Snow distribution in a steep mid-latitude alpine catchment

Kerr, Tim; Clark, Martyn P.; Hendrikx, Jordy; Anderson, B.

doi:10.1016/j.advwatres.2012.12.010

Cited by 36 publications

(38 citation statements)

References 29 publications

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“…Changes in snowpack properties also influence the development of avalanches in the Alps [ Castebrunet et al , ]. Studies to assess snowpack changes in relation to environmental impacts have also been conducted for other mountain chains such as the Australian Alps [ Whetton et al , ] and New Zealand's Southern Alps [ Kerr et al , ].…”

Section: Introductionmentioning

confidence: 99%

Measurement of the physical properties of the snowpack

Kinar

Pomeroy

2015

Reviews of Geophysics

190

202

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This paper reviews measurement techniques and corresponding devices used to determine the physical properties of the seasonal snowpack from distances close to the ground surface. The review is placed in the context of the need for scientific observations of snowpack variables that provide inputs for predictive hydrological models that help to advance scientific understanding of geophysical processes related to snow in the near-surface cryosphere. Many of these devices used to measure snow are invasive and require the snowpack to be disrupted, thereby precluding the possibility for multiple measurements to be made at the same sampling location. Moreover, many devices rely on the use of empirical calibration equations that may not be valid at all geographic locations. The spatial density of observations with most snow measurement devices is often inadequate. There is a need for improved automation of snowpack measurement instrumentation with an emphasis on field-based feedback of measurement validity in lieu of postprocessing of samples or data at a lab or office location. The scientific future of snow measurement instrumentation thereby requires a synthesis between science and engineering principles that takes into consideration geophysics and the physics of device operation.

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Section: Introductionmentioning

confidence: 99%

Measurement of the physical properties of the snowpack

Kinar

Pomeroy

2015

Reviews of Geophysics

190

202

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“…The degree of slope impacts the stability of the snowpack (influencing snow accumulation and redistribution) and input of solar radiation (influencing melt) (Anderton et al, 2004). Previous studies have used slope angle as an explanatory variable for describing the distribution of snow (e.g., Kerr et al, 2013), therefore the variable was tested in this study.…”

Section: Basin Scale Swe Variabilitymentioning

confidence: 99%

What drives basin scale spatial variability of snowpack properties in northern Colorado?

Sexstone

Fassnacht

2014

The Cryosphere

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Abstract. This study uses a combination of field measurements and Natural Resource Conservation Service (NRCS) operational snow data to understand the drivers of snow density and snow water equivalent (SWE) variability at the basin scale (100s to 1000s km 2 ). Historic snow course snowpack density observations were analyzed within a multiple linear regression snow density model to estimate SWE directly from snow depth measurements. Snow surveys were completed on or about 1 April 2011 and 2012 and combined with NRCS operational measurements to investigate the spatial variability of SWE near peak snow accumulation. Bivariate relations and multiple linear regression models were developed to understand the relation of snow density and SWE with terrain variables (derived using a geographic information system (GIS)). Snow density variability was best explained by day of year, snow depth, UTM Easting, and elevation. Calculation of SWE directly from snow depth measurement using the snow density model has strong statistical performance, and model validation suggests the model is transferable to independent data within the bounds of the original data set. This pathway of estimating SWE directly from snow depth measurement is useful when evaluating snowpack properties at the basin scale, where many timeconsuming measurements of SWE are often not feasible. A comparison with a previously developed snow density model shows that calibrating a snow density model to a specific basin can provide improvement of SWE estimation at this scale, and should be considered for future basin scale analyses. During both water year (WY) 2011 and 2012, elevation and location (UTM Easting and/or UTM Northing) were the most important SWE model variables, suggesting that orographic precipitation and storm track patterns are likely driving basin scale SWE variability. Terrain curvature was also shown to be an important variable, but to a lesser extent at the scale of interest.

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“…However, other studies did not identify positive elevation gradients for their study sites and some even found negative dependencies of elevation and snow amount. For a study site in New Zealand, Kerr et al (2013) could not identify elevation gradients of SWE, and no clear correlations between elevation and SWE were found Published by Copernicus Publications on behalf of the European Geosciences Union.…”

Section: Introductionmentioning

confidence: 99%

Elevation dependency of mountain snow depth

Bühler²,

2014

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Abstract. Elevation strongly affects quantity and distribution patterns of precipitation and snow. Positive elevation gradients were identified by many studies, usually based on data from sparse precipitation stations or snow depth measurements. We present a systematic evaluation of the elevationsnow depth relationship. We analyse areal snow depth data obtained by remote sensing for seven mountain sites near to the time of the maximum seasonal snow accumulation. Snow depths were averaged to 100 m elevation bands and then related to their respective elevation level. The assessment was performed at three scales: (i) the complete data sets (10 km scale), (ii) sub-catchments (km scale) and (iii) slope transects (100 m scale). We show that most elevation-snow depth curves at all scales are characterised through a single shape. Mean snow depths increase with elevation up to a certain level where they have a distinct peak followed by a decrease at the highest elevations. We explain this typical shape with a generally positive elevation gradient of snow fall that is modified by the interaction of snow cover and topography. These processes are preferential deposition of precipitation and redistribution of snow by wind, sloughing and avalanching. Furthermore, we show that the elevation level of the peak of mean snow depth correlates with the dominant elevation level of rocks (if present).

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Snow distribution in a steep mid-latitude alpine catchment

Cited by 36 publications

References 29 publications

Measurement of the physical properties of the snowpack

Measurement of the physical properties of the snowpack

What drives basin scale spatial variability of snowpack properties in northern Colorado?

Elevation dependency of mountain snow depth

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