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This article presents a short-range, bistatic radar system for in situ , long-term snow monitoring. The proposed system is capable of autonomously capturing time-lapsed, 2-D images of the snow cover. In this article, we report on system concept and initial results for average snow-water-equivalent (SWE) assessment. Average SWE of dry snow is estimated based on two-way-travel time, bistatic radar measurements and compared to observed snow pit data as well as monostatic radar estimates. Although current empirical results are limited, measurements are indicating good performance. The bistatic radar instrument is capable of 2-D imaging and wet snow assessment, but empirical validation is required.

International audience; The presence of a snowpack, which may last up to 9 months in the Arctic, can provide insulation from the cold winter temperature for small mammals living beneath it, such as lemmings. Since lemmings have to move through the snowpack during that period, it is important to better understand how the physical properties of snow affect the way they dig tunnels. Here, we tested 1) whether lemmings systematically dig in the snowpack at the ground level where they can find their food plants, and 2) whether they choose the softest snow layer in which to dig, which is usually the depth hoar layer in the arctic snowpack. We found 33 lemming tunnels in 2017 and 2018 by digging through the snow at the sites of arctic fox attacks on lemmings. Contrary to our expectation, almost all the tunnels (32/33) were found to be higher than ground level, probably because of the presence of obstacles (i.e., melt-freeze crusts or hummocks) at the base of the snowpack. As predicted, all tunnels were dug in the soft depth hoar layer, which had a lower density than snow layers below and above it. Lemmings also showed a preference to dig their tunnels at the top of the depth hoar, just below a hard snow layer. Systematically digging their tunnels in the lowest-density snow layer, regardless of its height in the snow pack, could be a strategy for lemmings to minimize energy expenditure, which could improve their survival and chances of reproducing in winter. La présence d'un manteau neigeux, qui peut durer jusqu'à 9 mois en Arctique, assure une protection contre le froid pour certains petits mammifères qui s'y abritent pendant l'hiver, tels les lemmings. Cette neige est également susceptible d'affecter leurs mouvements, mais l'endroit exact où les lemmings creusent dans la neige pour se déplacer demeure peu connu. Nous avons donc testé 1) si les lemmings creusent toujours au niveau du sol, près de la végétation dont ils se nourrissent, et 2) s'ils choisissent la couche la plus molle pour creuser, généralement le givre de profondeur dans le manteau neigeux arctique. Nous avons trouvé 33 tunnels de lemmings en 2017 et 2018 en creusant dans la neige à des sites d'attaques de renards sur des lemmings. Nos résultats contredisent notre première hypothèse car presque tous les tunnels étaient plus haut que le niveau du sol (32/33), probablement en raison de la présence d'obstacles (c.-à-d. couches de fonte-regel et hummocks) dans les couches basales du manteau neigeux. En revanche, nos résultats supportent notre deuxième hypothèse puisque les tunnels étaient tous creusés dans le givre de profondeur qui avait une densité plus faible que les couches supérieures et inférieures. Les tunnels de lemmings étaient aussi souvent creusés à la limite supérieure du givre de profondeur, tout juste sous une couche plus dure. Creuser dans la couche de neige la moins dense pourrait être une stratégie des lemmings pour minimiser leurs dépenses énergétiques, ce qui pourrait augmenter leurs chances de survie et de reproduction en hiver.

Snow avalanche can be triggered by different mechanisms including metrological conditions, snow pack stability together with external factor such as seismic tremor and explosions. The snow avalanche triggered by seismic event is very important hazard phenomena in the snow covered region. In the present paper, investigation of earthquake-induced snow avalanches is introduced in Nubra–Shyok region of Western Himalaya, India. Compilation of seismogenic snow avalanche and earthquakes occurred in the Nubra–Shyok region during the period of 2010–2012 is made, which reveals that out of 393 natural avalanches, 81 avalanches was triggered due to the earthquake during this period. The local earthquakes occurred in Nubra–Shyok region, recorded by a local seismic network, are utilized for this work. The same date of occurrence of earthquakes and snow avalanches confirm seismogenic snow avalanche in this region. In the present work, avalanches triggered due to natural seismicity during the period of 2010–2012 related with earthquakes of magnitude 1.7 ≤ Mw ≤ 4.4 and distance of induced snow avalanche from epicenter of earthquakes, i.e., 4–92 km. In this study, lower bound limits of earthquake magnitudes, which cause avalanches, are established up to the distance of 92 km. Relation between earthquake magnitude and distance of induced snow avalanche from epicenter reveals that an earthquake of magnitude 1.4 (Mw) can trigger a snow avalanche as distance approaches to zero from earthquake epicenter. The comparison of obtained relation with other similar types of studies, i.e., Keefer (Geol Soc Am Bull 95:406–421, 1984), Podolskiy et al. (J Glaciol 56(197):431–446, 2010a) confirms the reliability of the present work.

International audience; Snow packs cover large expanses of Earth’s land surface, making them integral components of the cryosphere in terms of past climate and atmospheric proxies, surface albedo regulators, insulators for other Arctic environments and habitats for diverse microbial communities such as algae, bacteria and fungi. Yet, most of our current understanding of snow pack environments, specifically microbial activity and community interaction, is limited to the main microbial growing season during spring ablation. At present, little is known about microbial activity and its influence on nutrient cycling during the subfreezing temperatures and 24-h darkness of the polar winter. Here, we examined microbial dynamics in a simulated cold (−5°C), dark snow pack to determine polar winter season microbial activity and its dependence on critical nutrients. Snow collected from Ny-Ålesund, Svalbard was incubated in the dark over a 5-week period with four different nutrient additions, including glacial mineral particles, dissolved inorganic nitrogen (DIN), dissolved inorganic phosphorus (DIP) and a combined treatment of DIN plus DIP. Data indicate a consumption of dissolved inorganic nutrients, particularly DIN, by heterotrophic communities, suggesting a potential nitrogen limitation, contradictory to phosphorus limitations found in most aquatic environments. 16S amplicon sequencing also reveal a clear difference in microbial community composition in the particulate mineral treatment compared to dissolved nutrient treatments and controls, suggesting that certain species of heterotrophs living within the snow pack are more likely to associate with particulates. Particulate phosphorus analyses indicate a potential ability of heterotrophic communities to access particulate sources of phosphorous, possibly explaining the lack of phosphorus limitation. These findings have importance for understanding microbial activity during the polar winter season and its potential influences on the abundance and bioavailability of nutrients released to surface ice and downstream environments during the ablation season.

