Snow Pit Research Articles

Exploring microplastic distribution in Western North American snow

Microplastic (MP) transport in the atmosphere, one of the least studied environmental compartments because of the relatively small size of air-borne MPs and the challenges in identifying them, may be inferred from their occurrence in snowfall. In this study, 11 sites across western coastal North America were sampled and analyzed for MP presence in fresh snowfall, months-old summer surface snow, and stratified deposits in snow pits. MPs were detected and characterized using a method integrating linear array µ-Fourier Transform Spectroscopy (µFTIR) and batch spectral analysis with open-source platform Open Specy. Recovery rate analysis from sample filtration to data analysis was conducted, and analysis of field or laboratory blanks suggested negligible contamination (≤ 1 polyamide fragment per blank). Concentrations of MPs in the fresh snowfall of remote sites and those proximal to sources were 5.1–150.8 p/L and 104.5–325 p/L of snowmelt water, respectively. Summer surface snow that was several months old had MP concentrations ranging from 57.5–539 p/L of meltwater, and snow sampled at different depths within a snowpack had concentrations ranging from 35–914 p/L. Our results demonstrate a streamlined method that may be used for measuring MPs in remote or pristine environments, contributing to a better understanding of long-range MP transport.

Provenance of mineral dust deposited on Antarctica over the last sixty years by strontium isotopic analysis of snow from Dome C

The strontium (Sr) concentration and 87Sr/86Sr isotope ratio have been determined in 132 snow pit samples collected at Dome C, on the East Antarctic Plateau, corresponding to the period 1958–2019, and in 12 surface snow samples collected at the same site in 2016–2017. The average Sr concentration was 8.1 ± 5.7 pg g−1 (mean ± SD) in snow pit samples and 10.1 ± 8.0 pg g−1 (mean ± SD) in surface snow, without any significant temporal trend. The 87Sr/86Sr isotope ratio showed small variations (<0.15%), with an average 87Sr/86Sr value of 0.7094 ± 0.0010 (mean ± SD) in snow pit and 0.7103 ± 0.0004 (mean ± SD) in surface snow samples. These results seem to suggest that no change in the source(s) of mineral dust has occurred throughout the period investigated.Comparison of the 87Sr/86Sr isotope ratio of the snow samples with those of potential source areas suggests that the mineral dust reaching the East Antarctic plateau over the last decades is of a mixed origin, with a contribution from Patagonia and one or more other source(s) having a more radiogenic Sr isotopic signature, with a major fraction coming from South Australia.

Sublimation of Snow

Abstract Snow is a vital part of water resources, and sublimation may remove 10%–90% of snowfall from the system. To improve our understanding of the physics that govern sublimation rates, as well as how those rates might change with the climate, we deployed an array of four towers with over 100 instruments from NCAR’s Integrated Surface Flux System from November 2022 to June 2023 in the East River watershed, Colorado, in conjunction with the U.S. Department of Energy’s Surface Atmosphere Integrated Field Laboratory (SAIL) and the National Oceanic and Atmospheric Administration (NOAA)’s Study of Precipitation, the Lower Atmosphere and Surface for Hydrometeorology (SPLASH) campaigns. Mass balance observations, snow pits, particle flux sensors, and terrestrial lidar scans of the evolving snowfield demonstrated how blowing snow influences sublimation rates, which we quantified with latent heat fluxes measured by eddy-covariance systems at heights 1–20 m above the snow surface. Detailed temperature profiles at finer resolutions highlighted the role of the stable boundary layer. Four-stream radiometers indicated the important role of changing albedo in the energy balance and its relationship to water vapor losses. Collectively, these observations span scales from seconds to seasons, from boundary layer turbulence to valley circulation to mesoscale meteorology. We describe the field campaign, highlights in the observations, and outreach and education products we are creating to facilitate cross-disciplinary dialogue and convey relevant findings to those seeking to better understand Colorado River snow and streamflow.

Uncovering sources, distribution, and seasonal patterns of trace element deposition: the elemental puzzle of the western Himalayas.

