scholarly journals Intercomparison of measurements of bulk snow density and water equivalent of snow cover with snow core samplers: Instrumental bias and variability induced by observers

2020 ◽  
Vol 34 (14) ◽  
pp. 3120-3133
Author(s):  
J. Ignacio López‐Moreno ◽  
Leena Leppänen ◽  
Bartłomiej Luks ◽  
Ladislav Holko ◽  
Ghislain Picard ◽  
...  
Keyword(s):  
Snow Cover ◽  
Snow Density ◽  
Instrumental Bias

Measurement inter-comparison of bulk snow density and water equivalent of snow cover with snow core samplers

2020 ◽  
Author(s):  
Leena Leppänen ◽  
Juan Ignazio Lopez-Moreno ◽  
Bartłomiej Luks ◽  
Ladislav Holko ◽  
Ghislain Picard ◽  
...  
Keyword(s):  
Snow Cover ◽  
Bulk Density ◽  
Snow Depth ◽  
Ground Truth ◽  
Natural Variability ◽  
Repeated Measurements ◽  
Snow Density ◽  
Field Campaign ◽  
Uncertainty Sources ◽  
Instrumental Bias

<p>Manually collected snow data can be considered as ground truth for many applications, such as climatological or hydrological studies. Water equivalent of snow cover (SWE) can be manually measured by using a snow tube or snow cylinder to extract a snow core and measure the bulk density of the core by weighing it. Different snow core samplers and scales are used, but they all use the same measurement principle. However, there are various sources of uncertainty that have not been quantified in detail. To increase the understanding of these errors, different manual SWE measurement devices used across Europe were evaluated within the framework of the COST Action ES1404 HarmoSnow. Two field campaigns were organized in different environments to quantify uncertainties when measuring snow depth, snow bulk density and SWE with core samplers. The 1<sup>st</sup> field campaign in 2017 in Iceland focused on measurement differences attributed to different instrumentation compared with the natural variability in the snowpack, and the 2<sup>nd</sup> field campaign in 2018 in Finland focused on device comparison and on the separation of the different sources of variability. To our knowledge, such a comparison has not previously been conducted in terms of the number of device and different environments.</p><p>During the 1<sup>st</sup> campaign, repeated measurements were taken along two 20 m long snow trenches to distinguish snow variability measured at the plot and at the point scale. The results revealed a much higher variability of SWE at the plot scale, resulting from both natural variability and instrument bias, compared to repeated measurements at the same spot, resulting mostly from error induced by observers or a high variability in the snow depth. Snow Micro Pen sampling showed that the snowpack was very homogeneous for the 2<sup>nd</sup> campaign, which allowed for the disregarding of the natural variability of the snowpack properties and the focus to be on separating between instrumental bias and error induced by observers. Results confirmed that instrumental bias exceeded both the natural variability and the error induced by observers, even when observers performed measurements with snow core samplers they were not formally trained on. Under such measurement conditions, the uncertainty in bulk snow density estimation is about 5% for an individual instrument and is close to 10% among different instruments. The results showed that the devices provided slightly different uncertainties since they were designed for different snow conditions. The aim of this comparison was not to provide a definitive estimation of uncertainty for manual SWE measurements, but to illustrate the role of the different uncertainty sources.</p>

scholarly journals Intercomparison of measurements of bulk snow density and water equivalent of snow cover with snow core samplers: instrumental bias and variability induced by observers

Authorea ◽  
2019 ◽  
Author(s):  
Ignacio Lopez Moreno ◽  
Leena Lepp nen ◽  
Bart omiej Luks ◽  
Ladislav Holko ◽  
Ghislain Picard ◽  
...  
Keyword(s):  
Snow Cover ◽  
Snow Density ◽  
Instrumental Bias

scholarly journals Some Measurements of Settlement in a Rocky Mountains Snow Cover

1978 ◽  
Vol 20 (82) ◽  
pp. 141-148 ◽  
Author(s):  
James D. Bergen
Keyword(s):  
Grain Size ◽  
Snow Cover ◽  
Rocky Mountains ◽  
Early Spring ◽  
Fair Agreement ◽  
Snow Density ◽  
Density Measurements ◽  
Colorado Rockies ◽  
Precipitation Records ◽  
Snow Temperature

AbstractSnow-cover settlement was measured in a dry, annual sub-alpine snow cover in the Colorado Rockies with settlement gages. Settlement viscosities were calculated from the change in gage heights over various periods during the winter and early spring, and the associated overburden over the gages as estimated from density measurements and precipitation records. When adjustments are made for local snow temperature, viscosities are in fair agreement with values found in the literature from similar snow covers, although considerable scatter for a given snow density is found in all sets compared. The viscosity for a given density does not appear to vary systematically with grain size.