Seasonal snow cover is a crucial resource for hydropower in Norway. Understanding water sources and processes related to inter-annual snow cover variability is therefore of fundamental societal relevance. The stable water isotope composition of precipitation provides a natural, integrated tracer of the condensation history during atmospheric water transport. The main parameters dD and d18O along with the secondary quantity d-excess give information about the origin and transport history of moisture from its source to its sink. When snow falls and deposits on the ground as a sediment, it creates a record in the form of the seasonal snow pack.Here we utilize data acquired during a field campaign in the winter season of 2018-2019 at the Finse Alpine Research Station Center (1222m, 60.6N, 7.5E) in Norway, in order to investigate the transfer of the isotopic signal of source and transport conditions from vapour to snowfall, and to the snow pack.Over a main period of two months, snowfall was sampled daily, while the water vapour was continuously measured from ambient air guided through a heated inlet to a Picarro L2130i infrared spectrometer, with daily calibration runs. During five periods with intense snowfall, we carried out higher frequency sampling down to 15 minute intervals. Covering the entire winter season, five snowpits were sampled for isotopic analysis as well as detailed stratigraphy. In total more than 400 snow samples where taken and analysed for their isotopic composition, accompanied by routine meteorological observations over the winter season at the site. In addition, we compare the variations in the observed isotope signal at Finse with one derived from moisture source analysis using the Lagrangian diagnostic WaterSip, based on the FLEXPART model and ERA Interim reanalysis data.To investigate to what degree moisture source information is archived in the snow pack, and how it evolves during the season, we compare snow observations at different time resolution (daily and high frequency snowfall samples) with the record of the snow pack, aided by the snow model CROCUS. The meteorological observations supply context for understanding the snow formation conditions. In particular, deviations from isotopic equilibrium between vapour and precipitation at ambient temperature conditions provide insight into the dominant condensation regime during different intense observation periods.

The techniques and software presented in the article are to be used for taking into account the solid precipitation (in the form of snow deposits) effect on the seasonal changes in the Earth’s gravitational field. The combined gravitational effect of the snow cover is similar in value with the gravity measurements accuracy. Special program has been developed which makes it possible to assess the snow pack effect on the gravity changes. The map presented here shows gravitational field changes due to solid precipitation.

Here, we present the first detailed analysis of processes by which various current use pesticides (CUPs) and legacy organochlorine pesticides (OCPs) are concentrated in melt ponds that form on Arctic sea ice in the summer, when surface snow is melting and ice eventually breaks up. Four current use pesticides (dacthal, chlorpyrifos, trifiuralin, and pentachloronitrobenzene) and one legacy organochlorine pesticide (a-hexachlorocyclohexane) were detected in ponds in Resolute Passage, Canadian Arctic, in 2012. Melt-pond concentrations changed over time as a function of gas exchange, precipitation, and dilution with melting sea ice. Observed increases in melt-pond concentrations for all detected pesticides were associated with precipitation events. Dacthal reached the highest concentration of all current use pesticides in ponds (95 +/- 71 pg L-1), a value exceeding measured concentrations in the under-ice (0 m) and 5 m seawater by >10 and >16 times, respectively. Drainage of dacthal-enriched pond water to the ocean during ice break-up provides an important ice-mediated annual delivery route, adding-30% of inventory in the summer Mixed Layer (ML; 10 m) in the Resolute Passage, and a concentrating mechanism with potential implications for exposures to organisms such as ice algae, and phytoplankton. Crown Copyright (C) 2016 Published by Elsevier B.V. All rights reserved.

Snow avalanches in mountainous areas pose a significant threat to infrastructure (roads, railways, energy transmission corridors), personal property (homes) and recreational areas as well as for lives of people living and moving in alpine terrain. The impacts of snow avalanches range from delays and financial loss through road and railway closures, destruction of property and infrastructure, to loss of life. Avalanche warnings today are mainly based on meteorological information, snow pack information, field observations, historically recorded avalanche events as well as experience and expert knowledge. The ability to automatically identify snow avalanches using Very High Resolution (VHR) optical remote sensing imagery has the potential to assist in the development of accurate, spatially widespread, detailed maps of zones prone to avalanches as well as to build up data bases of past avalanche events in poorly accessible regions. This would provide decision makers with improved knowledge of the frequency and size distributions of avalanches in such areas. We used an object–oriented image interpretation approach, which employs segmentation and classification methodologies, to detect recent snow avalanche deposits within VHR panchromatic optical remote sensing imagery. This produces avalanche deposit maps, which can be integrated with other spatial mapping and terrain data. The object-oriented approach has been tested and validated against manually generated maps in which avalanches are visually recognized and digitized. The accuracy (both users and producers) are over 0.9 with errors of commission less than 0.05. Future research is directed to widespread testing of the algorithm on data generated by various sensors and improvement of the algorithm in high noise regions as well as the mapping of avalanche paths alongside their deposits.

Controls of stream water NO3 in mountainous and forested catchments are not thoroughly understood. Long-term trends in stream water NO3 are positive, neutral and negative, often apparently independent of trends in N deposition. Here, time series of NO3 in four small acid-sensitive catchments in southern Norway were analysed in order to identify likely drivers of long-term changes in NO3. In two sites, stream water NO3 export declined ca 50% over a period of 25 years while in the other sites NO3 export increased with roughly 20%. Discharge and N deposition alone were poor predictors of these trends. The most distinct trends in NO3 were found in winter and spring. Empirical models explained between 45% and 61% of the variation in weekly concentrations of NO3, and described both upward and downward seasonal trends tolerably well. Key explaining variables were snow depth, discharge, temperature and N deposition. All catchments showed reductions in snow depth and increases in winter discharge. In two inland catchments, located in moderate N deposition areas, these climatic changes appeared to drive the distinct decreases in winter and spring concentrations and fluxes of NO3. In a coast-near mountainous catchment in a low N deposition area, these climatic changes appeared to have the opposite effect, i.e. lead to increases in especially winter NO3. This suggests that the effect of a reduced snow pack may result in both decreased and increased catchment N leaching depending on interactions with N deposition, soil temperature regime and winter discharge.