The transport and deposition of atmospheric pollutants in the Himalayas have a adverse impact on the climate, cryosphere, ecosystem, and monsoon patterns. Unfortunately, there is a insufficiency of data on trace element concentrations and behaviors in the high-altitude Himalayan region, leading to limited research in this area. This study presents a comprehensive and detailed comprehension of trace element deposition, its spatial distribution, seasonal variations, and anthropogenic signals in the high-altitude Kashmir region of the Western Himalayas. Our investigation involved the analysis of 10 trace elements (Al, Cr, Mn, Fe, Co, Ni, Cu, Zn, Cd, and Pb) in glacier ice, snow pits, surface snow, and rainwater collected at various sites including Kolahoi, Thajwas, Pahalgam (Greater Himalayan ranges), and Kongdori and Shopian (Pir Panjal Ranges) during 2021.The study reveals distinct ranges of concentrations for the trace elements at different sampling sites. Our analysis of trace element concentration depth profiles in snow pits reveals seasonal fluctuations during the deposition year. The highest concentrations were found in the autumn(below 20cm) and summer (top layer), compared to the winter concentration (10-20cm). The high enrichment factors (EFs) suggest the severity of human-induced trace metal deposition in the western Himalayan region, relative to surrounding regions. Surprisingly, the concentrations and EFs of trace elements showed seasonal contradictions, with lower concentration values and higher EFs during the non-monsoon season and vice versa. A source apportionment analysis using the positive matrix factorization (PMF) technique identified five sources of trace element deposition in the region, including crustal sources (32.33%), coal combustion (15.62%), biomass burning (17.63%), traffic emission (18.8%), and industrial sources (15.6%). Additionally, the study incorporated backward trajectories coupled with δ18O using the NOAA HYSPLIT model to estimate moisture sources in the region, which suggests atmospheric pollutants predominately deposited from the large-scale atmospheric circulation from westerlies (75%) during non-monsoon season. These findings underscore the urgent need for enhanced monitoring and research efforts in the future.

Elevation bias due to penetration of spaceborne radar signal on Grosser Aletschgletscher, Switzerland

Abstract Digital elevation models (DEMs) from the spaceborne interferometric radar mission TanDEM-X hold a large potential for glacier change assessments. However, a bias is potentially introduced through the penetration of the X-band signal into snow and firn. To improve our understanding of radar penetration on glaciers, we compare DEMs derived from the almost synchronous acquisition of TanDEM-X and Pléiades optical stereo-images of Grosser Aletschgletscher in March 2021. We found that the elevation bias – averaged per elevation bin – can reach up to 4–8 m in the accumulation area, depending on post co-registration corrections. Concurrent in situ measurements (ground-penetrating radar, snow cores, snow pits) reveal that the signal is not obstructed by the last summer horizon but reaches into perennial firn. Because of volume scattering, the TanDEM-X surface is determined by the scattering phase centre and does not coincide with a specific firn layer. We show that the bias corresponds to more than half of the decadal ice loss rate. To minimize the radar penetration bias, we recommend to select DEMs from the same time of the year and over long observation periods. A correction of the radar penetration bias is recommended, especially when combining optical and TanDEM-X DEMs.

Soot-on-snow experiment: artificial deposition of light-absorbing particles onto snow surfaces in 2018

The absorption of shortwave irradiance in snow depends on the physical properties of snow (e.g., snow grain size and shape, liquid water content, etc.) and light-absorbing particles (LAP). Originating from natural and anthropogenic sources, LAP has been reported to accelerate snowmelt significantly in different regions globally. Yet, our process-level understanding of LAP after deposition onto snow remains rather limited. Here we investigate the impacts of artificial deposition of different LAP onto snow surfaces in an outdoor environment of northern Finland. Following LAP dry deposition into a custom-made tent standing on top of the snowpack, the albedo was followed along with the properties of snow in snow pit measurements throughout the spring season. The results showed that the albedo decay at the end of the season for the different spots were linked to the initial amount and type of LAP that were deposited onto the snowpack. Measured snow temperature profiles from LAP doped snow versus natural reference snow illustrated that the LAP affected snow had higher temperatures in the subsurface snow layers. Collected snow samples analyzed for size distribution of soot particles revealed no apparent agglomeration of soot particles during thaw-freezing events taking place during the experiment. Despite the relatively large perturbation of the experimentally deposited LAP, their impact on the season length was only up to 3 days. Additional experiments are, nevertheless, needed to better constrain the effects of LAP on snow albedo, melt rate, and other associated processes.

