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Snow density is of paramount importance in water resource management, snow avalanche warning, and climate change research. However, the lack of competent methods for long‐term and vast‐scale snow density mapping persists due to the intricate spatiotemporal dependencies inherent in snow density, resulting in scarce and inaccurate snow density products. To address this challenge, a spatiotemporal random forest (STRF) model is constructed by leveraging in‐situ measurements, multisource remote sensing, and reanalysis data. It tackles the spatiotemporal dependencies in snow density arising from its inherent heterogeneity and the relations involving snow density and nonlinearly connected meteorological, terrain, vegetation, and snow‐related factors. The effectiveness of the model is substantiated through rigorous validation methods, including random, temporal, and spatial block cross‐validations as well as independent validation, apparently surpassing ERA5‐Land snow density. The estimated snow density is also demonstrated to be able to improve existing snow water equivalent data set using fixed snow density. Utilizing the proposed model, a data set of daily 25‐km snow density from 1980 to 2018 is constructed for stable snow cover areas in China, which holds significant potential for research and applications in the realm of snow hydrology. Plain Language Summary: Snow density plays a crucial role in managing water resources and issuing snow avalanche warnings. Snow density exhibits great spatiotemporal dependent structure due to its spatiotemporal heterogeneity and its relations with various influencing factors, which pose challenges to mapping long‐term and large‐scale snow density. This leads to a scarcity of accurate snow density products. To tackle this issue, we develope a spatiotemporal random forest (STRF) model by combining ground measurements, remote sensing sources, and reanalysis data. Notably, our model shows impressive results apparently outperforming the ERA5‐Land snow density data set. Our estimated snow density can also enhance existing snow water equivalent data set that rely on fixed snow density. Using our model, we produce a data set of daily 25‐km snow density from 1980 to 2018 for stable snow cover areas in China. This data set holds significant potential for research and practical applications in the field of snow hydrology. Key Points: A spatiotemporal random forest (STRF) model is proposed for estimating large‐scale and long‐term snow densitySTRF depicts the spatiotemporal dependent structure of snow density and handles its nonlinear relations with various influencing factorsThe estimated snow density outperforms ERA5‐Land snow density and could improve snow water equivalent data set using fixed snow density [ABSTRACT FROM AUTHOR]

Snow plays a vital role in the interaction between land and atmosphere in the state-of-the-art land surface models (LSMs) and the real world. While snow plays a crucial role as a boundary condition in meteorological applications and serves as a vital water resource in certain regions, the acquisition of its observational data poses significant challenges. An effective alternative lies in utilizing simulation data generated by Land Surface Models (LSMs), which accurately calculate the snow-related physical processes. The LSMs show significant differences in the complexities of the snow parameterizations in terms of variables and processes considered. In this regard, the synthetic intercomparisons of the snow physics in the LSMs can give insight for further improvement of each LSM. This study revealed and discussed the differences in the parameterizations among LSMs related to snow cover fraction, albedo, and snow density. We selected the most popular and well-documented LSMs embedded in the earth system models or operational forecasting systems. We examined single-layer schemes, including the Unified Noah Land Surface Model (Noah LSM), the Hydrology Tiled ECMWF Scheme of Surface Exchanges over Land (HTESSEL), the Biosphere-Atmosphere Transfer Scheme (BATS), the Canadian Land Surface Scheme (CLASS), the University of Torino land surface Process Interaction model in Atmosphere (UTOPIA), and multilayer schemes of intermediate complexity including the Community Noah Land Surface Model with Multi-Parameterization Options (Noah-MP), the Community Land Model version 5 (CLM5), the Joint UK Land Environment Simulator (JULES), and the Interaction Soil-Biosphere-Atmosphere (ISBA). Through the comparison analysis, we emphasized that inclusion of geomorphic and vegetation-related variables such as elevation, slope, time-varying roughness length, and vegetation indexes as well as optimized parameters for specific regions, in the snow-related physical processes, are crucial for further improvement of the LSMs. [ABSTRACT FROM AUTHOR]