This article provides an overview on active microwave methods for remote sensing of water equivalent (SWE). Technical concepts and applications for SWE sensing are presented based on the following principles: (1) measurement of backscatter intensity of the snow volume; (2) measurement of interferometric phase delay caused by signal propagation in the snow pack; and (3) measurement of reflecting layers to retrieve snow depth and infer SWE by means of radar altimetry.

On 19 January 2012, a large D3 avalanche (approximately 10 3 t) was recorded with an infrasound array ideally situated for observing the avalanche velocity. The avalanche crossed Highway 21 in Central Idaho during the largest avalanche cycle in the 15 years of recorded history and deposited approximately 8 m of snow on the roadway. Possible source locations along the avalanche path were estimated at 0.5 s intervals and were used to calculate the avalanche velocity during the 64 s event. Approximately 10 s prior to the main avalanche signal, a small infrasound signal originated from the direction of the start zone. We infer this to be the initial snow pack failure, a precursory signal to the impending avalanche. The avalanche accelerated to a maximum velocity of 35.9 ± 7.6 ms −1 within 30 s before impacting the highway. We present a new technique to obtain high spatial and temporal resolution velocity estimates not previously demonstrated with infrasound for avalanches and other mass wasting events.

Every year a large number of avalanches are triggered by anthropogenic activities. This study examines the occurrence of snow avalanches from 1940 to 2011 in the Fagaraș massif and Bâlea glacial area in the eastern region of the Southern Carpathian Mountains, Romania. We focused our attention on the relationship between anthropogenically triggered avalanches and terrain factors (including elevation, aspect and slope angle), snow depth and snow pack characteristics. We also examined the influence of issuing snow avalanche danger-level alerts to the public by the Bâlea Work Nivology Laboratory on the number of accidents since 2005. Most avalanche accidents occurred in the alpine and subalpine areas. The majority of avalanche fatalities occurred at high danger levels, while avalanche burials and injuries occurred at all danger levels (low–very high). Since the establishment of the National Administration of Meteorology and the Bâlea Work Nivology Laboratory, a marked reduction in accidents has occurred because of better education regarding avalanche hazards and the regular issuing of avalanche danger-level alerts to the public and authorities.

The current (2005–2011) status of the chemical composition of snow cover and rain collected at a height of 1.5 m above ground was studied within 11 km around the Severonikel industrial complex, one of the largest SO2 and metal contamination sources in N Europe. In spite of a significant decrease in emissions during the past 20 years, Ni and Cu concentrations in snow remain extremely high near the source (2500 and 1500 times background values, respectively). Although showing a five- to six-fold decrease in Ni and Cu concentrations since 1994, rain water currently still has concentrations 150 and 80 times background, respectively. Differences in the chemical composition of snow pack and rain collected at a height of 1.5 m above ground in this case are not caused by seasonal effects, but rather by the height of precipitation sampling relative to the ground.

© 2017 by the authors. Monitoring the snow pack is crucial for many stakeholders, whether for hydro-power optimization, water management or flood control. Traditional forecasting relies on regression methods, which often results in snow melt runoff predictions of low accuracy in non-average years. Existing ground-based real-time measurement systems do not cover enough physiographic variability and are mostly installed at low elevations. We present the hardware and software design of a state-of-the-art distributedWireless Sensor Network (WSN)-based autonomous measurement system with real-time remote data transmission that gathers data of snow depth, air temperature, air relative humidity, soil moisture, soil temperature, and solar radiation in physiographically representative locations. Elevation, aspect, slope and vegetation are used to select network locations, and distribute sensors throughout a given network location, since they govern snow pack variability at various scales. Three WSNs were installed in the Sierra Nevada of Northern California throughout the North Fork of the Feather River, upstream of the Oroville dam and multiple powerhouses along the river. The WSNs gathered hydrologic variables and network health statistics throughout the 2017 water year, one of northern Sierra’s wettest years on record. These networks leverage an ultra-low-power wireless technology to interconnect their components and offer recovery features, resilience to data loss due to weather and wildlife disturbances and real-time topological visualizations of the network health. Data show considerable spatial variability of snow depth, even within a 1 km2network location. Combined with existing systems, these WSNs can better detect precipitation timing and phase in, monitor sub-daily dynamics of infiltration and surface runoff during precipitation or snow melt, and inform hydro power managers about actual ablation and end-of-season date across the landscape.

Initial densities of deposited snows mainly depend on hydrometeors types of snowfalls. Some previous researchers qualitatively indicated their relations. However a quantitative relationship between snowfall densities and hydrometeors types had not been established because of difficulty in parameterizing varieties of types of hydrometeors in a snowfall event. Thus in a previous study, we introduced a new variable, CMF (Center of Mass Flux distribution), which described the hydrometeors mainly contributing to a snowfall. The CMF is a set of the averages of the size and the fall speed weighted by the mass flux estimated from all measured hydrometeors in a snowfall. It represents the predominant type of hydrometeors of the snowfall quantitatively. In this study, we examined the relationships between the densities of newly fallen snows and their predominant snow types as indicated by the CMFs. We measured the snowfall densities, simultaneously observing the size and the fall speed of the hydrometeors of the snowfalls and deduced the predominant hydrometeor types of each snowfall event from their CMFs. The measurements of snow densities were carried out for short periods, 1 or 2 h, during which the densification of the deposited snows was negligible. We also selected cases when snowfalls contained basically the same type of hydrometeors. As a result, we could obtain the relationships between the main snow types and the snowfall densities not only qualitatively reported by previous researchers but also the quantitative relationships between snowfall densities and the CMF-densities, which were a presumed density derived from the size and mass components of the CMFs. This suggests the possibility of estimating snowfall densities, which reflected snow types (the main types of hydrometeors), directly from the measured size and the fall speed data, and using them as the initial densities for a snow pack in a numerical model, even though difficult issues remain in parameterization for a plactical use. Our results might be applicable only to temperate snow at our observation site, Nagaoka, 37°N, where riming and aggregation are predominant. However the methodology in this study would be useful for other kinds of snow.