Bayesian physical–statistical retrieval of snow water equivalent and snow depth from X- and Ku-band synthetic aperture radar – demonstration using airborne SnowSAr in SnowEx'17

Abstract. A physical–statistical framework to estimate snow water equivalent (SWE) and snow depth from synthetic aperture radar (SAR) measurements is presented and applied to four SnowSAR flight-line data sets collected during the SnowEx'2017 field campaign in Grand Mesa, Colorado, USA. The physical (radar) model is used to describe the relationship between snowpack conditions and volume backscatter. The statistical model is a Bayesian inference model that seeks to estimate the joint probability distribution of volume backscatter measurements, snow density and snow depth, and physical model parameters. Prior distributions are derived from multilayer snow hydrology predictions driven by downscaled numerical weather prediction (NWP) forecasts. To reduce the signal-to-noise ratio, SnowSAR measurements at 1 m resolution were upscaled by simple averaging to 30 and 90 m resolution. To reduce the number of physical parameters, the multilayer snowpack is transformed for Bayesian inference into an equivalent one- or two-layer snowpack with the same snow mass and volume backscatter. Successful retrievals meeting NASEM (2018) science requirements are defined by absolute convergence backscatter errors ≤1.2 dB and local SnowSAR incidence angles between 30 and 45∘ for X- and Ku-band VV-pol backscatter measurements and were achieved for 75 % to 87 % of all grassland pixels with SWE up to 0.7 m and snow depth up to 2 m. SWE retrievals compare well with snow pit observations, showing strong skill in deep snow with average absolute SWE residuals of 5 %–7 % (15 %–18 %) for the two-layer (one-layer) retrieval algorithm. Furthermore, the spatial distributions of snow depth retrievals vis-à-vis lidar estimates have Bhattacharya coefficients above 94 % (90 %) for homogeneous grassland pixels at 30 m (90 m resolution), and values up to 76 % in mixed forest and grassland areas, indicating that the retrievals closely capture snowpack spatial variability. Because NWP forecasts are available everywhere, the proposed approach could be applied to SWE and snow depth retrievals from a dedicated global snow mission.

Snow water equivalent retrieval over Idaho – Part 2: Using L-band UAVSAR repeat-pass interferometry

Abstract. This study evaluates using interferometry on low-frequency synthetic aperture radar (SAR) images to monitor snow water equivalent (SWE) over seasonal and synoptic scales. We retrieved SWE changes from nine pairs of SAR images, mean 8 d temporal baseline, captured by an L-band aerial platform, NASA's Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR), over central Idaho as part of the NASA SnowEx 2020 and 2021 campaigns. The retrieved SWE changes were compared against coincident in situ measurements (SNOTEL and snow pits from the SnowEx field campaign) and to 100 m gridded SnowModel modeled SWE changes. The comparison of in situ to retrieved measurements shows a strong Pearson correlation (R=0.80) and low RMSE (0.1 m, n=64) for snow depth change and similar results for SWE change (RMSE = 0.04 m, R=0.52, n=57). The comparison between retrieved SWE changes to SnowModel SWE change also showed good correlation (R=0.60, RMSD = 0.023 m, n=3.2×106) and especially high correlation for a subset of pixels with no modeled melt and low tree coverage (R=0.72, RMSD = 0.013 m, n=6.5×104). Finally, we bin the retrievals for a variety of factors and show decreasing correlation between the modeled and retrieved values for lower elevations, higher incidence angles, higher tree percentages and heights, and greater cumulative melt. This study builds on previous interferometry work by using a full winter season time series of L-band SAR images over a large spatial extent to evaluate the accuracy of SWE change retrievals against both in situ and modeled results and the controlling factors of the retrieval accuracy.