Variations of the maximal depth of snow cover and snow density on the continental part of the Russian Arctic in different periods are considered. The distributions of the maximal snow depth and density are mapped. The values of snow density were taken for the moment when the snow cover thickness is maximal. [ABSTRACT FROM AUTHOR]

The detailed characterization of snow particles is critical for understanding the snow settling behavior and modeling the ground snow accumulation for various applications such as prevention of avalanches and snowmelt‐caused floods, etc. In this study, we present a snow particle analyzer for simultaneous measurements of various properties of fresh falling snow, including their size, shape, type, and density. The analyzer consists of a digital inline holography module for imaging falling snow particles in a sample volume of 88 cm3 and a high‐precision scale to measure the weight of the same particles in a synchronized fashion. The holographic images are processed in real‐time using a machine learning model and post‐processing to determine snow particle size, shape, and type. Such information is used to obtain the estimated volume, which is subsequently correlated with the weight of snow particles to estimate their density. The performance of the analyzer is assessed using monodispersed spherical glass and foam beads, irregular salt crystals, and thin disks with various shapes with known density, which shows <10% density measurement errors. In addition, the analyzer was tested in a number of field deployments under different snow and wind conditions. The system is able to achieve measurements of various snow properties at single particle resolution and statistical robustness. The analyzer was also deployed for 4 hr of operation during a snow event with changing snow and wind conditions, demonstrating its potential for long‐term and real‐time monitoring of the time‐varying snow properties in the field. Plain Language Summary: Our study introduces a snow particle analyzer designed to simultaneously measure various properties of falling snow, including size, shape, type, and density. The analyzer uses a compact digital holography system to capture images of snow particles and a high‐precision scale to weigh them. A machine learning‐based software processes the images in real‐time to extract snow particle properties, which are crucial for estimating the cumulative snow volume and average density. These properties are essential for the investigation of snow morphology, fall speed, accumulation rate, and related study of avalanches and snow drift. The analyzer is accurate, with less than 10% error in density measurement as assessed through laboratory tests. It has been successfully deployed under various snow conditions and provides continuous, real‐time monitoring of changing snow properties, even within the same snowfall event. This information is vital for improving models of snow settling and weather forecast. Compared to existing methods, the snow particle analyzer measures frozen hydrometeors in a larger range of sizes and offers faster, more accurate volume estimation. In addition to snow, the system could be applied to other geophysical processes where the measured particle properties are important, such as monitoring mineral dust, embers, sediments, volcanic ashes, and pollens. Key Points: Snow analyzer measures size, shape, type, and density of falling snow particles using digital inline holography (DIH) and high precision scaleInstrumentation is tested under various snow and wind conditions, providing real‐time snow classification and accurate density measurementsConditions under which single snow particle density can be estimated with sufficient accuracy are discussed [ABSTRACT FROM AUTHOR]

The Tibet Plateau (TP) and the Arctic are typically cold regions with abundant snow cover, which plays a key role in land surface processes. Knowledge of variations in snow density is essential for understanding hydrology, ecology, and snow cover feedback. Here, we utilized extensive measurements recorded by 697 ground-based snow sites during 1950–2019 to identify the spatio-temporal characteristics of snow density in these two regions. We examined the spatial heterogeneity of snow density for different snow classes, which are from a global seasonal snow cover classification system, with each class determined from air temperature, precipitation, and wind speed climatologies. We also investigated possible mechanisms driving observed snow density differences. The long-term mean snow density in the Arctic was 1.6 times that of the TP. Slight differences were noted in the monthly TP snow densities, with values ranging from 122 ± 29 to 158 ± 52 kg/m3. In the Arctic, however, a clear increasing trend was shown from October to June, particularly with a rate of 30.3 kg/m3 per month from March to June. For the same snow class, the average snow density in the Arctic was higher than that in the TP. The Arctic was characterized mainly by a longer snowfall duration and deeper snow cover, with some areas showing perennial snow cover. In contrast, the TP was dominated by seasonal snow cover that was shallower and warmer, with less (more) snowfall in winter (spring). The results will be helpful for future simulations of snow cover changes and land interactions at high latitudes and altitudes. [ABSTRACT FROM AUTHOR]