We quantify the effect of the snow-albedo feedback on Swiss spring temperature trends using daily temperature and snow depth measurements from six station pairs for the period 1961–2011. We show that the daily mean 2-m temperature of a spring day without snow cover is on average 0.4 °C warmer than one with snow cover at the same location. This estimate is comparable with estimates from climate modelling studies. Caused by the decreases in snow pack, the snow-albedo feedback amplifies observed temperature trends in spring. The influence is small and confined to areas around the upward-moving snow line in spring and early summer. For the 1961–2011 period, the related temperature trend increases are in the order of 3–7 % of the total observed trend.

The temperature of snow at 10 discrete vertical levels in the snow pack was measured using automatic temperature recorders at 10 min intervals following 5 snowfalls between January and March 2009 at the Tianshan Station for Snow Cover and Avalanche Research. The amplitude of the diurnal fluctuation in the temperature during the measurement run of 10 March 2009, when the snow was melting, was 1.85 times greater than during the measurement run of 15 February, when snow was accumulating. Analysis of the vertical temperature gradient for all five measurement runs shows that the temperature gradient of the snow was at a maximum value at the snow surface. The maximum snow temperature gradient was measured during the run of 21 January and was 4.46 times greater than during the run of 15 February. The temperature gradient was approximately zero 30 cm below the snow surface. Analysis of the characteristics of the snow temperature and the daily mean volumetric moisture content of the snow leads to the conclusion that the snow cover in the western Tianshan Mountains can be divided into stable, interim, and melting stages. A critical snow volumetric moisture content of 0.1% separates the stable and interim stages, while a volumetric moisture content of 0.3% separates the interim and melting stages.

A multilayered backscattering model for a lossy medium has been presented in this paper. This multilayered model has been used to calculate the total surface re∞ection coe-cients of a snow pack for both horizontal and vertical co-polarizations. The total surface re∞ection coe-cients include contributions from both surface and volumetric backscattering. The backscattering coe-cients calculated by this model were compared with in situ measurements on dry and wet snow. Results show that good agreements are obtained between the model and measurements for the co-polarization modes, especially for the snow with less liquid water content.

Summary This paper presents that a simple regional regression-based approach with readily available geographic and meteorological parameters as predictors could be a viable method for mapping snow water equivalent (SWE) climatology in the mountainous areas of the western United States. Such climatological information is potentially useful for several hydrologic applications, including estimation of real-time SWE grids and calibration and evaluation of SWE estimates obtained from remote sensing or through various snow models. Regional delineation for the mountainous regions of the western United States was done through cluster analysis in consideration of the characteristics of seasonal snow pack accumulation and ablation processes. Various geographic and meteorological parameters were further investigated through stepwise regression as potential predictors of monthly changes in climatological SWE in each delineated region. In-situ measurements of SWE, obtained from the Natural Resources Conservation Service’s Snow Telemetry network for the period from 1980 to 2004, were used to calibrate regional equations. The spatial and temporal resolution of the analysis was based on the resolution of available meteorological data – 4 km and 1 month, respectively. For a monthly time step, the reliability of the SWE estimates did not significantly increase when the number of regions was more than five. The performance of the developed regional equations was evaluated via a jack-knife technique. The regional equations developed using monthly resolution and large regions provided reliable estimates for the majority of regions from October to March, but not in April, especially in the North Pacific and Southwest regions. Sub-regionalization, a finer time step for the analysis and/or inclusion of additional SWE predictor variables may increase the reliability of SWE climatological estimates for warmer months.

Seasonal snow cover is an important source of melt water for irrigation and hydropower production in many regions of the world, but can also be a cause of disasters, such as avalanches and floods. In the remote Himalayan environment there is a great demand for up-to-date information on the snow conditions for the purposes of planned hydropower development and disaster risk reduction initiatives. We describe and evaluate a snow mapping setup for the remote Langtang Valley in the Nepal Himalayas, which can deliver data for snow and water availability mapping all year round. The setup utilizes (1) robust and almost maintenance-free in-situ instrumentation with satellite transmission, (2) a freely available numerical snow model, and (3) estimation of model key parameters from local meteorological and snow observations as well as from freely available climatological data. Novel features in the model include the estimation of melt parameters and solid precipitation from passive gamma-radiation based snow sensor data, as well as improved parameterization and estimation of melt water refreezing (36% of total melt) within, and sublimation/evaporation (57 mm yr−1) from the snow pack. Evaluation of the model results show a reasonable fit with snow cover data from satellite images. As many of the high-mountain regions in central and eastern Nepal show high correlation (>0.8) with the estimated snow line elevation in the Langtang catchment, the results may provide a first-order approximation of the snow conditions for these areas too.

Understanding and quantifying satellite-based remotely sensed snow cover errors are critical for successful utilization of snow cover products. The Moderate Resolution Imaging Spectroradiometer (MODIS) Snow Covered Area (SCA) product errors have been previously recognized to be associated with factors such as cloud contamination, snow pack particles, vegetation cover, and topography; however, the quantitative relationship between the retrieval errors and these factors remains elusive. Joint analysis of MODIS SCA and land surface temperature (LST) products, and in-situ air temperature and snow water equivalent (SWE) measurements provides a unique look at the error structure of the recently developed MODIS SCA products. Analysis of the MODIS SCA data set over the period from 2000 to 2005 was undertaken for the California/Nevada and Colorado regions of the western United States. Both regions have extensive observational networks. For this study area, the MODIS SCA product demonstrates strong ability in detecting the presence of snow cover (80%). However, significant spatial and temporal variations in accuracy (from 75% in high roughness to 86% in low roughness regions and 45% in October to 94% in February) suggest that a proxy is required to adequately predict the MODIS SCA product errors. For the first time, we demonstrate a relationship between the errors in the MODIS SCA products and temperature in the western United States, and find that this relationship is well-represented by the cumulative double exponential distribution function. We have performed a fitting and validation experiment by deriving the relationship between temperature and the errors in the MODIS SCA product from 2000-2004 period and using 2005 to independently test our method. This relationship is shown to hold for both in-situ daily mean air temperature and MODIS LST. Both of them are useful indices in quantifying the errors in MODIS product for various hydrological applications.

