Details

Autor(en) / Beteiligte

• Kaur, Sukhdeep
• Satyawali, PK

Titel

Estimation of snow density from SnowMicroPen measurements

Ist Teil von

• Cold regions science and technology, 2017-02, Vol.134, p.1-10

Ort / Verlag

Elsevier B.V

Erscheinungsjahr

2017

Link zum Volltext

Quelle

Elsevier ScienceDirect Journals Complete

Beschreibungen/Notizen

• Snow density information is essentially required in snowpack research. We estimated snow density from penetration resistance measured with the SnowMicroPen (SMP). A 100cm3 density cutter was used for manual measurements of snow density. The measurements were taken at Patsio (3800m) station of SASE located in the Greater Himalayan range. The maximum penetration resistance values obtained for 1mm depth windows of SMP profiles were used for snow density modelling. A statistical density model is then proposed from these data which correlated well with the manual measurements (rp: 0.83). The model was tested for two other stations (Dhundi and Gulmarg) located in different climatic zones. A correlation of 82% for Dhundi and 83% for Gulmarg was found. The model was then compared with existing SMP based density models. The present model estimated densities fairly well for various snow types as compared to other SMP based models except for fresh snow. A new parameter, the number of peaks of maximum penetration resistance for 1mm depth (Np) has been introduced. The parameter is found to be uncorrelated with other statistical parameters presented so far in snow characterization. •Snow density is estimated from maximum penetration resistance of SnowMicroPen (SMP).•Maximum penetration resistance of SMP corresponds to rupturing of snow microstructural elements.•Density model is tested for three stations located in the Western Himalayan range viz. Patsio, Dhundi and Gulmarg.•The model fairly estimates density for snow types: DF, MF, FC and DH but overestimates for PP snow.•A new parameter, number of peaks of maximum penetration resistance for 1mm depth (Np) has been introduced.•Np could be significant; as found uncorrelated with the statistical parameters used so far in snow classification.