In this paper, we study centennial trends of climate and snow cover within the Ossola valley, in the Western Italian Alps. We pursue different tests (Mann Kendall MK, bulk, and sequential/progressive MKprog, Linear Regression, also with change point detection, and moving window average MW) on two datasets, namely (i) dataset1, daily temperature, precipitation, snow depth for 9 stations in the area, during 1930–2018, and (ii) dataset2, snow depth and density, measured twice a month (from February 1st to June 1st) for 47 stations during 2007–2023. We also verify correlation with glacier retreat nearby. In dataset1, we highlight a positive trend for minimum temperature with MK, and Linear Regression. Using MKprog/MW, a negative change of snow cover depth, and duration starting from the late 1980s is found. In dataset2, despite the annual variability in snow cover and 2022–2023 winter drought, we assess the maximum snow water equivalent (SWE) to be delayed with respect to maximum snow depth at high altitude (over a month above 2.700 m a.s.l.), highlighting the effect of settling in decreasing snow depth during spring. We also present a formula linking through Linear Regression the Day of the Year of SWE peak to altitude, relevant to assess the onset of thaw season. Due to the high altitude of the stations, and the paradigmatic nature of the Ossola Valley, hosting Toce River, a main contributor to the Lake Maggiore of Italy, our results are of interest, and can be used as a benchmark for the Italian Alps. [ABSTRACT FROM AUTHOR]

The results of long-term studies on the problems of forecasting avalanche risk and active avalanche control carried out in various organizations on the territory of the former USSR are presented. The studies were performed at specially equipped high-mountain avalanche stations, which made it possible to obtain experimental data on the snow cover mechanics and the dynamic parameters of avalanches. The results allowed developing a technology for preventive avalanche triggering based on the prediction of avalanche risk, the diagnosis of snow in the avalanche area, and the dynamic impact on snow stability. In most situations, the prevention of avalanches dangerous to humans and vital activity facilities includes a set of special avalanche safety measures based on active and passive avalanche defense. [ABSTRACT FROM AUTHOR]

The results of processing of a profile snow-measuring survey of snow cover in the Khibiny Mountains are presented. The survey was performed during the period of maximum snow accumulation (March of 2020) on the main elements of the landscape: mixed forest on the plain, open woodlands at the bottom of valleys, plateaus, wooded slopes, and upper slopes without woody vegetation. The averaged values of snow storage for different types of the landscapes were obtained for the period of the maximum snow accumulation in the snowy winter of 2019/20. The maximum snow storage (> 700 mm w.e.) was determined for areas on the high plateaus and open woodlands at the bottom of valleys. Minimum snow storage (> 400 mm w.e.) was recorded in areas of mixed forest on the plain and on an ice cover of lakes. Measurements of snow depth were carried out by the standard method (a handspike) and the ground-based radio-echo sounding using georadar with the frequency of 1600 MHz. The accuracy of this method allows measuring of the snow depth with accuracy of 1 cm for a dense snow and 2 cm for a loose one. Thus, the accuracy of measuring the snow depth with the radar is comparable to the accuracy of a handspike. A large number of radar measurements of snow depth on the profiles makes possible to determine the spatial variability of this value and its statistical characteristics. As a result, a vertical gradient of snow accumulation was defined as 25 mm w.e. per 100 m. The smallest spatial variability of snow depth was observed on profiles in the forests on the plain, in woodlands, and on the upper slopes. On profiles with complex relief (plateau, lower slopes), the spatial variability of snow depth is significant – the standard deviation was within limits of 30%. Based on the results of processing the field data, a map of snow storage over the studying area during the period of maximum snow accumulation was constructed. When constructing the map, we took into account the averaged data of the measurements for each type of landscape, the boundaries of woody vegetation, the height, steepness of slopes, and the high-altitude gradient of snow accumulation. It was found that features of the spatial distribution of snow cover were primarily due to the location of natural landscape complexes. The role of changes in snow storages with altitude was found to be insignificant.

