Predicting snow density using meteorological data

Meløysund, Vivian; Leira, Bernt J.; Høiseth, Karl V.; Lisø, Kim Robert

doi:10.1002/met.40

Cited by 69 publications

(39 citation statements)

References 9 publications

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“…The former status occurs principally during the accumulation season, while the latter one during the melting season. Estimates of the bulk density of the snowpack are often operated via multiple regressions on variables including snow depth, temperature, site altitude, wind velocity (Meløysund et al, 2007;Bavera and De Michele, 2009;Jonas et al, 2009;Bavera et al, 2012), with values of the determination coefficient up to ≈ 0.70.…”

Section: Introductionmentioning

confidence: 99%

Investigating the dynamics of bulk snow density in dry and wet conditions using a one-dimensional model

et al. 2013

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Abstract. The snowpack is a complicated multiphase mixture with mechanical, hydraulic, and thermal properties highly variable during the year in response to climatic forcings. Bulk density is a macroscopic property of the snowpack used, together with snow depth, to quantify the water stored. In seasonal snowpacks, the bulk density is characterized by a strongly non-linear behaviour due to the occurrence of both dry and wet conditions. In the literature, bulk snow density estimates are obtained principally with multiple regressions, and snowpack models have put the attention principally on the snow depth and snow water equivalent. Here a one-dimensional model for the temporal dynamics of the snowpack, with particular attention to the bulk snow density, has been proposed, accounting for both dry and wet conditions. The model represents the snowpack as a twoconstituent mixture: a dry part including ice structure, and air; and a wet part constituted by liquid water. It describes the dynamics of three variables: the depth and density of the dry part and the depth of liquid water. The model has been calibrated and validated against hourly data registered at three SNOTEL stations, western US, with mean values of the Nash-Sutcliffe coefficient ≈ 0.73-0.97 in the validation period.

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Section: Introductionmentioning

confidence: 99%

Investigating the dynamics of bulk snow density in dry and wet conditions using a one-dimensional model

et al. 2013

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“…This value, according to Meløysund et al [19], can be considered equivalent to 165 mm of rain. The second, smaller, change of trend was recorded on 9 June 2016 after a long period of above-average water discharge in the drainage system of the road tunnel.…”

Section: Discussionmentioning

confidence: 99%

Structural Health Monitoring of a Road Tunnel Intersecting a Large and Active Landslide

2017

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Dealing with engineering structures that are not easily replaceable requires frequent assessment of the damage state of the construction in order to estimate its durability and reliability. The procedures that allow damage to be detected and identified are broadly defined as Structural Health Monitoring (SHM). In this work, a SHM network has been deployed in a road tunnel that intersects a massive landslide, whose movements are causing the formation of cracks along the tunnel lining. The monitoring system measures in real time the displacements across major cracks and the rotation of the tunnel segments; data are gathered and made easily accessible through a web-based platform. The mechanisms by which the tunnel deforms under the landslide-induced stress have been defined through the analysis of three years of monitoring data. The factors triggering an increase in deformation rates and causing damage to the structure have also been investigated. This evidence will support the design of mitigation works to extend the life-span of the tunnel.

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“…As given in NS 3491-3 and EN 1991-1-3 (European Committee for Standardization, 2003) the mean density of snow is given in Table 1 (Meløysund et al, 2007).…”

Section: Introductionmentioning

confidence: 99%

Estimation of snow density using full-polarimetric Synthetic Aperture Radar (SAR) data

Surendar¹,

Bhattacharya²,

Singh³

et al. 2015

Physics and Chemistry of the Earth, Parts A/B/C

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Predicting snow density using meteorological data

Cited by 69 publications

References 9 publications

Investigating the dynamics of bulk snow density in dry and wet conditions using a one-dimensional model

Investigating the dynamics of bulk snow density in dry and wet conditions using a one-dimensional model

Structural Health Monitoring of a Road Tunnel Intersecting a Large and Active Landslide

Estimation of snow density using full-polarimetric Synthetic Aperture Radar (SAR) data

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