Sensitivity of modeled snow stability data to meteorological input uncertainty

Abstract

Abstract. To perform spatial snow cover simulations for numerical avalanche forecasting, interpolation and downscaling of meteorological data are required, which introduce uncertainties. The repercussions of these uncertainties on modeled snow stability remain mostly unknown. We therefore assessed the contribution of meteorological input uncertainty to modeled snow stability by performing a global sensitivity analysis. We used the numerical snow cover model SNOWPACK to simulate two snow instability metrics, i.e., the skier stability index and the critical crack length, for a field site equipped with an automatic weather station providing the necessary input for the model. Simulations were performed for a winter season, which was marked by a prolonged dry period at the beginning of the season. During this period, the snow surface layers transformed into layers of faceted and depth hoar crystals, which were subsequently buried by snow. The early-season snow surface was likely the weak layer of many avalanches later in the season. Three different scenarios were investigated to better assess the influence of meteorological forcing on snow stability during (a) the weak layer formation period, (b) the slab formation period, and (c) the weak layer and slab formation period. For each scenario, 14 000 simulations were performed, by introducing quasi-random uncertainties to the meteorological input. Uncertainty ranges for meteorological forcing covered typical differences observed within a distance of 2 km or an elevation change of 200 m. Results showed that a weak layer formed in 99.7 % of the simulations, indicating that the weak layer formation was very robust due to the prolonged dry period. For scenario a, modeled grain size of the weak layer was mainly sensitive to precipitation, while the shear strength of the weak layer was sensitive to most input variables, especially air temperature. Once the weak layer existed (case b), precipitation was the most prominent driver for snow stability. The sensitivity analysis highlighted that for all scenarios, the two stability metrics were mostly sensitive to precipitation. Precipitation determined the load of the slab, which in turn influenced weak layer properties. For cases b and c, the two stability metrics showed contradicting behaviors. With increasing precipitation, i.e., deep snowpacks, the skier stability index decreased (became less stable). In contrast, the critical crack length increased with increasing precipitation (became more stable). With regard to spatial simulations of snow stability, the high sensitivity to precipitation suggests that accurate precipitation patterns are necessary to obtain realistic snow stability patterns.

Using snow depth observations to provide insight into the quality of snowpack simulations for regional-scale avalanche forecasting

“…Given precipitation has been shown to be a primary source of uncertainty in snowpack simulations (Raleigh et al, 2015;Richter et al, 2020), meaningful methods to identify and correct erroneous precipitation inputs could dramatically improve the quality of snowpack models. However, this study highlights large gaps and uncertainties in many observation networks that warrant careful approaches when evaluating snowpack models, especially at regional scales.…”

“…While snowpack models are sensitive to all weather input variables, precipitation is consistently identified as the main driver of uncertainty in the simulated stratigraphy (Raleigh et al, 2015;Richter et al, 2020). Observations of winter precipitation are available from different types of measurements including cumulative precipitation from rain gauges, snow water equivalent from snow pillows, and snow depth from acoustic sensors or manual probing (Wang et al, 2017).…”

A closed-form model for layered snow slabs

“…Their failure can lead to uncritical failure (whumpf sounds, shooting cracks) or avalanche release. The layering of snow covers is an essential part of avalanche forecasting (Richter et al, 2020) and for in-terrain decision making (Schweizer and Jamieson, 2007). It is known that the layering directly affects crack arrest or crack propagation .…”

Examining the operational use of avalanche problems with decision trees and model-generated weather and snowpack variables

“…Physical models offer additional data to supplement field observations, most notably weather data from numerical weather prediction models and snowpack data from snow cover models. Weather models have been shown to add value to avalanche forecasts (Roeger et al, 2001;Schirmer and Jamieson, 2015), especially because of their capability to make predictions about future conditions. Snowpack models such as SNOWPACK and Crocus can provide detailed simulations of snowpack structure but have had limited adoption into operational forecasting.…”