This paper presents results of two experiments on snow stress measurements under vehicles running over snow pack. In the experiments, a 5.6 T 4 × 4 military truck and a grooming machine were used. The military truck was driven over a 30-cm shallow snow pack in which a stress state transducer (SST) was installed. The grooming machine was driven over a 170-cm deep snow pack, in which four pressure sensors were installed at four different depths: 10, 20, 30, and 40 cm. Stress state in snow under the military truck was determined for rolling and for driving (pulling tests at high slip) modes and also for multiple passes. Stresses under the grooming machine were measured during low-speed rolling mode and multiple passes.

For the development of passive microwave remote sensing techniques, brightness temperature information on the medium covering the Earth's surface under different conditions is required. An emission model is a useful tool for the estimation of the brightness temperature of the medium. If the medium is a snow pack, the microwave radiation emitted will depend on the physical temperature, crystal characteristics, stratification and density of the snow. The parameters of microwave emission models available for the retrieval of snow characteristics are highly dependent on local environmental and climatological conditions. The aim of this study was to compare the empirical Chang model with the semiempirical radiative transfer model of snow developed at Helsinki University of Technology (HUT) for snow depth (SD) retrieval for UK snow packs. In the first step we used the HUT model. The root mean square error (RMSE) was used to validate the accuracy of model estimates. The snow events from different days in 1995, 1...

This paper presents the estimation of snow depth over north-western Indian Himalaya using the 18.7H and 36.5H GHz channels of Advanced Microwave Scanning Radiometer-EOS (AMSR-E). The Microwave Emission Model of Layered Snowpacks (MEMLS) was used along with AMSR-E to understand the difference in the snow pack emitted and sensor received signals due to the prevailing topography. The study shows that the brightness temperature of AMSR-E and MEMLS are comparable at 18.7 GHz with some differences in their values at 36.5 GHz showing the sensitivity of this channel to the prevailing topography. Three years of AMSR-E data were used to modify the 1.59 algorithm to suit the terrain and snow conditions of the north-western Indian Himalayas. The retrieved snow depth is then compared with ground observations. Data from December to February 2003-2006 were used for the study of snow depths less than 1 m. The modified algorithm estimates the snow depth better than the old algorithm over the mountainous terrains of the north-western Himalayas.

To make snow from treated municipal wastewater is one possible polishing treatment method that can be used in cold climates. In this study, the behavior of an artificial snow pile made from treated wastewater was studied. The primary focus of the study was the release of residual contaminants into the meltwaters. It was found that the first meltwaters of the snow pack are the most concentrated with contaminants. The first 30% of the meltwaters from the snow storage area included 50% of all the impurities held in the snow pack.

Frequency Modulated Continuous Wave (FMCW) radars have been used by snow scientists for the past 30 years. This radar technology provides a promising alternative to point measurements, as properties such as snow depth can be measured quickly and non-destructively. Recent advances in microwave FMCW radar technology have resulted in lightweight, portable instrumentation. This is in contrast to the early FMCW radar systems which were often too heavy to cover large distances efficiently. These advanced FMCW radars provide snow scientists and hydrologists with the ability to map snow pack properties, such as depth, snow water equivalent (SWE) and stratigraphy, rapidly over large distances, at high resolution. We discuss the development of FMCW radar over the past 30 years and review the diverse applications of these radars by snow scientists.

