Spatial snowfall distribution in mountainous areas estimated with a snow model and satellite remote sensing

Asaoka, Yoshihiro; Kominami, Yuji

doi:10.3178/hrl.6.1

Cited by 20 publications

(26 citation statements)

References 21 publications

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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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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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“…Lauscher, 1976;Rohrer et al, 1994;Basist et al, 1994;Sevruk, 1997;Wastl and Zängl, 2008). This strong variability is attributed to the highly complex interaction of the weather patterns with the local topography.…”

Section: T Grünewald Et Al: Elevation Dependency Of Snowmentioning

confidence: 99%

“…Most studies identified a distinctive increase of precipitation with altitude (e.g. Spreen, 1947;Peck and Brown, 1962;Frei and Schär, 1998;Blumer, 1994;Johnson and Hanson, 1995;Roe and Baker, 2006;Liu et al, 2011;Asaoka and Kominami, 2012). Contrary, Blumer (1994), Basist et al (1994) and Arakawa and Kitoh (2011) reported on negative elevation gradients of precipitation.…”

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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“…Second, the spatio-temporal patterns of the TLR varied greatly across the different geographical regions of Taiwan. Consequently, the variations of the TLR among the different regions imply that the use of a constant, or socalled standard TLR, such as -6.0 or -6.5°C km -1 (e.g., Willmott and Matsuura 2001; Asaoka and Kominami 2012;Samanta et al 2012) is unjustifiable when predicting full-scale temperatures, especially in complex terrains and under monsoon conditions, as in Taiwan. For example, air temperature of Mt.…”

Section: Regression Formula T = α + β × Ementioning

confidence: 99%

Spatio-Temporal Variation and Monsoon Effect on the Temperature Lapse Rate of a Subtropical Island

Chiu¹,

Lin²,

Tsai³

2014

Terr. Atmos. Ocean. Sci.

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Temperature lapse rate (TLR) has been widely used in the prediction of mountain climate and vegetation and in many ecological models. The aims of this paper are to explore the spatio-temporal variations and monsoon effects on the TLR in the subtropical island of Taiwan with its steep Central Mountain Region (CMR). A TLR analysis using the 32-year monthly mean air temperatures and elevations from 219 weather stations (sea level to 3852 m a.s.l.) was performed based on different geographical regions and monsoon exposures. The results revealed that the average TLR for all of Taiwan is -5.17°C km -1 , with a general tendency to be steeper in summer and shallower in winter. The results are also shallower than the typical or global average TLR of -6.5°C km -1 . During the prevailing northeast monsoon season (winter), the TLR exhibits a contrast between the windward side (steeper, -5.97°C km -1 ) and the leeward side (shallower, -4.51°C km -1 ). From the diagnosis on spatial characteristics of monthly cloud amount and vertical atmospheric profiles, this contrasting phenomenon may be explained by the warming effect of onshore stratus clouds (500 -2500 m depth) on cold and dry Siberian monsoon air on the windward side of the CMR. On the southwestern leeward side of the CMR, the low-level (1500 m), the weak ventilation atmosphere and temperature inversion make the TLR shallower than on the windward side.Key words: Temperature lapse rate, Prevailing monsoon, Spatio-temporal variation, Taiwan Citation: Chiu, C. A., P. H. Lin, and C. Y. Tsai, 2014: Spatio-temporal variation and monsoon effect on the temperature lapse rate of a subtropical island.

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Spatial snowfall distribution in mountainous areas estimated with a snow model and satellite remote sensing

Cited by 20 publications

References 21 publications

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

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

Elevation dependency of mountain snow depth

Spatio-Temporal Variation and Monsoon Effect on the Temperature Lapse Rate of a Subtropical Island

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