Articles | Volume 25, issue 3
https://doi.org/10.5194/hess-25-1165-2021
https://doi.org/10.5194/hess-25-1165-2021
Research article
 | 
05 Mar 2021
Research article |  | 05 Mar 2021

Snow water equivalents exclusively from snow depths and their temporal changes: the Δsnow model

Michael Winkler, Harald Schellander, and Stefanie Gruber

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Cited articles

Akaike, H.: A new look at the statistical model identification, IEEE T. Automat. Control, 19, 716–723, https://doi.org/10.1109/TAC.1974.1100705, 1974. a
Armstrong, R. L. and Brun, E.: Snow and climate: physical processes, surface energy exchange and modeling, Cambridge University Press, Cambridge, 2010. a
Austrian Standards Institute: ÖNORM B 1991-1-3:2018-12-01, Vienna, Austria, 2018. a, b, c
Avanzi, F., De Michele, C., and Ghezzi, A.: On the performances of empirical regressions for the estimation of bulk snow density, Geografia Fisica e Dinamica Quaternaria, 38, 105–112, https://doi.org/10.4461/GFDQ.2015.38.10, 2015. a, b, c, d
Blanchet, J. and Davison, A.: Spatial modeling of extreme snow depth, Ann. Appl. Stat., 5, 1699–1725, https://doi.org/10.1214/11-AOAS464SUPP, 2011. a
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Short summary
A new method to calculate the mass of snow is provided. It is quite simple but gives surprisingly good results. The new approach only requires regular snow depth observations to simulate respective water mass that is stored in the snow. It is called ΔSNOW model, its code is freely available, and it can be applied in various climates. The method is especially interesting for studies on extremes (e.g., snow loads or flooding) and climate (e.g., precipitation trends).