Assessment of snow simulation using Noah-MP land surface model forced by various precipitation sources in the Central Tianshan Mountains, Central Asia

Accurate mountainous snow estimation is paramount for hydrological processes and water resources estimation in arid regions. Using Land surface models (LSMs) is a practical numerical approach for snow estimation in a complex orography region such as the Tianshan Mountains. However, the bias of precipitation forcing is a major source of uncertainty for snow simulation over scarce-data regions. This study evaluated the performance of 4 km snow simulations using the Noah-MP LSM driven by eight precipitation sources. They are retrieved from non-model products and climate model simulations during the 2018-2019 cold season in the Central Tianshan Mountains (CTS) region. Multi-source validation datasets (in-situ observation, field snow pits, and remote sensing products) have been used for evaluation and uncertainty estimation. The results showed that the difference in precipitation amount significantly affected the snowpack simulation performance. Compared with non-model products (Global Land Data Assimilation System (GLDAS), Global Precipitation Measurement (GPM), and Gauge-adjusted Global Satellite Mapping of Precipitation (GSMaP)), the cold season precipitation from climate model simulations exhibited a better performance overall in the high-elevation regions (elevation > 1000 m) evaluated by in-situ observations. In addition, the 4 km convection-permitting modeling (CPM) precipitation (WRF-Morrison and WRF-WSM6) showed higher accuracy (RMSE: 23.48 mm/season and 18.94 mm/season, respectively) than gray-zone resolution simulations and ERA5-land driven runs in the high-elevation regions. The snow depth simulation driven by WRF-Morrison precipitation had the second smallest RMSE (4.67 cm/day) and lowest bias (-0.74 cm/day) value in the high-elevation regions compared with in-situ observation. Meanwhile, CPM precipitation-driven runs exhibited the smallest RMSE value based on the assessment of snow field pits. In addition, the CPM-driven simulation achieved the closest match to the elevation-based distribution of snow cover days in regions with elevation over 1000 m and duration over 60 days compared to the MODIS product. The findings of this study highlight that CPM with proper parameterization configurations has added values in producing realistic topographic precipitation for snow modeling using LSMs over data-scarcity mountainous areas.

Spatial Estimation of Snow Water Equivalent for Glaciers and Seasonal Snow in Iceland Using Remote Sensing Snow Cover and Albedo

Efficient water resource management in glacier- and snow-dominated basins requires accurate estimates of the snow water equivalent (SWE) in late winter and spring and melt onset timing and intensity. To understand the high spatio-temporal variability of snow and glacier ablation, a spatially distributed energy balance model combining satellite-based retrievals of albedo and snow cover was applied. Incoming short-wave energy, contributing to daily estimates of melt energy, was constrained by remotely sensed surface albedo for snow-covered surfaces. Fractional snow cover was used for non-glaciated areas, as it provides estimates of snow cover for each pixel to better constrain snow melt. Thus, available daily estimates of melt energy in a given area were the product of the possible melt energy and the fractional snow cover of the area or pixel for non-glaciated areas. This provided daily estimates of melt water to determine seasonal snow and glacier ablation in Iceland for the period 2000–2019. Observations from snow pits on land and glacier summer mass balance were used for evaluation, and observations from land and glacier-based automatic weather stations were used to evaluate model inputs for the energy balance model. The results show that the interannual SWE variability was generally high both for seasonal snow and glaciers. For seasonal snow, the largest SWE (&gt;1000 mm) was found in mountainous and alpine areas close to the coast, notably in the East- and Westfjords, Tröllaskaga, and in the vicinity of glacier margins. Lower SWE values were observed in the central highlands, flatter inland areas, and at lower elevations. For glaciers, more SWE (glacier ablation) was associated with lower glacier elevations while less melt was observed at higher elevations. For the impurity-rich bare-ice areas that are exposed annually, observed SWE was more than 3000 mm.

Microbial ecology and activity of snow algae within a Pacific Northwest snowpack

ABSTRACT Snow algae blooms are common occurrences in alpine systems and contribute to increasing snow and glacial ice melt rates. Despite the cosmopolitan distribution of snow algae, little is known about the role their life cycle plays in community composition and activity in snowpack. Mount Baker, in Washington, USA, is a rapidly changing alpine ecosystem. Here we present estimates of cellular biomass, microbial community composition, and primary productivity measurements associated with surficial snow algae blooms and a vertical profile within a snow pit to characterize the microbial ecology of this snowpack system. Chloromonas was the most abundant algae detected in our samples. Sphingobacteriaceae were the most dominant bacteria in samples where fungi were the most abundant eukaryote, whereas Chitinophagaceae dominated when Chlorophyceae were the most abundant eukaryote. In a snow pit vertical profile, we observed more algae in the green-pigmented, flagellated life cycle stage at depth (~30 cm below the surface) than at the surface where the astaxanthin-rich, aplanospore life cycle stage dominated. Higher resolution analyses revealed that Chloromonas-associated photosynthesis transcripts were more abundant at depth than at the surface of the snowpack. These results suggest that there is a photosynthetic niche within alpine snowpack that has unknown effects on the carbon budget and provides potentially undetected primary productivity resulting in a “cryptic” photosynthetic system.