Previous studies have indicated and shown the feasibility of retrieving snow density from ground based passive microwave measurements at the L band (1 – 2 GHz) from theoretical and experimental viewpoints. This paper expands the previous studies by presenting a case study of the retrieval problem with space borne brightness temperature measurements from the SMOS satellite over Sodankylä, Finland. To successfully retrieve snow density, also ground and forest parameters were included to the retrieval process. The retrieved variables were validated against in-situ ground, snow, and forest measurements made around the Sodankylä area over 12 winters from 2010 to 2022. A Bayesian framework was used to account for and quantify the uncertainties associated with the retrieval process. The results show good agreement between the retrieved ground and forest variables with the respective reference values. The snow density retrievals were seen to suffer from multiple sources of geophysical noise. However, the monthly average bulk snow density was successfully retrieved under stable midwinter conditions. The best agreement with the in-situ measurements was found for February, with a bias of -0.4 kg/m3, an unbiased root mean square difference of 12.2 kg/m3, and a correlation coefficient of 0.75. • Ground, snow, and forest parameters were retrieved over 12 winters. • The results were validated against comprehensive in-situ measurements. • The study site represents a typical heterogeneous boreal forest site. • Retrieved vegetation optical depth follows the modelled dynamics and value range. • Retrieved snow density agrees with measurements during the cold mid-winter period. [ABSTRACT FROM AUTHOR]

The paper presents characteristics of a snow cover observed on the Lagonaky high plateau (the Western Caucasus) in the interfluve of the rivers Belaya and Pshekha along two snow-measuring courses. The period of observations is 1973–2015, and the altitude interval is 460–2020 m. The characteristics measured are the following: depths of snow cover, the snow density, and temperature of different snow layers in the test pits. The stratigraphy of the snow thickness is described. In different years, observations did show changes in the depths which felt within a wide range of values from a few centimeters up to 5–6 m at the same points. The observations allowed determination of dates of snow appearance (setting-up of snow cover) as well as the disappearance in different parts of the Lagonaky high plateau. Durations of the snow cover in different altitudinal belts were also determined. By the end of March the snow cover disappears to altitudes of 700 m, and after 20th of April the slopes become free of snow to 1000 m. The snow cover duration in the low-altitude zone (500–1000 m) is 70–80 days, while in the middle altitudes (1,000–1,500 m) it is 130 days. In the highlands (above 2000 m), the snow cover lasts for 215 days. It must be emphasized that during the last five years the duration of snow cover has changed significantly. Materials of snow surveys showed that snow depths and water contents (water equivalents of snow cover) significantly differ from year to year. In seasons 1975/76, 1978/79, 19861/87, 1989/90 the winters were extremely snowy, while winters in 1976/77, 1979/80, 1983/84, 1990/91, 2013–2015 had small amount of snow. According to data of distant snow-measuring sticks, the maximum values of the snow depth (610 cm) have been recorded in March of 1976 near the tourist shelter Fisht (1596 м). Significant changes in the distribution of snow cover in recent years were probably related to the global climate changes.

