Journal cover Journal topic
The Cryosphere An interactive open-access journal of the European Geosciences Union
Journal topic

Journal metrics

Journal metrics

  • IF value: 4.790 IF 4.790
  • IF 5-year value: 5.921 IF 5-year
    5.921
  • CiteScore value: 5.27 CiteScore
    5.27
  • SNIP value: 1.551 SNIP 1.551
  • IPP value: 5.08 IPP 5.08
  • SJR value: 3.016 SJR 3.016
  • Scimago H <br class='hide-on-tablet hide-on-mobile'>index value: 63 Scimago H
    index 63
  • h5-index value: 51 h5-index 51
TC | Volume 13, issue 7
The Cryosphere, 13, 1767–1784, 2019
https://doi.org/10.5194/tc-13-1767-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
The Cryosphere, 13, 1767–1784, 2019
https://doi.org/10.5194/tc-13-1767-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 04 Jul 2019

Research article | 04 Jul 2019

Converting snow depth to snow water equivalent using climatological variables

David F. Hill et al.
Related authors  
What's streamflow got to do with it? A probabilistic simulation of the competing oceanographic and fluvial processes driving extreme along-river water levels
Katherine A. Serafin, Peter Ruggiero, Kai Parker, and David F. Hill
Nat. Hazards Earth Syst. Sci., 19, 1415–1431, https://doi.org/10.5194/nhess-19-1415-2019,https://doi.org/10.5194/nhess-19-1415-2019, 2019
Short summary
Related subject area  
Discipline: Snow | Subject: Seasonal Snow
Avalanches and micrometeorology driving mass and energy balance of the lowest perennial ice field of the Alps: a case study
Rebecca Mott, Andreas Wolf, Maximilian Kehl, Harald Kunstmann, Michael Warscher, and Thomas Grünewald
The Cryosphere, 13, 1247–1265, https://doi.org/10.5194/tc-13-1247-2019,https://doi.org/10.5194/tc-13-1247-2019, 2019
Short summary
The optical characteristics and sources of chromophoric dissolved organic matter (CDOM) in seasonal snow of northwestern China
Yue Zhou, Hui Wen, Jun Liu, Wei Pu, Qingcai Chen, and Xin Wang
The Cryosphere, 13, 157–175, https://doi.org/10.5194/tc-13-157-2019,https://doi.org/10.5194/tc-13-157-2019, 2019
Short summary
Brief Communication: Early season snowpack loss and implications for oversnow vehicle recreation travel planning
Benjamin J. Hatchett and Hilary G. Eisen
The Cryosphere, 13, 21–28, https://doi.org/10.5194/tc-13-21-2019,https://doi.org/10.5194/tc-13-21-2019, 2019
Short summary
Simulated single-layer forest canopies delay Northern Hemisphere snowmelt
Markus Todt, Nick Rutter, Christopher G. Fletcher, and Leanne M. Wake
The Cryosphere Discuss., https://doi.org/10.5194/tc-2018-270,https://doi.org/10.5194/tc-2018-270, 2019
Revised manuscript accepted for TC
Short summary
Multi-component ensembles of future meteorological and natural snow conditions for 1500 m altitude in the Chartreuse mountain range, Northern French Alps
Deborah Verfaillie, Matthieu Lafaysse, Michel Déqué, Nicolas Eckert, Yves Lejeune, and Samuel Morin
The Cryosphere, 12, 1249–1271, https://doi.org/10.5194/tc-12-1249-2018,https://doi.org/10.5194/tc-12-1249-2018, 2018
Short summary
Cited articles  
Alford, D.: Density variations in alpine snow, J. Glaciol., 6, 495–503, https://doi.org/10.3189/S0022143000019717, 1967. 
Avanzi, F., De Michele, C., and Ghezzi, A.: On the performances of empirical regressions for the estimation of bulk snow density, Geogr. Fis. Din. Quat., 38, 105–112, https://doi.org/10.4461/GFDQ.2015.38.10, 2015. 
Beaumont, R.: Mt. Hood pressure pillow snow gage, J. Appl. Meteorol., 4, 626–631, https://doi.org/10.1175/1520-0450(1965)004<0626:MHPPSG>2.0.CO;2, 1965. 
Beaumont, R. and Work, R.: Snow sampling results from three samplers, Hydrolog. Sci. J., 8, 74–78, https://doi.org/10.1080/02626666309493359, 1963. 
Burakowski, E. A., Wake, C. P., Stampone, M., and Dibb, J.: Putting the Capital “A” in CoCoRAHS: An Experimental Program to Measure Albedo using the Community Collaborative Rain Hail and Snow (CoCoRaHS) Network, Hydrol. Process., 27, 3024–3034, https://doi.org/10.1002/hyp.9825, 2013. 
Publications Copernicus
Download
Short summary
We present a new statistical model for converting snow depths to water equivalent. The only variables required are snow depth, day of year, and location. We use the location to look up climatological parameters such as mean winter precipitation and mean temperature difference (difference between hottest month and coldest month). The model is simple by design so that it can be applied to depth measurements anywhere, anytime. The model is shown to perform better than other widely used approaches.
We present a new statistical model for converting snow depths to water equivalent. The only...
Citation