On forecasting wet-snow avalanche activity using simulated snow cover data

Bellaire, Sascha; Herwijnen, A. van; Mitterer, Christoph; Schweizer, Jürg

doi:10.1016/j.coldregions.2017.09.013

Cited by 44 publications

(36 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Wet snow avalanches are a common type of snow avalanche (hereafter also called avalanche) and can release from a point or as a slab, in both cases transporting high-density snow masses downslope at relatively low flow velocities (McClung and Schaerer 2006). Wet snow avalanches are serious natural hazards, due to their mass, their long runout distances and their difficulty to forecast (Bellaire et al 2017). Slushflows are a specific form of wet snow avalanche and are defined as a 'mudflow-like flowage of water-saturated snow' (Washburn and Goldthwait 1958), with a very high liquid water content (Fierz et al 2009).…”

Section: Introductionmentioning

confidence: 99%

A large wet snow avalanche cycle in West Greenland quantified using remote sensing and in situ observations

et al. 2019

View full text Add to dashboard Cite

On 11 April 2016 we observed high slushflow and wet snow avalanche activity at the environmental monitoring station Kobbefjord in W-Greenland. Snow avalanches released as a result of snow wetting induced by rain-on-snow in combination with a strong rise in air temperature. We exploit high-resolution satellite imagery covering pre-and post-event conditions for avalanche quantification and show that nearly 800 avalanches were triggered during this cycle. The nature of this extraordinary event is put into a longer temporal context by analysing several years of meteorological data and time-lapse imagery. We find that no event of similar size has occurred during the past 10 years of intense environmental monitoring in the study area. Meteorological reanalysis data reveal consistent relevant weather patterns for potential rain-on-snow events in the study area being warm fronts from Southwest with orographic lifting processes that triggered heavy precipitation.

show abstract

Section: Introductionmentioning

confidence: 99%

A large wet snow avalanche cycle in West Greenland quantified using remote sensing and in situ observations

et al. 2019

View full text Add to dashboard Cite

show abstract

“…As such, the introduced biases can be seen as potential errors due to the interpolation schemes, or biases in the NWP output. For instance, for air temperature, the variation of ± 3 • C (Table 1) corresponds roughly to typical errors between NWP output and TA measurements (Bellaire et al, 2017).…”

Section: Discussionmentioning

confidence: 52%

“…P had the most significant impact on modeled sensitivity, which may partly be due to the high magnitude of bias (Raleigh et al, 2015). These results have implications for spatial snow cover modeling, which is increasingly applied in avalanche forecasting (Bellaire et al, 2017(Bellaire et al, , 2011Morin et al, 2020;Lafaysse et al, 2017;Vernay et al, 2015). Indeed, our results suggest that if we want to obtain realistic spatial patterns, we need to adequately model snow distribution in mountainous regions.…”

Section: Discussionmentioning

confidence: 68%

Sensitivity of modeled snow stability data to meteorological input uncertainty

Herwijnen¹,

Rotach²,

Schweizer³

2020

Preprint

Self Cite

View full text Add to dashboard Cite

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 on 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. Uncertainty ranges for meteorological forcing covered typical differences observed within a distance of 2 km and an elevation change of 200 m. 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. 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 precipitation. Precipitation determined the load of the slab, which in turn influenced weak layer properties. For case b) and c), the two stability metrics showed contradicting behaviors. With increasing precipitation, i.e. deep snowpacks, the skier stability index decreased (less stable). In contrast, the critical crack length increased with increasing precipitation. With regard to spatial simulations of snow stability, the high sensitivity on precipitation suggests that accurate precipitation patterns are necessary to obtain realistic snow stability patterns. With regard to spatial simulations of snow stability, the high sensitivity on precipitation suggests that accurate precipitation patterns are necessary to obtain realistic snow stability patterns.

show abstract

“…Ref. [15] uses this model in conjunction with a snow cover model to forecast avalanche activity; particularly, they concentrate on wet-snow instability. The WSL Institute for Snow and Avalanche Research SLF creates a repository that includes the snow cover model that they used and other similar ones (https://models.slf.ch/).…”

Section: Avalanchesmentioning

confidence: 99%

The Internet of Things for Natural Risk Management (Inte.Ri.M.)

Beltramo¹,

Cantore²,

Vesce³

et al. 2019

Perspectives on Risk, Assessment and Management Paradigms

View full text Add to dashboard Cite

This chapter deals with the development of a management system, which integrates the use of IoT in natural risk detection, revention, and management with economic evaluation of each stage. In the introductory part, recent data are presented that document the importance that natural disasters have for the environment and for the Italian economy. Section 2 presents the Inte.Ri.M. project-the Internet of Things for Natural Risk Management-its purpose, activity plan, and bodies involved. Technical aspects are treated in Section 3 with the choice of hardware and software components and the solutions for collecting and transmitting data. Section 4 is about the economic aspects considering the stages of prevention, intervention, and restoration and the relation between the intensity of human activity and environment to define a range of situations. These scenarios call for different economic methodologies useful to estimate economic implications of each stage in the short, medium, and long term. Section 5 describes the structure of the Inte.Ri.M. management system and the foreseen functionalities. In the conclusion, the critical points are discussed, and the steps for the transposition of the work carried out on the territory are outlined, according to the provisions of the work program.

show abstract

On forecasting wet-snow avalanche activity using simulated snow cover data

Cited by 44 publications

References 37 publications

A large wet snow avalanche cycle in West Greenland quantified using remote sensing and in situ observations

A large wet snow avalanche cycle in West Greenland quantified using remote sensing and in situ observations

Sensitivity of modeled snow stability data to meteorological input uncertainty

The Internet of Things for Natural Risk Management (Inte.Ri.M.)

Contact Info

Product

Resources

About