In recent years, ground-penetrating radars are widely used for measuring thickness and liquid water content in snow cover on land and glaciers. The measurement accuracy depends on radio wave velocity (RWV) adopted for calculations. The RWV depends mainly on density, water content and structure of the snow cover and ice layers in it. The density and wetness of snow, and its structure can be estimated from data on RWV, using the available experimental and theoretical relations. Satisfactory results can be obtained using the Looyenga’s (1965) equations to estimate the density and wetness of snow cover, and equations of van Beek’s (1967) showing the distinction between RWV speeds velocities in snow cover and ice layers with different prevailing orientation and sizes of air or water inclusions.RWV in dry snow with density 300 kg/m3 may vary by 32 m/µs, depending on whether the vertical or horizontal orientation of the air inclusions prevails therein. In ice with density 700 kg/m3 effect of air inclusions orientation on differences in RWV is reduced to 5 m/µs. If the inclusions are not filled with air but with water, the difference in RWV in snow is 21 m/µs, and in ice is 24 m/µs. The RWV is affected not only by orientation of the inclusions, but their elongation. Twofold elongation of ellipsoidal air and water inclusions increases the difference in RWV in snow (with a density 300 kg/m3 ) to 23 m/µs and 22 m/µs.These estimates show a noticeable influence of snow structure on RWV in snow cover. The reliability of the above RWV estimates depends significantly on a thermal state of the snow cover, and decreases during snowmelt and increases in the cold period. It strongly depends on accuracy of measurements of the RWV in snow cover and its separate layers. With sufficiently high accuracy of the measurements this makes possible to detect and identify loose layers of deep hoar and compact layers of infiltration and superimposed ice, which is important for studying the liquid water storage of snow cover and a glacier mass balance. Therefore, considerable attention should be given to accuracy of the RWV measurements in dry and wet snow cover and its individual layers. With sufficiently high accuracy of measurements of the RWV, this should allow revealing such layers and estimating their thickness and average density.

Snow water equivalent (SWE) quantifies the amount of water present in a snowpack. Water in a snowpack is dependent on its depth, density, type of snow, previous freezing-thawing cycles, recent precipitation events, etc. This study deals with estimation of snow depth (HS) and snow density (RHO) from remotely sensed data using empirical relationships developed elsewhere and thereby generation of SWE maps and assessing the usability of such approach for non-instrumented eastern Himalayan watersheds. Remotely sensed data: digital elevation model (DEM), MODIS (Moderate Resolution Imaging Spectroradiometer) land surface temperature (LST), snow albedo, fractional snow cover and precipitation were used as inputs. Models were developed to find out the RHO, HS and SWE at pixel level. Finally, the SWE maps were generated for the dominating snow months of the year 2013. Results showed that RHO increased from January to May with its peak in the month of May having considerable amount of ripened snow. HS and SWE were found to follow the same trend as SCA (Snow Covered Area), which increased from January to peak in February and then decreased till May. The estimated RHO, HS and SWE were found acceptable in comparison to other published data collected from relevant research works on the Himalayan region and SWE from Global Land Data Assimilation Systems NOAH Land Surface Model (GLDAS NOAH LSM) simulation. [ABSTRACT FROM AUTHOR]

Thermal inertia has been successfully used in remote sensing applications that span from geology, geomorphology to hydrology. In this paper, we propose the use of thermal inertia for describing snow dynamics. Two different formulations of thermal inertia were tested using experimental and simulated data related to snowpack dynamics. Experimental data were acquired between 2012 and 2017 from an automatic weather station located in the western Italian Alps at 2,160 m. Simulations were obtained using the one‐dimensional multilayer Crocus model. Results provided evidences that snowmelt phases can be recognized, and average snowpack density can be estimated reasonably well from thermal inertia observations (R2 = 0.71; RMSE = 65 kg/m3). The empirical model was also validated with manual snow density measurements (R2 = 0.80; RMSE = 54 kg/m3). This study is the first attempt at the exploitation of thermal inertia for snow monitoring, combining optical and thermal remote sensing data. Plain Language Summary: Alpine snow represents a fundamental reservoir of fresh water at midlatitude. Remote sensing offers the opportunity to estimate snow properties in different spectral domains. In particular, the knowledge of the spatial and temporal variability of snow density could allow modeling of the snow water equivalent, which knowledge is crucial for managing water resources in the face of current climate change. In this study we show for the first time that snow thermal inertia can contribute to monitoring of snowmelt processes and snow density, opening new perspectives for remote sensing of the cryosphere. