Elevation dependency of mountain snow depth

Grünewald, Thomas; Bühler, Yves; Lehning, Michael

doi:10.5194/tc-8-2381-2014

Cited by 122 publications

(102 citation statements)

References 66 publications

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“…10). This was also found by lidar measurements carried out in the same catchment (Helfricht et al, 2012) as well as in several catchments in Switzerland (Grünewald et al, 2014). By applying a simple hydrological model that uses elevation bands instead of raster cells to a variety of other catchments in the Alps, Frey (2015) could identify this elevation range, too.…”

Section: Transferability To Other Catchmentsmentioning

confidence: 68%

A conceptual, distributed snow redistribution model

Frey

Holzmann

2015

Hydrol. Earth Syst. Sci.

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Abstract. When applying conceptual hydrological models using a temperature index approach for snowmelt to high alpine areas often accumulation of snow during several years can be observed. Some of the reasons why these "snow towers" do not exist in nature are vertical and lateral transport processes. While snow transport models have been developed using grid cell sizes of tens to hundreds of square metres and have been applied in several catchments, no model exists using coarser cell sizes of 1 km 2 , which is a common resolution for meso-and large-scale hydrologic modelling (hundreds to thousands of square kilometres). In this paper we present an approach that uses only gravity and snow density as a proxy for the age of the snow cover and land-use information to redistribute snow in alpine basins. The results are based on the hydrological modelling of the Austrian Inn Basin in Tyrol, Austria, more specifically the Ötztaler Ache catchment, but the findings hold for other tributaries of the river Inn. This transport model is implemented in the distributed rainfall-runoff model COSERO (Continuous Semidistributed Runoff). The results of both model concepts with and without consideration of lateral snow redistribution are compared against observed discharge and snow-covered areas derived from MODIS satellite images. By means of the snow redistribution concept, snow accumulation over several years can be prevented and the snow depletion curve compared with MODIS (Moderate Resolution Imaging Spectroradiometer) data could be improved, too. In a 7-year period the standard model would lead to snow accumulation of approximately 2900 mm SWE (snow water equivalent) in high elevated regions whereas the updated version of the model does not show accumulation and does also predict discharge with more accuracy leading to a Kling-Gupta efficiency of 0.93 instead of 0.9. A further improvement can be shown in the comparison of MODIS snow cover data and the calculated depletion curve, where the redistribution model increased the efficiency (R 2 ) from 0.70 to 0.78 (calibration) and from 0.66 to 0.74 (validation).

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Section: Transferability To Other Catchmentsmentioning

confidence: 68%

A conceptual, distributed snow redistribution model

Frey

Holzmann

2015

Hydrol. Earth Syst. Sci.

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“…Topography is an important factor affecting climatology of snow depth and the main reason accounting for snow depth data inhomogeneity Lehning, 2011, 2013;Grünewald et al, 2014). To explore the effects of complex terrain on snow depth over Eurasia, we conducted a linear regression analysis of long-term mean annual snow depth with latitude, elevation, and continentality (Fig.…”

Section: Impact Of Topography On Snow Depthmentioning

confidence: 99%

“…Ye et al (1998) and Kitaev et al (2005) showed that higher air temperatures caused an increase in snowfall in winter from 1936 through 1995, and thus greater snow depth was observed in northern Eurasia. Snow depth distribution and variation are controlled by terrain (i.e., elevation, slope, aspect, and roughness) and vegetation Grünewald et al, 2014;Revuelto et al, 2014;Rees et al, 2014;Dickerson-Lange et al, 2015). Snow depth is closely related to synoptic-scale atmospheric circulation indices such as the North Atlantic OscillationArctic Oscillation (NAO/AO).…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal variability of snow depth across the Eurasian continent from 1966 to 2012

et al. 2018

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Abstract. Snow depth is one of the key physical parameters for understanding land surface energy balance, soil thermal regime, water cycle, and assessing water resources from local community to regional industrial water supply. Previous studies by using in situ data are mostly site specific; data from satellite remote sensing may cover a large area or global scale, but uncertainties remain large. The primary objective of this study is to investigate spatial variability and temporal change in snow depth across the Eurasian continent. Data used include long-term ground-based measurements from 1814 stations. Spatially, long-term (1971Spatially, long-term ( -2000 mean annual snow depths of >20 cm were recorded in northeastern European Russia, the Yenisei River basin, Kamchatka Peninsula, and Sakhalin. Annual mean and maximum snow depth increased by 0.2 and 0.6 cm decade −1 from 1966 through 2012. Seasonally, monthly mean snow depth decreased in autumn and increased in winter and spring over the study period. Regionally, snow depth significantly increased in areas north of 50 • N. Compared with air temperature, snowfall had greater influence on snow depth during November through March across the former Soviet Union. This study provides a baseline for snow depth climatology and changes across the Eurasian continent, which would significantly help to better understanding climate system and climate changes on regional, hemispheric, or even global scales.

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“…The accumulated snow depth varied from 1.16 to 4.34 m in the north flank, 1.68 to 2.94 m in the south flank and 0.87 to 2.61 m in the central glacier during the study period. More precipitation (snow) at higher altitude could be attributed to the orographic effect, which results in an increase in snow accumulation with elevation [43][44][45][46][47][48] . Correlation coefficients of accumulated snow depth on the north flank, south flank and central glacier with the altitude were found to be 0.78, 0.84 and 0.57 respectively (significant at 1% significance level).…”

Section: Uncertainties In Snow Accumulation On Glaciermentioning

confidence: 99%

Estimation of Snow Accumulation on Samudra Tapu Glacier, Western Himalaya Using Airborne Ground Penetrating Radar

Singh¹,

Negi²,

Kumar³

et al. 2017

Current Science

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In this study an airborne ground penetrating radar (GPR) is used to estimate spatial distribution of snow accumulation in the Samudra Tapu glacier (the Great Himalayan Range), Western Himalaya, India. An impulse radar system with 350 MHz antenna was mounted on a helicopter for the estimation of snow depth. The dielectric properties of snow were measured at a representative site (Patseo Observatory) using a snow fork to calibrate GPR data. The snow depths estimated from GPR signal were found to be in good agreement with those measured on ground with an absolute error of 0.04 m. The GPR survey was conducted over Samudra Tapu glacier in March 2009 and 2010. A kriging-based geostatistical interpolation method was used to generate a spatial snow accumulation map of the glacier with the GPR-collected data. The average accumulated snow depth and snow water equivalent (SWE) for a part of the glacier were found to be 2.23 m and 0.624 m for 2009 and 2.06 m and 0.496 m for 2010 respectively. Further, the snow accumulation data were analysed with various topographical parameters such as altitude, aspect and slope. The accumulated snow depth showed good correlation with altitude, having correlation coefficient varying between 0.57 and 0.84 for different parts of the glacier. Higher snow accumulation was observed in the north-and east-facing regions, and decrease in snow accumulation was found with an increase in the slope of the glacier. Thus, in this study we generate snow accumulation/SWE information using airborne GPR in the Himalayan terrain.Keywords: Glacier, ground penetrating radar, snow accumulation, snow water equivalent.IN the Himalaya snow and glaciers cover a large geographical area and influence climate and environment of the region. Recent studies carried out using satellite images suggest approximately 40,800 sq. km area is covered by glaciers in the Great Himalaya and Karakoram mountain ranges 1 . These glaciers are a perennial and vital source of freshwater to the countries around the Himalayan chain. In recent years, glaciers and seasonal snow cover in this region have been significantly influenced by climate change 2-7 ; therefore, it is useful to monitor the spatial and temporal changes of snow thickness.Estimation of snow thickness using field-based methods such as snow stakes, automatic sensors, etc. provides mostly point-specific information. However, ground penetrating radar (GPR), a non-destructive technique, can be used to collect both point as well as spatial distribution of snow thickness. It has been widely used in ground mode, for snow and ice thickness measurements [8][9][10][11][12][13][14][15] , snowpack stratigraphic delineation 16 and to study the subsurface properties of other strata 17 . Forte et al. 18,19 reported the applications of GPR data in determining the density and electromagnetic (EM) wave velocity for snow, firn and ice. Colucci et al. 20 used GPR profiles in combination with LiDAR data to calculate the volumetric and mass variations in the body of the ice. Apart from ground...

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Elevation dependency of mountain snow depth

Cited by 122 publications

References 66 publications

A conceptual, distributed snow redistribution model

A conceptual, distributed snow redistribution model

Spatiotemporal variability of snow depth across the Eurasian continent from 1966 to 2012

Estimation of Snow Accumulation on Samudra Tapu Glacier, Western Himalaya Using Airborne Ground Penetrating Radar

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