Ground-penetrating radar as a tool for determining the interface between temperate and cold ice, and snow depth: a case study for Hurd-Johnsons glaciers, Livingston Island, Antarctica

Abstract We analyze the internal structure of two polythermal glaciers, Hurd and Johnsons, located on Livingston Island, Antarctica, using 200 and 750 MHz GPR data collected in 2003/04, 2008/09 and 2016/17 field campaigns. Based on the different permittivities of snow and ice, we determined the thickness distribution of the end-of winter snow cover and of the cold ice layer. Their knowledge is fundamental for mass balance and glacier dynamics studies due to the different densities and rheological properties of such media. The average measured thicknesses for the snow and cold ice layers (the latter including the snow layer) were of 1.44 ± 0.09 and 29.1 ± 1.5 m, and their corresponding maxima were of 2.45 ± 0.21 and 80.8 ± 2.5 m. GPR snow profiling allowed for extension of the coverage of the snow thickness survey, but added little information to that supplied by snow pits, stake readings and manual snow probing, because of the multiplicity of reflections within the seasonal snowpack caused by internal ice layers and lenses. The polythermal structure determined for Hurd Glacier fits into the so-called Scandinavian type, seldom reported for the Antarctic region.

Development of a handheld integrating sphere snow grain sizer (HISSGraS)

Abstract We developed a Handheld Integrating Sphere Snow Grain Sizer (HISSGraS) for field use to measure the specific surface area (SSA) of snow. In addition to snow samples, HISSGraS can directly measure snow surfaces and snow pit walls. The basic measurement principle is the same as that of the IceCube SSA instrument. The retrieval algorithm for SSA from reflectance employs two conversion equations formulated using spherical and nonspherical grain shape models. We observed SSAs using HISSGraS, IceCube and the gas adsorption method in a snowfield in Hokkaido, Japan. Intercomparison of the results confirmed that with HISSGraS direct measurement, SSA profile observations can be completed in just ~1/10 the time required for measurement of snow samples. Our results also suggest that HISSGraS and IceCube have similar accuracy when the same snow samples are measured using the same grain shape model. However, SSAs of near-surface snow layers measured using the three techniques exhibited some biases, possibly due to rapid snow metamorphism or melting during measurement and some technical issues with optical techniques. When excluding SSA data for the surface layer, which metamorphosed remarkably during measurement, IceCube- and HISSGraS-derived SSAs correlated strongly with those obtained by gas adsorption and HISSGraS accuracy is 21–34%.

Monitoring the annual geodetic mass balance of Bordu and Sary-Tor glaciers using UAV data

Abstract The geodetic mass balance of a glacier corresponds to glacier-wide volume changes, converted to mass changes using density assumptions. It is typically calculated by differencing multi-temporal digital elevation models. In this study, we show how the annual geodetic mass balance of a glacier can be derived from uncrewed aerial vehicle (UAV) data. The presented workflow is applied to two small- to medium-sized glaciers in the Kyrgyz Tien Shan (Central Asia): Bordu glacier and Sary-Tor glacier. The obtained geodetic mass balance is compared with the glaciological mass balance derived from a network of ablation stakes and snow pits. A previously calibrated mass-balance model is used to correct for the difference in acquisition dates. The results show that the determined geodetic mass balance matches closely with the glaciological mass balance. Besides, for both glaciers the geodetic mass balance does not seem to be particularly sensitive to the assumptions regarding volume-to-mass conversion. Therefore, our results demonstrate that UAVs can serve as a valuable instrument to quantify the annual geodetic mass balance and to validate the glaciological mass balance. The conventional glaciological mass-balance estimation often relies on interpolation and extrapolation methods, whereas UAVs offer the potential for direct data acquisition over the entire glacier surface.

