Cold-to-warm flow regime transition in snow avalanches

Abstract

Abstract. Large avalanches usually encounter different snow conditions along their track. When they release as slab avalanches comprising cold snow, they can subsequently develop into powder snow avalanches entraining snow as they move down the mountain. Typically, this entrained snow will be cold (T < −1 °C) at high elevations near the surface, but warm (T > −1 °C) at lower elevations or deeper in the snow pack. The intake of thermal energy in the form of warm snow is believed to cause a flow regime transition. Measurements of flow regime transitions are performed at the Vallée de la Sionne avalanche test site in Switzerland using two different radar systems. The data are then combined with snow temperatures calculated with the snow cover model SNOWPACK. We define transitions as complete, when the deposit at runout is characterized only by warm snow, or as partial, if there is a warm flow regime but the furthest deposit is characterized by cold snow. We introduce a transition factor Ft, based on the runout of cold and warm flow regimes, as a measure to quantify the transition type. Finally, we parameterize the snow cover temperature along the avalanche track by the altitude Hs, which represents the point where the average temperature of the uppermost 0.5 m changes from cold to warm. We find that Ft is related to the snow cover properties, i.e. approximately proportional to Hs. Thus, the flow regime in the runout area and the type of transition can be predicted by knowing the snow cover temperature distribution. We find, that, if Hs is more than 500 m above the valley floor for the path geometry of Vallée de la Sionne, entrainment of warm surface snow leads to a complete flow regime transition and the runout area is reached by only warm flow regimes. Such knowledge is of great importance since the impact pressure and the effectiveness of protection measures are greatly dependent on the flow regime.

Decoupling the Role of Inertia, Friction, and Cohesion in Dense Granular Avalanche Pressure Build‐up on Obstacles

“…For low velocities up to 10 m/s, the scatter of the simulated data is in the same range as the scatter of the measured data. The large scatter of the measurements for velocities higher than 10 m/s is very likely to be caused by the variability of the snow properties in the avalanches (Köhler et al, ). The scatter in the simulations is smaller because many snow properties are kept constant (Table ).…”

“…This is, however, a very coarse distinction as Köhler et al () have recently shown that even for the same topography, the flow regimes can be manifold. It is assumed that snow properties, which are mechanically and thermodynamically very sensitive, are a main contributor to this diversity of flow behaviors as well as an important influence on avalanche‐obstacle interaction behavior, which is not yet fully understood (Köhler et al, ; Steinkogler et al, ). The problem of snow avalanche impact pressure has been investigated for some decades in field experiments (Gauer & Kristensen, ; Sovilla et al, ; Thibert et al, ) and small‐scale chute experiments (Hauksson et al, ; Moriguchi et al, ; Salm, ).…”

Three-dimensional and real-scale modeling of flow regimes in dense snow avalanches

“…The avalanche in case I collapses as a dry cohesionless granular flow, which is more consistent with the point release in comparison to the fracture line. In reality, the plug flow simulated in case IV generally does not start from the release zone and normally forms at a later stage of an avalanche when it reaches the lower and warmer part of the mountain (Köhler et al 2018a). It is worth noting that this study aims at examining the distinct flow features with a wide variety of snow types/properties, while other conditions (i.e., release size, release position, terrain) are fixed to be exactly identical.…”

Inishell 2.0: semantically driven automatic GUI generation for scientific models

“…A small fraction of the end users could be described as "power users" that are able to autonomously run the most complex simulation setups (including fully autonomous model toolchains for operational applications; Sato et al, 2004;Côté et al, 2014;Bair et al, 2020), implement their own ideas in the model, and support other users they work with. The bulk of the user base is made of researchers that want to use these models to expand their research field (Rasmus et al, 2016;Haberkorn et al, 2017;Grünewald et al, 2018;Köhler et al, 2018) and that possess varying degrees of computer fluency. Finally, there are practitioners who mostly do not run the models themselves but rely on outputs from model toolchains set up by somebody else (Morin et al, 2020).…”