The RHOSSA campaign: multi-resolution monitoring of the seasonal evolution of the structure and mechanical stability of an alpine snowpack

Abstract

Abstract. The necessity of characterizing snow through objective, physically motivated parameters has led to new model formulations and new measurement techniques. Consequently, essential structural parameters such as density and specific surface area (for basic characterization) or mechanical parameters such as the critical crack length (for avalanche stability characterization) gradually replace the semiempirical indices acquired from traditional stratigraphy. These advances come along with new demands and potentials for validation. To this end, we conducted the RHOSSA field campaign, in reference to density (ρ) and specific surface area (SSA), at the Weissfluhjoch research site in the Swiss Alps to provide a multi-instrument, multi-resolution dataset of density, SSA and critical crack length over the complete winter season of 2015–2016. In this paper, we present the design of the campaign and a basic analysis of the measurements alongside predictions from the model SNOWPACK. To bridge between traditional and new methods, the campaign comprises traditional profiles, density cutter, IceCube, SnowMicroPen (SMP), micro-computed-tomography, propagation saw tests and compression tests. To bridge between different temporal resolutions, the traditional weekly to biweekly (every 2 weeks, used in this sense throughout the paper) snow pits were complemented by daily SMP measurements. From the latter, we derived a recalibration of the statistical retrieval of density and SSA for SMP version 4 that yields an unprecedented spatiotemporal picture of the seasonal evolution of density and SSA in a snowpack. Finally, we provide an intercomparison of measured and modeled estimates of density and SSA for four characteristic layers over the entire season to demonstrate the potential of high-temporal-resolution monitoring for snowpack model validation.

On the performance of the snow model Crocus driven by in situ and reanalysis data at Villum Research Station in northeast Greenland

“…We evaluated the model performance by comparing simulated and measured snow depth, snow density and SSA ( Vertical profiles of snow density and SSA were retrieved from a SnowMicroPen (SMP), a snow penetrometer measuring the bonding force between snow grains with a vertical resolution of 1.25 mm (Schneebeli et al, 1999). While the density retrieved from SMP measurements was calculated following Calonne et al (2020), Proksch et al (2015) and King et al (2020), SSA was obtained based on Calonne et al (2020) and Proksch et al (2015), as King et al (2020) In addition, on 03 April 2018, snow depth was measured with a ruler, and every 10 cm of the profile, snow density with a density cutter (60x30x56 mm) and SSA with an IceCube3. The optical system IceCube measures the hemispherical infrared reflectance of snow and converts the reflectance in SSA (Zuanon, 2013).…”

“…Proksch et al (2015) used data from the European Alps and the Arctic, while measurements for the parametrisation after Calonne et al (2020) were from the Swiss Alps and therefore for warm snow. However, Calonne et al (2020) developed the parametrisation for the SMP Version 4 we used, while the parametrisation after Proksch et al (2015) was developed for SMP Version 2 (Calonne et al, 2020). King et al (2020) found large discrepancies between measured density with a density cutter and SMP after Proksch et al (2015) for snow on Arctic sea ice.…”

“…King et al (2020) found large discrepancies between measured density with a density cutter and SMP after Proksch et al (2015) for snow on Arctic sea ice. Parametrisation for SSA after Proksch et al (2015) was developed using the precise MicroCT while Calonne et al (2020) used measurements from the IceCube. We found that densities for Calonne et al (2020) and King et al (2020), whose parametrisation is for Arctic snow on sea ice with the SMP Version 4, differ only slightly.…”

“…However, snow plays a crucial role in the Arctic, as it covers the ground most of the year as well as the sea-ice during the cold season. Snow depth, snow cover duration as well as snow properties such as density, specific surface area (SSA: Iceair interface surface area divided by snow mass used instead of grain size , (Calonne et al, 2020)), thermal conductivity and albedo have climatic, ecology and socioeconomic impacts (Callaghan et al, 2011;Sturm et al, 1997;Box et al, 2012;Hall, https://doi.org/10.5194/tc-2021-100 Preprint. Discussion started: 7 April 2021 c Author(s) 2021.…”

Deep ice layer formation in an alpine snowpack: monitoring and modeling

“…Three water transport schemes implemented in SNOW-PACK were evaluated. First, the bucket approach (BA) is a common method used in snow-cover models (e.g., Bartelt and Lehning, 2002;Vionnet et al, 2012), which assumes that water is transported to the next downward layer when the liquid water content exceeds the water holding capacity of a given layer (depending on the ice volumetric content of snow; Coléou and Lesaffre, 1998). Second, the Richards equation (RE) was implemented in SNOWPACK by Wever et al (2014) to account for capillary effects.…”