions, but also much more sophisticated thermodynamic snow models not necessarily perform better.

Not least, this study outlines the need for comprehensive comparison studies on SWE measurement and modeling at the point and local scale.

Michael Winkler et al.

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Michael Winkler et al.

Michael Winkler et al.


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Latest update: 19 Sep 2020
Publications Copernicus
Short summary
A new method to calculate the mass of snow is provided. It is quite simple, but gives surprisingly precise results. The new approach only relies on snow height observations, and the authors are confident that it can be applied quite generally, at various places in different climates. The water mass, that is stored in the snow, can be attributed to all snow height records. This is especially interesting for studies on extremes (e.g. snow loads or flooding) and climate (e.g. precipitation trends).
A new method to calculate the mass of snow is provided. It is quite simple, but gives...