Snow accumulation and ablation in disturbed mountain spruce forest in West Tatra Mts

Biologia ◽  
2014 ◽  
Vol 69 (11) ◽  
Author(s):  
Martin Bartík ◽  
Roman Sitko ◽  
Marek Oreňák ◽  
Juraj Slovik ◽  
Jaroslav Škvarenina
Keyword(s):  
Snow Cover ◽  
Open Space ◽  
Snow Water Equivalent ◽  
Reference Value ◽  
Research Area ◽  
Snow Accumulation ◽  
Snow Density ◽  
Tatra Mts ◽  
Snow Water ◽  
The Impact

AbstractIn the presented paper we deal with the impact of the mature spruce stand on the accumulation and melting of snow cover at Červenec research area located in the Western Tatras at an elevation of 1420 m a.s.l. The work analyses the data obtained from the monitoring of snow cover during the period 2009–2014 (6 seasons). Since the season 2012/2013 the measurements have been also performed in a dead part of the stand and in a meadow. The results proved significant impact of the spruce stand on hydro-physical characteristics of snow cover — snow water equivalent, snow density, as well as on their change due to the dieback of the stand. The data measured at individual locations (open space in the forest, open meadow area, living and dead forest) were tested with the paired t-test for the significance of average differences. Average snow water equivalent in the living forest, dead forest and meadow was 42%, 47% and 83% of the reference value measured at the open space in the forest, respectively. The process of snow accumulation and melting was fastest at the open space, followed by the dead forest. In the living forest, the processes were the slowest.

scholarly journals Model for effective thermal conductivity of a dry snow cover composed of uniform ice spheres

1993 ◽  
Vol 18 ◽  
pp. 300-304 ◽  
Author(s):  
Edward E. Adams ◽  
Atsushi Sato
Keyword(s):  
Thermal Conductivity ◽  
Thermal Resistance ◽  
Snow Cover ◽  
Effective Thermal Conductivity ◽  
Pore Space ◽  
Contact Radius ◽  
Snow Density ◽  
Temperature Conductivity ◽  
In Series ◽  
Sphere Radius

The effective thermal conductivity of a snow cover is estimated assuming an idealized collection of uniformly packed ice spheres. An effective thermal conductivity is calculated based on the thermal resistance due to ice-grain contacts or bonds, the pore space/ice acting in series and the unobstructed pore. It is shown to depend very strongly on the snow density and intergranular bonding and, to some extent, on temperature. Conductivity tends to increase as density and the ratio of the contact radius to ice-sphere radius increase. The ice network is generally determined to be the most influential in determining the effective thermal conductitivity. Calculated results fall within the range of empirically determined values.

scholarly journals Snow cover variability across glaciers in Nordenskiöldland (Svalbard) from point measurements in 2014–2016

2019 ◽  
Author(s):  
Marco Möller ◽  
Rebecca Möller
Keyword(s):  
Snow Cover ◽  
Snow Depth ◽  
East Coast ◽  
Data Repository ◽  
Snow Density ◽  
Snow Layer ◽  
Density Measurements ◽  
Sampling Locations ◽  
Snow Water ◽  
Terrain Elevation