Recent decline in atmospheric Pb deposition and isotopic constraints on changes in source contributions in snow from northwestern Greenland

We present lead (Pb) concentrations and isotope ratios in a continuous series of 38 snow samples from a 1.9-m snow pit, covering the period from winter 2012 to summer 2017, at the East Greenland Ice-core Project (EGRIP) ice core drill site in northwestern Greenland. Pb concentrations were highly variable, ranging from 1.53 to 94.9 pg g−1 (mean value of 10.6 pg g−1), with higher concentrations during winter and spring and lower concentrations during summer and fall. Our results show a substantial reduction in the Pb concentration of ∼50% between the 2000s and 2010s, reaching a level close to that observed in the mid-18th century, that is, the time of the Industrial Revolution. Remarkably low radiogenic Pb isotope compositions were observed in our samples compared to previously reported values during the 2000s. The Pb isotope mixing model results indicated a decreasing Chinese contribution from the 2000s onwards, while Europe/Russia emerged as a relatively more important contributor to the anthropogenic Pb input to central Greenland during the corresponding period. Thus, we hypothesized that the reduction in Pb pollution in central Greenland is largely due to a decreasing contribution from Chinese sources in response to the effectiveness of stringent emission control measures in China.

Dust source tracing and their transport mechanisms before monsoon season from three glaciers at the Tibetan Plateau: revelations from REE analysis and an indirect tracing approach.

Understanding the origins of Tibetan Plateau (TP) glacier dust is vital for glacier dynamics and regional climate understanding. In May 2016, snow pit samples were collected from glaciers on the TP: Qiyi (QY) in the north, Yuzhufeng (YZF) in the center, and Xiaodongkemadi (XDK) in the south. Rare earth element (REE) concentrations were analyzed using inductively coupled plasma mass spectrometry (ICP-MS), and near-surface PM10 concentrations were extracted from a dataset of Chinese near-surface PM10. Two tracing approaches were used: direct REE tracing and an indirect approach combining potential source contribution function (PSCF) and concentration-weighted trajectory (CWT). Both methods yielded consistent results. Pre-monsoon, TP surface soils, Taklimakan Desert, and Qaidam Basin contributed to glacier dust. Notably, central and southern glaciers showed Thar Desert influence, unlike the northern ones. Taklimakan and Thar Deserts were major contributors due to their substantial contribution and high dust concentration. Taklimakan dust, influenced by terrain and westerly winds, affected central and southern glaciers more than northern ones. Westerlies carried Thar Desert dust to the TP after it was uplifted by updrafts in northwest India, significantly affecting southern glaciers. Furthermore, comparing the two tracer methods, the indirect approach combining PSCF and CWT proved more effective for short-term dust source tracing.

Automatic snow type classification of snow micropenetrometer profiles with machine learning algorithms

Abstract. Snow-layer segmentation and classification are essential diagnostic tasks for various cryospheric applications. The SnowMicroPen (SMP) measures the snowpack's penetration force at submillimeter intervals in snow depth. The resulting depth–force profile can be parameterized for density and specific surface area. However, no information on traditional snow types is currently extracted automatically. The labeling of snow types is a time-intensive task that requires practice and becomes infeasible for large datasets. Previous work showed that automated segmentation and classification is, in theory, possible but cannot be applied to data straight from the field or needs additional time-costly information, such as from classified snow pits. We evaluate how well machine learning models can automatically segment and classify SMP profiles to address this gap. We trained 14 models, among them semi-supervised models and artificial neural networks (ANNs), on the MOSAiC SMP dataset, an extensive collection of snow profiles on Arctic sea ice. SMP profiles can be successfully segmented and classified into snow classes based solely on the SMP's signal. The model comparison provided in this study enables SMP users to choose a suitable model for their task and dataset. The findings presented will facilitate and accelerate snow type identification through SMP profiles. Anyone can access the tools and models needed to automate snow type identification via the software repository “snowdragon”. Overall, snowdragon creates a link between traditional snow classification and high-resolution force–depth profiles. Traditional snow profile observations can be compared to SMP profiles with such a tool.