It was discovered in 1995 that, during the spring time, unexpectedly low concentrations of gaseous elemental mercury (GEM) occurred in the Arctic air. This was surprising for a pollutant known to have a long residence time in the atmosphere; however conditions appeared to exist in the Arctic that promoted this depletion of mercury (Hg). This phenomenon is termed atmospheric mercury depletion events (AMDEs) and its discovery has revolutionized our understanding of the cycling of Hg in Polar Regions while stimulating a significant amount of research to understand its impact to this fragile ecosystem. Shortly after the discovery was made in Canada, AMDEs were confirmed to occur throughout the Arctic, sub-Artic and Antarctic coasts. It is now known that, through a series of photochemically initiated reactions involving halogens, GEM is converted to a more reactive species and is subsequently associated to particles in the air and/or deposited to the polar environment. AMDEs are a means by which Hg is transferred from the atmosphere to the environment that was previously unknown. In this article we review Hg research taken place in Polar Regions pertaining to AMDEs, the methods used to collect Hg in different environmental media, research results of the current understanding of AMDEs from field, laboratory and modeling work, how Hg cycles around the environment after AMDEs, gaps in our current knowledge and the future impacts that AMDEs may have on polar environments. The research presented has shown that while considerable improvements in methodology to measure Hg have been made but the main limitation remains knowing the speciation of Hg in the various media. The processes that drive AMDEs and how they occur are discussed. As well, the role that the snow pack and the sea ice play in the cycling of Hg is presented. It has been found that deposition of Hg from AMDEs occurs at marine coasts and not far inland and that a fraction of the deposited Hg does not remain in the same form in the snow. Kinetic studies undertaken have demonstrated that bromine is the major oxidant depleting Hg in the atmosphere. Modeling results demonstrate that there is a significant deposition of Hg to Polar Regions as a result of AMDEs. Models have also shown that Hg is readily transported to the Arctic from source regions, at times during springtime when this environment is actively transforming Hg from the atmosphere to the snow and ice surfaces. The presence of significant amounts of methyl Hg in snow in the Arctic surrounding AMDEs is important because this species is the link between the environment and impacts to wildlife and humans. Further, much work on methylation and demethylation processes has occurred but these processes are not yet fully understood. Recent changes in the climate and sea ice cover in Polar Regions are likely to have strong effects on the cycling of Hg in this environment; however more research is needed to understand Hg processes in order to formulate meaningful predictions of these changes. It was discovered in 1995 that, during the spring time, unexpectedly low concentrations of gaseous elemental mercury (GEM) occurred in the Arctic air. This was surprising for a pollutant known to have a long residence time in the atmosphere; however conditions appeared to exist in the Arctic that promoted this depletion of mercury (Hg). This phenomenon is termed atmospheric mercury depletion events (AMDEs) and its discovery has revolutionized our understanding of the cycling of Hg in Polar Regions while stimulating a significant amount of research to understand its impact to this fragile ecosystem. Shortly after the discovery was made in Canada, AMDEs were confirmed to occur throughout the Arctic, sub-Artic and Antarctic coasts. It is now known that, through a series of photochemically initiated reactions involving halogens, GEM is converted to a more reactive species and is subsequently associated to particles in the air and/or deposited to the polar environment. AMDEs are a means by which Hg is transferred from the atmosphere to the environment that was previously unknown. In this article we review Hg research taken place in Polar Regions pertaining to AMDEs, the methods used to collect Hg in different environmental media, research results of the current understanding of AMDEs from field, laboratory and modeling work, how Hg cycles around the environment after AMDEs, gaps in our current knowledge and the future impacts that AMDEs may have on polar environments. The research presented has shown that while considerable improvements in methodology to measure Hg have been made but the main limitation remains knowing the speciation of Hg in the various media. The processes that drive AMDEs and how they occur are discussed. As well, the role that the snow pack and the sea ice play in the cycling of Hg is presented. It has been found that deposition of Hg from AMDEs occurs at marine coasts and not far inland and that a fraction of the deposited Hg does not remain in the same form in the snow. Kinetic studies undertaken have demonstrated that bromine is the major oxidant depleting Hg in the atmosphere. Modeling results demonstrate that there is a significant deposition of Hg to Polar Regions as a result of AMDEs. Models have also shown that Hg is readily transported to the Arctic from source regions, at times during springtime when this environment is actively transforming Hg from the atmosphere to the snow and ice surfaces. The presence of significant amounts of methyl Hg in snow in the Arctic surrounding AMDEs is important because this species is the link between the environment and impacts to wildlife and humans. Further, much work on methylation and demethylation processes has occurred but these processes are not yet fully understood. Recent changes in the climate and sea ice cover in Polar Regions are likely to have strong effects on the cycling of Hg in this environment; however more research is needed to understand Hg processes in order to formulate meaningful predictions of these changes.

Many arctic ecological processes are regulated by soil temperature that is tightly interconnected with snow cover distribution and persistence. Recently, various climate-induced changes have been observed in arctic tundra ecosystems, e.g. shrub expansion, resulting in reduction in albedo and greater C fixation in aboveground vegetation as well as increased rates of soil C mobilization by microbes. Importantly, the net effects of these shifts are unknown, in part because our understanding of belowground processes is limited. Here, we focus on the effects of increased snow depth, and as a consequence, increased winter soil temperature on ectomycorrhizal (ECM) fungal communities in dry and moist tundra. We analyzed deep DNA sequence data from soil samples taken at a long-term snow fence experiment in Northern Alaska. Our results indicate that, in contrast with previously observed responses of plants to increased snow depth at the same experimental site, the ECM fungal community of the dry tundra was more affected by deeper snow than the moist tundra community. In the dry tundra, both community richness and composition were significantly altered while in the moist tundra, only community composition changed significantly while richness did not. We observed a decrease in richness of Tomentella, Inocybe and other taxa adapted to scavenge the soil for labile N forms. On the other hand, richness of Cortinarius, and species with the ability to scavenge the soil for recalcitrant N forms, did not change. We further link ECM fungal traits with C soil pools. If future warmer atmospheric conditions lead to greater winter snow fall, changes in the ECM fungal community will likely influence C emissions and C fixation through altering N plant availability, fungal biomass and soil-plant C-N dynamics, ultimately determining important future interactions between the tundra biosphere and atmosphere. ispartof: Global Change Biology vol:22 issue:9 pages:3080-3096 ispartof: location:England status: published

Snow avalanche studies require different snow-meteorological parameters for avalanche forecasting. Snow surface temperature is one of the major parameters, which is responsible for the evolution of snow pack characteristics. In the present paper, the snow surface temperature was estimated using TERRA satellite based — Moderate resolution imaging spectroradiometer (MODIS) sensor for NW-Himalayas. Ground data observed by automatic weather stations (AWS) was used to calibrate the brightness temperature obtained by MODIS thermal bands data into the actual snow surface temperature data through regression analysis. A split window technique has been implemented for the estimation of snow surface temperature. The multi-date satellite derived snow surface temperature was validated with ground data of winter 2004–05 and 2005–06 collected at various observation stations located in different ranges of NW-Himalaya. Good correlations were observed for Upper Himalaya (0.98, 0.98), Middle Himalaya (0.92, 0.96) and Lower Himalaya (0.88, 0.82) for 2004–05 and 2005–06 winter respectively. Further, estimated snow surface temperature was also verified with snow-cover information collected by manned observatories and area delineated by thematic maps of snow surface temperature was validated with the different snow climatic zones of NW-Himalaya.

A hydrologic model was driven by the climate projected by 11 GCMs under two emissions scenarios (the higher emission SRES A2 and the lower emission SRES B1) to investigate whether the projected hydrologic changes by 2071–2100 have a high statistical confidence, and to determine the confidence level that the A2 and B1 emissions scenarios produce differing impacts. There are highly significant average temperature increases by 2071–2100 of 3.7°C under A2 and 2.4°C under B1; July increases are 5°C for A2 and 3°C for B1. Two high confidence hydrologic impacts are increasing winter streamflow and decreasing late spring and summer flow. Less snow at the end of winter is a confident projection, as is earlier arrival of the annual flow volume, which has important implications on California water management. The two emissions pathways show some differing impacts with high confidence: the degree of warming expected, the amount of decline in summer low flows, the shift to earlier streamflow timing, and the decline in end-of-winter snow pack, with more extreme impacts under higher emissions in all cases. This indicates that future emissions scenarios play a significant role in the degree of impacts to water resources in California.

