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

2013 ◽  
Vol 7 (2) ◽  
pp. 433-444 ◽  
Author(s):  
C. De Michele ◽  
F. Avanzi ◽  
A. Ghezzi ◽  
C. Jommi
Keyword(s):  
Bulk Density ◽  
Liquid Water ◽  
Snow Depth ◽  
Snow Water Equivalent ◽  
Temporal Dynamics ◽  
Dimensional Model ◽  
Snow Density ◽  
One Dimensional ◽  
Hourly Data ◽  
Dry And Wet Conditions

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 two-constituent 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.

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

2012 ◽  
Vol 6 (4) ◽  
pp. 2305-2325
Author(s):  
C. De Michele ◽  
F. Avanzi ◽  
A. Ghezzi ◽  
C. Jommi
Keyword(s):  
Bulk Density ◽  
Liquid Water ◽  
Snow Depth ◽  
Snow Water Equivalent ◽  
Temporal Dynamics ◽  
Dimensional Model ◽  
Snow Density ◽  
Mean Values ◽  
One Dimensional ◽  
Hourly Data

Abstract. Snowpack is a complicated multiphase mixture with mechanical, hydraulic, and thermal properties, highly variable within 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 strong non-linear behaviour due to the occurrence of both dry and wet conditions. In 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 bulk snow density has been proposed, accounting for both dry and moist conditions. The model assimilates the snowpack to a two-constituent 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 in two SNOTEL stations, Western US, with mean values of the Nash-Sutcliffe coefficient ≈0.90–0.92.

scholarly journals Inter-comparison of snow depth over sea ice from multiple methods

2020 ◽  
Author(s):  
Lu Zhou ◽  
Julienne Stroeve ◽  
Shiming Xu ◽  
Alek Petty ◽  
Rachel Tilling ◽  
...  
Keyword(s):  
Sea Ice ◽  
Snow Depth ◽  
Ground Truth ◽  
Significant Trend ◽  
Data Sets ◽  
Snow Density ◽  
Spatial And Temporal Scales ◽  
Danish Meteorological Institute ◽  
The University

Abstract. In this study, we compare eight recently developed snow depth products that use satellite observations, modeling or a combination of satellite and modeling approaches. These products are further compared against various ground-truth observations, including those from ice mass balance buoys (IMBs), snow buoys, snow depth derived from NASA's Operation IceBridge (OIB) flights, as well as snow depth climatology from historical observations. Large snow depth discrepancies between the different snow depth data sets are observed over the Atlantic and Canadian Arctic sectors. Among the products evaluated, the University of Washington snow depth product (UW) produces the overall deepest spring (March-April) snow packs, while the snow product from the Danish Meteorological Institute (DMI) provide the shallowest spring snow depths. There is no significant trend in the mean snow depth among all snow products since the 2000s, despite the great differences in regional snow depth. Two products, SnowModel-LG and the NASA Eulerian Snow on Sea Ice Model (NESOSIM), also provide estimates of snow density. Arctic-wide, these density products show the expected seasonal evolution with varying inter-annual variability, and no significant trend since the 2000s. The snow density in SnowModel-LG is generally higher than climatology, whereas NESOSIM density is generally lower. Both SnowModel-LG and NESOSIM densities have a larger seasonal change than climatology. Inconsistencies in the reconstructed snow parameters among the products, as well as differences between in-situ and airborne observations can in part be attributed to differences in effective footprint and spatial/temporal coverage, as well as insufficient observations for validation/bias adjustments. Our results highlight the need for more targeted Arctic surveys over different spatial and temporal scales to allow for a more systematic comparison and fusion of airborne, in-situ and remote sensing observations.

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

Inter-comparison of snow depth over sea ice from multiple methods

2020 ◽  
Author(s):  
Lu Zhou ◽  
Julienne Stroeve ◽  
Shiming Xu
Keyword(s):  
Sea Ice ◽  
Snow Depth ◽  
Ground Truth ◽  
Significant Trend ◽  
Data Sets ◽  
Snow Density ◽  
Spatial And Temporal Scales ◽  
Danish Meteorological Institute ◽  
The University

<p>In this study, we compare eight recently developed snow depth products that use satellite observations, modeling or a combination of satellite and modeling approaches. These products are further compared against various ground-truth observations, including those from ice mass balance buoys (IMBs), snow buoys, snow depth derived from NASA's Operation IceBridge (OIB) flights, as well as snow depth climatology from historical observations.</p><p>Large snow depth differences between data sets are observed over the Atlantic and Canadian Arctic sectors. Among the products evaluated, the University of Washington snow depth product (UW) produces the overall deepest Spring snow packs, while the snow product from the Danish Meteorological Institute (DMI) provide the shallowest Spring snow depths. There is no significant trend for mean snow depth among all snow products since the 2000s, however, those in regional varies larhely. Two products, SnowModel-LG and the NASA Eulerian Snow on Sea Ice Model: NESOSIM, also provide estimates of snow density. Arctic-wide, these density products show the expected seasonal evolution with varying inter-annual variability, and no significant trend since the 2000s. Compared to climatology, snow density from SnowModel-LG is generally denser, whereas that from NESOSIM is less. Both SnowModel-LG and NESOSIM densities have a larger seasonal change than climatology.</p><p>Inconsistencies in the reconstructed snow parameters among the products, as well as differences and with in-situ and airborne observations can in part be attributed to differences in effective footprint and spatial/temporal coverage, as well as insufficient observations for validation/bias adjustments. Our results highlight the need for more targeted Arctic surveys over different spatial and temporal scales to allow for a more systematic comparison and fusion of airborne, in-situ and remote sensing observations.</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

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

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 Snow Depth and Snow Density at Resolute, Northwest Territories

1960 ◽  
Vol 3 (28) ◽  
pp. 733-738 ◽  
Author(s):  
Richmond W. Longley
Keyword(s):  
Snow Cover ◽  
Water Content ◽  
Northwest Territories ◽  
Snow Depth ◽  
Research Council ◽  
National Research Council ◽  
Snow Surface ◽  
Snow Density ◽  
Strong Winds

Abstract Observations of the snow depth at 21 sites at Resolute were made twice weekly during the winter of 1957–58. As a result of these observations, and of other observations on snow made for the National Research Council, it is shown that the snow depth and the water content of the snow did not continue to increase during the winter as the snow fell. Rather the strong winds eroded the snow surface and the increase in depth was irregular and relatively slow. Furthermore, the observations on the density of the snow cover lead to the conclusion that attempts to measure the density in similar regions with an accuracy greater than ± 0.05 g. cm.−3 are not warranted.

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 Snow Depth and Snow Density at Resolute, Northwest Territories

1960 ◽  
Vol 3 (28) ◽  
pp. 733-738
Author(s):  
Richmond W. Longley
Keyword(s):  
Snow Cover ◽  
Water Content ◽  
Northwest Territories ◽  
Snow Depth ◽  
Research Council ◽  
National Research Council ◽  
Snow Surface ◽  
Snow Density ◽  
Strong Winds

AbstractObservations of the snow depth at 21 sites at Resolute were made twice weekly during the winter of 1957–58. As a result of these observations, and of other observations on snow made for the National Research Council, it is shown that the snow depth and the water content of the snow did not continue to increase during the winter as the snow fell. Rather the strong winds eroded the snow surface and the increase in depth was irregular and relatively slow. Furthermore, the observations on the density of the snow cover lead to the conclusion that attempts to measure the density in similar regions with an accuracy greater than ± 0.05 g. cm.−3 are not warranted.

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