Monitoring snow water equivalent using the phase of RFID signals

Abstract. The amount of water contained in a snowpack, known as snow water equivalent (SWE), is used to anticipate the amount of snowmelt that could supply hydroelectric power plants, fill water reservoirs, or sometimes cause flooding. This work introduces a wireless, non-destructive method for monitoring the SWE of a dry snowpack. The system is based on an array of low-cost passive radiofrequency identification (RFID) tags, placed under the snow and read at 865–868 MHz by a reader located above the snow. The SWE was deduced from the phase delay of the tag's backscattered response, which increases with the amount of snow traversed by the radiofrequency wave. Measurements taken in the laboratory, during snowfall events and over 4.5 months at the Col de Porte test field, were consistent with reference measurements of cosmic rays, precipitation and snow pits. SWE accuracy was ±18 kg m−2 throughout the season (averaged over three tags) and ±3 kg m−2 during dry snowfall events (averaged over data from two antennas and four or five tags). The overall uncertainty compared to snow weighing was ±10 % for snow density in the range 61–390 kg m−3. The main limitations observed were measurement bias caused by wet snow (biased data were discarded) and the need for phase unwrapping. The method has a number of advantages: it allows for continuous measurement (1 min sampling rate in dry snow), it can provide complementary measurement of tag temperature, it does not require the reception of external data, and it opens the way towards spatialized measurements. The results presented also demonstrate that RFID propagation-based sensing can remotely monitor the permittivity of a low-loss dielectric material with scientific-level accuracy.

Pollution revealed by stable lead isotopes in recent snow from the northern and central Tibetan Plateau

Lead (Pb) isotopes are less fractionated than those from different sources, and thus were used to trace the sources of Pb in the environment. To investigate the sources of Pb in the atmosphere of the Tibetan Plateau, stable Pb isotopes (206Pb, 207Pb and 208Pb) in acidified snow pit samples collected from five glaciers (i.e., Qiyi-QY, Meikuang-MK, Yuzhufeng-YZF, Hariqin-HRQ and Xiaodongkemadi-XDKMD) in May 2016 of the northern and central Tibetan Plateau were measured. The results showed narrow ranges of 1.158–1.187 for 206Pb/207Pb and 2.450–2.489 for 208Pb/207Pb respectively. The 206Pb/207Pb ratios in all samples were obviously lower than the environmental background value of 1.196, indicating the primary contributions of anthropogenic sources. At least 60% of Pb was contributed by various human activities, which was supported by the Pb isotopes in the snow pit samples from the QY, MK, YZF, HRQ and XDKMD glaciers. By comparing Pb isotope data, we found that the primary anthropogenic sources are coal combustion, mining and smelting activities in northwestern China and mixed emissions from cities located in western China and close to the glaciers. These sources contributed to the Pb in the northern glaciers (QY and MK) in particular. Coal combustion in India probably contributes to the central glaciers (HRQ and XDKMD). Another potential source could be parts of central Asia (e.g., Kyrgyzstan and Uzbekistan) through long range transport. The above potential source areas of contaminants were traced further by the air mass back-trajectory tracing method.

Accuracy of Manual Snow Sampling, Depending on the Sampler’s Cross-Section—A Comparative Study

Snow sampling, either by inserting a tube through the entire snowpack or by taking samples from the vertical profile, is widely applied to measure the snow depth, density, and snow water equivalent (SWE). A comparative study of snow-sampling methods was carried out on 24 March 2022 in Sodankylä, Finland. Six groups from five countries (Estonia, Finland, Greenland, Iceland, and Sweden) participated, using 12 different snow samplers, including 9 bulk tube samplers and 3 density cutters. The cross-sectional area of the SWE samplers varied from 11 to 100 cm2, while tube length varied from 30 cm to 100 cm. The cross-sectional area of the density profile cutters varied from 100 cm2 to 200 cm2 and the vertical sampling step varied from 5 cm to 10 cm. The samples were taken from snow pits in 55–65-centimeter-deep snow cover in a flat area with sparse pine trees, with the pits at a maximum distance of 10 m from each other. Each tube sampling series consisted of 3–10 vertical columns to ensure statistical validation. The snowpack was relatively soft, with two moderately hard crust layers. The density recorded in the tube sample measurements varied from 218 to 265 kgm−3. The measurement results of SWE, however, varied depending on the sampling equipment used, ranging from 148 to 180 kgm−2, with two outliers of 77 and 106 kgm−2, both with 11 cm2 samplers.