Snow quality and its variations due to distance from the road were studied. Also, how the snow quality changes over time during the melting period was discussed. Snow samples were collected at three occasions during the winter of 2004. The samples were taken along a highway in the Luleå region, Sweden, with an average daily traffic load of 9,200 vehicles. Snow samples were taken perpendicular to the road and at different distances. The snow samples were analysed for metals and polycyclic aromatic hydrocarbons (PAH). Also, weather parameters such as temperature, precipitation, and wind speed and wind direction were measured. The highest total metal and PAH concentrations were found at the sample site closest to the road and at the end of the season. Before the melting period started, 42–57% of the total amount of metals and PAH were found in the first 1 m of the snow pack. This information could be valuable when one is discussing how to achieve sustainable snow-handling management.

Snow depth is one of the most important parameters for water resource management in those areas where snowfall is significant. Two different methods are applied here for snow monitoring using passive microwave data retrieved from the Special Sensor Microwave Imager (SSM/I). Since microwave sensors are least attenuated by the cloud cover, they have been widely used to retrieve snow cover data in a timely way. Passive microwave studies have shown that dry snow cover as well as mixed snow packs that contain free liquid water, affect naturally up-welling microwave radiation through forward scattering. In the first approach, brightness temperature (Tb) values at 18.7, 36.5 and 90 GHz vertical and horizontal frequencies for various snow packs are measured. In the second approach, the passive microwave response from snow packs of different physical conditions at 19, 37 and 85 GHz vertical and horizontal frequencies are modelled. Thus, various snow pack characteristics were estimated, the results indicat...

This paper introduces microwave radiometric data, snow pack properties and meteorological forcing data observation collected during the winter season '02/'03 in Fraser, Colorado, USA. The observation was part of the NASA Cold Land Processes Field Experiment (CLPX) at the Local Scale Observation Site (LSOS). Apart from continuous observation of meteorological data, which can be used to constrain physically based snow models, intensive ground-based passive microwave observations have been implemented during the winter season. Furthermore regular observations of snow pit properties have been conducted, including the snow density and snow grain size profiles. The data set provides the possibility to evaluate and improve radiative transfer models for snow and satisfactory modeling results have been achieved using the dense media radiative transfer theory (DMRT). All data will be released to the public on October 1, 2004 and are available from the website of the National Snow and Ice Data Center.

The search to improve protective techniques against natural phenomena such as snow avalanches requires thorough analysis of the behaviour of a snow cover in interaction with a structure. This paper deals with a multiscale approach allowing the overall behaviour of the snowpack to be inferred from the local properties. The constitutive equations are obtained from a statistical description of the mantel, regarded at the micro level as a cohesive granular assembly. Further general results are derived on the changes in fabrics as an application, the case of a snow pack in interaction with a flexible structure is discussed.

Snowfall depth, maximum snow depth, and snow pack environments under global warming are estimated over all of Japan by using 5 sets of predicted data. The input data used for the estimation were interpolated monthly mean air temperatures and amounts of monthly precipitation under a gradually increasing concentration of CO2 for 100 years from present conditions as predicted by 5 different institutes.The predicted trends varied according to geographic location. In Hokkaido and in the highlands of Honshu, no significant change was found, but the maximum snow depth decreased. In the Tohoku district (northeastern Honshu), except for in the highlands, snowfall and maximum snow depth decreased considrably. Snow pack environments changed from “dry” to “wet”. At low elevations on the side of the Sea of Japan of Honshu south of the Hokuriku district, no snowfall occurred and no snow pack of consequence was present by the mid-21st century. Although details among the 5 sets of predicted global warming data are different for air temperature and precipitation, the results predicted for snow conditions are very similar. For the influences of precipitation, the decrease observed in Canadian Centre for Climate Modelling and Analysis, and the large fluctuations of Australia’s Commonwealth Scientific and Industrial Research Organization, are limited to the winter snowfall depth in Hokkaido and in the highlands of Honshu. In contrast, the influence of the late temperature rising of Meteorological Research Institute (Japan) affects all aspects of snow. Airtemperature is a more important predictor of snow than is precipitation.

Snow cover has a key effect on climate change and hydrological cycling, as well as water supply to a sixth of the world's population across the Northern Hemisphere. However, reliable data on trends in snow cover in the Northern Hemisphere is lacking. Snow water equivalent (SWE) is a common measure of the amount of equivalent water of the snow pack. Here we verify the accuracy of three existing global SWE products and merge the most accurate aspects of them to generate a new SWE product covering the last 32 years (1979/80–2010/11). Using this new SWE product, we show that there has been a significant decreasing trend in the total mass of snow in the Northern Hemisphere. The most notable changes in total snow mass are −16.45 ± 6.68 and −13.55 ± 7.80 Gt year−1 in January and February, respectively. These are followed by March and December, which have trends of −12.58 ± 6.88 and −10.70 ± 5.62 Gt year−1, respectively, from 1979/80 to 2010/11. During the same period, the temperature in the study area raised 0.17 °C decade−1, which is thought to be the main reason of SWE decline.

Spatial and temporal characteristics of Ku- and X-band backscatter signatures of Alpine snow are discussed and related to in situ snow observations. The radar data have been acquired with the airborne SnowSAR sensor over three test sites in the Austrian Alps during the AlpSAR campaign in winter 2012/13. An example for inversion of backscatter images in terms of snow water equivalent (SWE) is presented. The backscatter signatures of three test sites in different elevation zones show significant differences in terms of mean values and temporal trends during the winter season. These variations can be attributed to snow structure and to properties of the medium below the snow pack.

In order to develop a seasonal snow model of land surface process as accurately as possible for climatic study, it is necessary to fully understand the effects of important snow internal processes and interaction with air and to get an insight into influence of several relevant parameterization schemes with parameters’ uncertainty to some degree. Using the snow model (SAST) developed by first author and other one and some useful field observation data, this paper has conducted a series of sensitivity studies on the parameterization schemes. They are relative to compaction process, snow thermal conduction, methodology of layering snow pack and to key parameters such as snow albedo, water holding capacity. Then, based on the results from the sensitivity studies, some useful conclusions for snow cover model improvement are obtained from the analysis of the results.

