Modelling of Snow Depth and Snow Density based on Capacitive Measurements using Machine Learning Methods.


  • Nils-Olav Skeie
  • Henrik Nikolai Vahl
  • Håkon Viumdal



snow density, snow water equivalent, capacitive sensor, model development, machine learning


In countries with cold winters, snowpack will affect the hydropower production during the melting periods. To optimize the hydropower production, it is relevant to consider information from the snowpack to estimate the water content when melting. Several techniques and devices can be used to measure the water content of the snowpack. This paper discusses a prototype based on capacitive measurements with a small footprint, and the development of data driven models to estimate the snow density, snow depth and snow water equivalent in a snowpack. The device was deployed in a snowy area throughout the winter with logging while manual reference measurements were made sporadically. Machine learning methods were used for developing the models, and several models were combined to estimate the water content of the snowpack. The developed model estimated the snow density, snow depth and snow water equivalent during the wintertime with good results. However, during the springtime, the capacitive measurements have some limitations.


J. M. Bjerke, M. N. Murillo Abril, A. Jaganjac and N. Pouladi, Measurement of snow density, “MSc. Project”, University of South-Eastern Norway (USN), Porsgrunn, 2019.

Ccin, Current Snow Cover, Snow Anomaly Tracking,, Canadian Cryospheric Information Network, University of Waterloo, 2021.

A. Denoth, A. Foglar, P. Weiland, C. Mätzler, H. Aebischer, M. Tiuri and A. Sihvola, A comparative study of instruments for measuring the liquid water content of snow, Journal of Applied Physics, 1984, DOI: 10.1063/1.334215.

A. Denoth and I. Wilhelmy, Snow dielectric devices and field applications, International Snow Science Workshop, 1988.

M. Hallikainen, F. Ulaby and M. Abdel-Razik, Measurements of the dielectric properties of snow in the 4-18 GHz frequency range, 12th European Conference on Microwave, 1984.

K. Muller, H. Toft Larsen, and G. Sojer, Snøomvandling (in Norwegian), Fact sheet, NVE, 2020

M. N. Murillo Abril, Development of a remote measurement node for snow density, “MSc. Thesis”, University of South-Eastern Norway (USN), Porsgrunn, 2020.

M. N. Murillo Abril, B. Furenes, N.-O. Skeie, Development of a model to estimate parameters in a snowpack based on capacitive measurements, SIMS conference, 2020.

M. Niang, M. Bernier, M. Stacheder, A. Brandelik, and E. van Bochove, Influence of snow temperature interpolation algorithm and dielectric mixing-model coefficient on density and liquid water content determination in a cold seasonal snowpack, Sensing and Imaging, 2006, DOI: 10.1007/s11220-006-0020-9.

H. B. Stranden, B. Lirhus Ree and K. M. Møen, Recommendations for automatic measurements of snow water equivalent in NVE, Norwegian Water Resources and Energy Directorate (NVE), 2015.

F. Techel and C. Pielmeier, Point observations of liquid water content in wet snow – investigating methodical, spatial and temporal aspects, The Cryosphere, 2011.

H. N. Vahl, Development of models for estimating snow depth and snow density using machine learning methods, “MSc. Thesis”, University of South-Eastern Norway (USN), Porsgrunn, 2021.

Wsl, Long-term snow water equivalent measurements,, WSL Institute for Snow and Avalanche Research SLF, 2021.