Abstract. Snow depths and bulk densities of the annual snow layer were measured at 69 different locations on glaciers across Nordenskiöldland, Svalbard, during the spring seasons of the period 2014–2016. Sampling locations lie along nine transects extending over 17 individual glaciers. Several of the locations were visited repeatedly, leading to a total of 109 point measurements, on which we report in this study. Snow water equivalents were calculated for each point measurement. In the dataset, snow depth and density measurements are accompanied by appropriate uncertainties which are rigorously transferred to the calculated snow water equivalents using a straightforward Monte Carlo simulation-style procedure. The final dataset can be downloaded from the Pangaea data repository (https://www.pangaea.de; https://doi.org/10.1594/PANGAEA.896581). Snow cover data indicate a general and statistically significant increase of snow depths and water equivalents with terrain elevation. A significant increase of both quantities with decreasing distance towards the east coast of Nordenskiöldland is also evident, but shows distinct interannual variability. Snow density does not show any characteristic spatial pattern.

scholarly journals Review article: Parameterizations of snow-related physical processes in land surface models

2021 ◽  
Author(s):  
Won Young Lee ◽  
Hyeon-Ju Gim ◽  
Seon Ki Park
Keyword(s):  
Snow Cover ◽  
Land Surface ◽  
Snow Depth ◽  
Land Surface Model ◽  
Surface Model ◽  
Snow Density ◽  
Physical Processes ◽  
Snow Albedo ◽  
Surface Models ◽  
Noah Land Surface Model

Abstract. Snow on land surface plays a vital role in the interaction between land and atmosphere in the state-of-the-art land surface models (LSMs) and the real world. Since the snow cover affects the snow albedo and the ground and soil heat fluxes, it is crucial to detect snow cover changes accurately. It is challenging to acquire observation data for snow cover, snow albedo, and snow depth; thus, an excellent alternative is to use the simulation data produced by the LSMs that calculate the snow-related physical processes. The LSMs show significant differences in the complexities of the snow parameterizations in terms of variables and processes considered. Thus, the synthetic intercomparisons of the snow physics in the LSMs will help the improvement of each LSM. This study revealed and discussed the differences in the parameterizations among LSMs related to snow cover fraction, snow albedo, and snow density. We selected the most popular and well-documented LSMs embedded in the Earth System Model or operational forecasting systems. We examined single layer schemes, including the Unified Noah Land Surface Model (Noah LSM), the Hydrology Tiled ECMWF Scheme of Surface Exchanges over Land (HTESSEL), the Biosphere-Atmosphere Transfer Scheme (BATS), the Canadian Land Surface Scheme (CLASS), and multilayer schemes of intermediate complexity including the Community Noah Land Surface Model with Multi-Parameterization Options (Noah-MP), the Community Land Model version 5 (CLM 5), the Joint UK Land Environment Simulator (JULES), and the Interaction Soil-Biosphere-Atmosphere (ISBA). First, we identified that BATS, Noah-MP, JULES, and ISBA reflect the snow depth and roughness length to parameterize snow cover fraction, and CLM 5 accounts for the standard deviation of the elevation value for the snow cover decay function. Second, CLM 5 and BATS are relatively complex, so that they explicitly take into account the solar zenith angle, black carbon, mineral dust, organic carbon, and ice grain size for the determinations of snow albedo. Besides, JULES and ISBA are also complicated model which concerns ice grain size, solar zenith angle, new snow depth, fresh snowfall rate, and surface temperature for the albedo scheme. Third, HTESSEL, CLM 5, and ISBA considered the effects of both wind and temperature in the determinations of the new snow density. Especially, ISBA and JULES considered internal snow characteristics such as snow viscosity, snow temperature, and vertical stress for parameterizing new snow density. The future outlook discussed geomorphic and vegetation-related variables for the further improvement of the LSMs. Previous studies clearly show that spatio-temporal variation of snow is due to the influence of altitude, slope, and vegetation condition. Therefore, we recommended applying geomorphic and vegetation factors such as elevation, slope, time-varying roughness length, vegetation indexes, or optimized parameters according to the land surface type to parameterize snow-related physical processes.

scholarly journals Spatial Variability of Snow Water Equivalent – The Case Study from the Research Site in Khibiny Mountains, Russia

2019 ◽  
Vol 67 (1) ◽  
pp. 110-112 ◽  
Author(s):  
Anton Yu. Komarov ◽  
Yury G. Seliverstov ◽  
Pavel B. Grebennikov ◽  
Sergey A. Sokratov
Keyword(s):  
Spatial Variability ◽  
Snow Cover ◽  
Snow Water Equivalent ◽  
Snow Density ◽  
Hydrological Characteristic ◽  
Snow Water ◽  
Exact Position ◽  
Seasonal Snow Cover ◽  
Uniform Area