Reasonably simple yet realistic modelling schemes simulating the heat and mass balance within a snow pack are required to provide the necessary boundary conditions for meteorological and hydrological models. An improvement to a one-layer snow energy balance model (UEB, Tarboton etal., 1995) is proposed to better simulate snow surface and snow pack temperatures and, as a result, snowmelt. The modified scheme is assessed against measured snow data from the WINTEX field campaign during spring 1997 in northern Finland, and compared with results from a complex multi-layer snow energy balance scheme. The results show that separation of a one-layer representation into two snow layers and a soil layer enables a more realistic simulation of soil and snow temperatures as well as of the snow surface temperature. The two-layer and the multi-layer snow schemes yielded comparable results for internal processes in the snow whenever the simulation was carried out under similar boundary forcing. The modified scheme is proposed for use as a sub-scheme in meteorological or hydrological models, or as a tool for simulating spatially-variable snowmelt and the surface energy balance during seasonal snow cover.

The Chamonix and Maurienne valleys, French Alps, are major pathways for international truck and automobile traffic in Western Europe since they give access to the transalpine Mont Blanc and Frejus road tunnels. Moreover, the seasonal snow pack, which accumulates from autumn to spring in these two valleys, is a major contributor to fresh water resources for a large number of people, especially in large cities such as Geneva, Lyon and Grenoble. During winter 1998, we have collected a series of snow samples from two well-identified snowfall events at various altitudes ranging from 1150 to 3532 m on the sides of these two deep valleys. They were analysed for Li, B, Ti, V, Mn, Fe, Co, Cu, Zn, Mo, Pd, Ag, Cd, Sn, Sb, Ba, Pt, Au, Pb, Bi and U by double focusing inductively coupled plasma mass spectrometry with micro concentric nebulization (DF-ICP-MS-MCN). Ultraclean procedures were used for field sampling and laboratory analysis. The results show surprisingly low concentration values. With the exception of Li, B, Pd, Sn, Pt and Au, concentrations are found to decrease with increasing altitude. Crustal enrichment factors larger than ±10 times the mean crustal abundance are observed for Cu, Zn, Li, Mo, Pb, Ag, Bi, B, Sb, Sn, Cd, Au, Pt and Pd, strongly suggesting contributions from non-crustal sources for these metals, especially local and/or regional anthropogenic sources. Significant contributions are likely to originate from truck and automobile traffic, electrometallurgical and electrochemical industries and municipal incinerators. These data provide with a unique snapshot of the situation which prevailed in the two valleys before the disaster which occurred on 24 March 1999 in the tunnel of Mont Blanc, which resulted into a massive fall of the traffic in the Chamonix valley and a parallel rise in the traffic in the Maurienne valley.

During the last 2() years considerable progress has been made in our understanding of the physical processes controlling snowcover formation and melt. This work provides the basis required for a better understanding of, and ability to predict, the complex role snow plays in alpine and northern environments, including, for example, streamflow, climate, fluxes of nutrients and pollutants through the snow pack, and subnivean ecology. This paper provides a brief synthesis of the current state of our understanding and critical areas for future research on processes controlling snowcover distribution, melt and runoff. A major focus is to review progress to date on the importance of the heterogeneous nature of the snowcover over a wide range of scales.

The regional distribution of ion concentration and ionic load along a meridional transect in the Eastern Alps was investigated in 1993 and 1994 taking samples of the high alpine snow pack at six sites. High concentrations of atmospheric trace substances seemed to be related to high rates of snow accumulation. Zugspitze at the northern, and Careser Glacier at the southern margin of the Alps and Hintereisferner at the main divide had higher concentrations of sulfate and nitrate than adjacent sites. The pattern found in the ion concentrations was accentuated in the deposition rates with maximum ionic loads up to three times higher than the minima at the dry sites. Sulfate concentrations and sulfate to nitrate ratios were higher in the southern part of the transect.

International audience; Blowing snow events control the evolution of the snow pack in mountainous areas and cause inhomogeneous snow distribution. The goal of this study is to identify the main features of blowing snow events at an alpine site and assess the ability of the detailed snowpack model Crocus to reproduce the occurrence of these events in a 1D configuration. We created a database of blowing snow events observed over 10 years at our experimental site. Occurrences of blowing snow events were divided into cases with and without concurrent falling snow. Overall, snow transport is observed during 10.5% of the time in winter and occurs with concurrent falling snow 37.3% of the time. Wind speed and snow age control the frequency of occurrence. Model results illustrate the necessity of taking the wind-dependence of falling snow grain characteristics into account to simulate periods of snow transport and mass fluxes satisfactorily during those periods. The high rate of false alarms produced by the model is investigated in detail for winter 2010/2011 using measurements from snow particle counters.

Plant pathogenic snow-mold fungi have received much attention for many years due to their economic importance, but the identity and activity of nonpathogenic fungi of the under-snow environment have received much less study. In polar and alpine areas of the world, mats of saprophytic snow molds are commonly observed as snow melts in both forested and tundra biomes. Recent biogeochemical studies in high-elevation and high-latitude environments have shown conclusively that microbial activity under the snow pack contributes significantly to gas fluxes and to carbon and nitrogen cycling. In addition, Schmidt et al. have demonstrated that nonpathogenic snow-mold fungi are among the dominant organisms of the sub-nivean environment and carry out important ecosystem functions in tundra and forested ecosystems.

Simulation of brightness temperature and related snow parameters is essential to understand the microwave emission property and its evolution with change of the snow-soil system status. In this paper, a typical thin snow pack on North China Plain is measured on Nov 13–16th, 2009 at Luancheng test site HUT (Helsinki University of Technology) wet snow emission model is used to predict the brightness temperatures at 10.65, 18.7 and 36.5 GHz. A physically-based snow process model, SNTHERM (SNow THERmal Model), is applied to simulate the snow melting process. The measured snow density and grain size is compared with SNTHERM prediction and HUT inputs. Results show that the application of snow emission model and process model can explain the variation trend of wet snow emission properties well.