Regional evaluation of three day snow depth for avalanche hazard mapping in Switzerland

Bocchiola, Daniele; Janetti, Emanuela Bianchi; Gorni, E.; Marty, Christoph; Sovilla, Betty

doi:10.5194/nhess-8-685-2008

Cited by 32 publications

(36 citation statements)

References 64 publications

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“…The most direct application is an assessment of total snow water storage as compared to interpolations made from flat field snow stations (e.g. Bocchiola et al, 2008;Janetti et al 2008) and the validation of snow deposition and snow transport models . A further interesting aspect is the development of surface roughness as a function of the variable snow cover.…”

Section: Discussionmentioning

confidence: 99%

Spatial and temporal variability of snow depth and ablation rates in a small mountain catchment

Grünewald¹,

Schirmer²,

Mott³

et al. 2010

The Cryosphere

242

274

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Abstract. The spatio-temporal variability of the mountain snow cover determines the avalanche danger, snow water storage, permafrost distribution and the local distribution of fauna and flora. Using a new type of terrestrial laser scanner, which is particularly suited for measurements of snow covered surfaces, snow depth was monitored in a high alpine catchment during an ablation period. From these measurements snow water equivalents and ablation rates were calculated. This allowed us for the first time to obtain a high resolution (2.5 m cell size) picture of spatial variability of the snow cover and its temporal development. A very high variability of the snow cover with snow depths between 0-9 m at the end of the accumulation season was observed. This variability decreased during the ablation phase, while the dominant snow deposition features remained intact. The average daily ablation rate was between 15 mm/d snow water equivalent at the beginning of the ablation period and 30 mm/d at the end. The spatial variation of ablation rates increased during the ablation season and could not be explained in a simple manner by geographical or meteorological parameters, which suggests significant lateral energy fluxes contributing to observed melt. It is qualitatively shown that the effect of the lateral energy transport must increase as the fraction of snow free surfaces increases during the ablation period.

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Section: Discussionmentioning

confidence: 99%

Spatial and temporal variability of snow depth and ablation rates in a small mountain catchment

Grünewald¹,

Schirmer²,

Mott³

et al. 2010

The Cryosphere

242

274

View full text Add to dashboard Cite

show abstract

“…It has been widely used among others in hydrology (Katz et al, 2002;Vasiliev et al, 2007;Reiss and Thomas, 2007) and climatology (Naveau et al, 2005;Brown and Katz, 1995;Palutikof et al, 1999). However, its use for snow events is limited with a few exceptions (Bocchiola et al, 2006(Bocchiola et al, , 2008Blanchet et al, 2009). We revealed in a previous article (Blanchet et al, 2009) how extreme snowfall is spatially distributed over Switzerland and argued that this spread is determined by the main climatological patterns.…”

Section: Introductionmentioning

confidence: 99%

Mapping snow depth return levels: smooth spatial modeling versus station interpolation

Blanchet¹,

Lehning²

2010

Hydrol. Earth Syst. Sci.

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Abstract. For adequate risk management in mountainous countries, hazard maps for extreme snow events are needed. This requires the computation of spatial estimates of return levels. In this article we use recent developments in extreme value theory and compare two main approaches for mapping snow depth return levels from in situ measurements. The first one is based on the spatial interpolation of pointwise extremal distributions (the so-called Generalized Extreme Value distribution, GEV henceforth) computed at station locations. The second one is new and based on the direct estimation of a spatially smooth GEV distribution with the joint use of all stations. We compare and validate the different approaches for modeling annual maximum snow depth measured at 100 sites in Switzerland during winters 1965-1966 to 2007-2008. The results show a better performance of the smooth GEV distribution fitting, in particular where the station network is sparser. Smooth return level maps can be computed from the fitted model without any further interpolation. Their regional variability can be revealed by removing the altitudinal dependent covariates in the model. We show how return levels and their regional variability are linked to the main climatological patterns of Switzerland.

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“…Many studies have been conducted on snow cover area monitoring (Gutzler and Rosen, 1992;Hall et al, 2001;Liang et al, 2008b;Wang and Xie, 2009;Gao et al, 2010;Paudel et al, 2011), snow depth (SD) simulation (Stowe et al, 1991;Chang et al, 1996;Frei et al, 2012;Yu et al, 2012), snow disaster risk assessment (Romanov et al, 2002), loss evaluation post-disaster (i.e., the research for the pastoral areas after snow disasters have occurred and have resulted in losses of livestock) (Nakamura and Shindo, 2001), snow disaster and avalanche mapping, as well as their relations to climate change (Jones and Jamieson, 2001;Hendrikx et al, 2005;Bocchiola et al, 2008;Delparte et al, 2008;Hirashima et al, 2008;Lato et al, 2012). In 2006, the Chinese government issued a national standard for grading snow disasters in pastoral area (GB/T 20482-2006) (General Administration of Quality Supervision of China, 2006).…”

Section: Introductionmentioning

confidence: 99%

Early warning of snow-caused disasters in pastoral areas on the Tibetan Plateau

Wang

Liang

Huang

et al. 2013

Nat. Hazards Earth Syst. Sci.

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This study develops a model for early warning of snow-caused livestock disasters on a county basis and proposes a method of qualitative risk assessment of snow disasters at 500 m resolution for pastoral areas on the Tibetan Plateau (TP). Data used for the model development include remote sensing data, statistical data of weather, livestock, and social economy, and 45 typical snow disaster cases from 2000 to 2010. The principal component analysis (PCA) approach is used to choose 7 crucial factors that contribute over 85% of information for early warning snow disasters on the TP. They are mean annual probability of snow disaster, number of snow-covered days, livestock stocking rate, continual days of mean daily temperature below −10 °C, grassland burial index, rate of snow-covered grassland, and per livestock gross domestic product. The chosen 411 cases from 2008 to 2010 are used to validate the prediction results from the developed early warning model, with an overall accuracy of 85.64% in predicting snow disasters and no disasters. This suggests that the early warning approach developed in the study has operational potential for predicting snow disasters on the TP

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Regional evaluation of three day snow depth for avalanche hazard mapping in Switzerland

Cited by 32 publications

References 64 publications

Spatial and temporal variability of snow depth and ablation rates in a small mountain catchment

Spatial and temporal variability of snow depth and ablation rates in a small mountain catchment

Mapping snow depth return levels: smooth spatial modeling versus station interpolation

Early warning of snow-caused disasters in pastoral areas on the Tibetan Plateau

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