Application of physical snowpack models in support of operational avalanche hazard forecasting: A status report on current implementations and prospects for the future

Morin, Samuel; Horton, Simon; Techel, Frank; Bavay, Mathias; Coléou, Cécile; Fierz, Charles; Gobiet, Andreas; Hagenmuller, Pascal; Lafaysse, Matthieu; Ližar, Matjaž; Mitterer, Christoph; Monti, Fabiano; Müller, Karsten; Olefs, Marc; Snook, John; Herwijnen, Alec van; Vionnet, Vincent

doi:10.1016/j.coldregions.2019.102910

Cited by 83 publications

(89 citation statements)

References 81 publications

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“…In complex terrain, wind-induced processes strongly influence snow distribution (Mott and Lehning, 2010). The bias introduced for P agrees with the high variations in snow depths, measured at very small scales (Bühler et al, 2015).…”

Section: Discussionsupporting

confidence: 80%

“…This is not an easy task, as snow distribution is very complex (Grünewald et al, 2010;Kirchner et al, 2014;Reuter et al, 2016). Since the mountain snow cover is largely shaped by snow transport by wind, adequate modeling can only be achieved through computationally expensive snow drift modeling (Gerber et al, 2018;Mott and Lehning, 2010;Vionnet et al, 2014). While from an operational point of view, highresolution modeling (resolution of several meters) on large domains is presently out of reach, alternative approaches were suggested (e.g., Vögeli et al, 2016;Winstral et al, 2002).…”

Section: Discussionmentioning

confidence: 99%

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Sensitivity of modeled snow stability data to meteorological input uncertainty

Herwijnen¹,

Rotach

Schweizer³

2020

Nat. Hazards Earth Syst. Sci.

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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.

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Section: Discussionsupporting

confidence: 80%

Section: Discussionmentioning

confidence: 99%

Sensitivity of modeled snow stability data to meteorological input uncertainty

Herwijnen¹,

Rotach

Schweizer³

2020

Nat. Hazards Earth Syst. Sci.

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“…In this study, we used the spatial configuration for Crocus forced by SAFRAN (massifs/elevations and corresponding to observation stations) used operationally in France in support for avalanche forecasting as described in Morin et al . (2020). The SAFRAN‐Crocus simulations were also used in the gap filling procedure of 28 long‐term snow observation series used in the model evaluation process (section 3.2).…”

Section: Methodsmentioning

confidence: 99%

Long‐term trends (1958–2017) in snow cover duration and depth in the Pyrenees

López‐Moreno¹,

Soubeyroux

Gascoin

et al. 2020

Intl Journal of Climatology

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This study investigated the temporal variability and changes in snow cover duration and the average snow depth from December to April in the Pyrenees at 1,500 and 2,100 m a.s.l. for the period 1958–2017. This is the first such analysis for the entire mountain range using SAFRAN‐Crocus simulations run for this specific purpose. The SAFRAN‐Crocus simulations were evaluated for the period 1980–2016 using 28 in situ snow depth data time series, and for the period 2000–2017 using MODIS observations of the snow cover duration. Following confirmation that the simulated snow series satisfactorily reproduced the observed evolution of the snowpack, the Mann–Kendall test showed that snow cover duration and average depth decreased during the full study period, but this was only statistically significant at 2,100 m a.s.l. The temporal evolution in the snow series indicated marked differences among massifs, elevations, and snow variables. In general, the most western massifs of the French Pyrenees underwent a greater decrease in the snowpack, while in some eastern massifs the snowpack did not decrease, and in some cases increased at 1,500 m a.s.l. The results suggest that the trends were consistent over time, as they were little affected by the start and end year of the study period, except if trends are computed only starting after 1980, when no significant trends were apparent. Most of the observed negative trends were not correlated with changes in the atmospheric circulation patterns during the snow season. This suggests that the continuous warming in the Pyrenees since the beginning of the industrial period, and particularly the sharp increase since 1955, is a major driver explaining the snow cover decline in the Pyrenees.

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“…snow layer temperatures, liquid water content and microstructure). Models used at very high resolution for avalanche risk forecasting (such as Crocus and SNOWPACK; Morin et al, 2020) and by the tourism industry are constantly being tested during the snow season and errors can cost lives and money. However, obtaining reliable data and designing appropriate evaluation methodologies to drive progress in complex snow models is challenging (Menard et al, 2019).…”

Section: What the Future Holdsmentioning

confidence: 99%