Abstract The aim of the investigation was assessment of spatial variability of the characteristics of snowpack, including the snow water equivalent (SWE) as the main hydrological characteristic of a seasonal snow cover. The study was performed in Khibiny Mountains (Russia), where snow density and snow cover stratigraphy were documented with the help of the SnowMicropen measurements, allowing to determine the exact position of the snow layers’ boundaries with accuracy of 0.1 cm. The study site was located at the geomorphologically and topographically uniform area with uniform vegetation cover. The measurement was conducted at maximum seasonal SWE on 27 March 2016. Twenty vertical profiles were measured along the 10 m long transect. Vertical resolution depended on the thickness of individual layers and was not less than 10 cm. The spatial variation of the measured snowpack characteristics was substantial even within such a homogeneous landscape. Bulk snow density variability was similar to the variability in snow height. The total variation of the snowpack SWE values along the transect was about 20%, which is more than the variability in snow height or snow density, and should be taken into account in analysis of the results of normally performed in operational hydrology snow course SWE estimations by snow tubes.

scholarly journals Multilayer observation and estimation of the snowpack cold content in a humid boreal coniferous forest of eastern Canada

2021 ◽  
Vol 15 (12) ◽  
pp. 5371-5386
Author(s):  
Achut Parajuli ◽  
Daniel F. Nadeau ◽  
François Anctil ◽  
Marco Alves
Keyword(s):  
Time Series ◽  
Snow Cover ◽  
Snow Water Equivalent ◽  
Critical Role ◽  
Coniferous Forest ◽  
Energy Deficit ◽  
Snow Density ◽  
The Impact ◽  
Snow Temperature

Abstract. Cold content (CC) is an internal energy state within a snowpack and is defined by the energy deficit required to attain isothermal snowmelt temperature (0 ∘C). Cold content for a given snowpack thus plays a critical role because it affects both the timing and the rate of snowmelt. Measuring cold content is a labour-intensive task as it requires extracting in situ snow temperature and density. Hence, few studies have focused on characterizing this snowpack variable. This study describes the multilayer cold content of a snowpack and its variability across four sites with contrasting canopy structures within a coniferous boreal forest in southern Québec, Canada, throughout winter 2017–2018. The analysis was divided into two steps. In the first step, the observed CC data from weekly snowpits for 60 % of the snow cover period were examined. During the second step, a reconstructed time series of modelled CC was produced and analyzed to highlight the high-resolution temporal variability of CC for the full snow cover period. To accomplish this, the Canadian Land Surface Scheme (CLASS; featuring a single-layer snow model) was first implemented to obtain simulations of the average snow density at each of the four sites. Next, an empirical procedure was used to produce realistic density profiles, which, when combined with in situ continuous snow temperature measurements from an automatic profiling station, provides a time series of CC estimates at half-hour intervals for the entire winter. At the four sites, snow persisted on the ground for 218 d, with melt events occurring on 42 of those days. Based on snowpit observations, the largest mean CC (−2.62 MJ m−2) was observed at the site with the thickest snow cover. The maximum difference in mean CC between the four study sites was −0.47 MJ m−2, representing a site-to-site variability of 20 %. Before analyzing the reconstructed CC time series, a comparison with snowpit data confirmed that CLASS yielded reasonable bulk estimates of snow water equivalent (SWE) (R2=0.64 and percent bias (Pbias) =-17.1 %), snow density (R2=0.71 and Pbias =1.6 %), and cold content (R2=0.93 and Pbias =-3.3 %). A snow density profile derived by utilizing an empirical formulation also provided reasonable estimates of layered cold content (R2=0.42 and Pbias =5.17 %). Thanks to these encouraging results, the reconstructed and continuous CC series could be analyzed at the four sites, revealing the impact of rain-on-snow and cold air pooling episodes on the variation of CC. The continuous multilayer cold content time series also provided us with information about the effect of stand structure, local topography, and meteorological conditions on cold content variability. Additionally, a weak relationship between canopy structure and CC was identified.

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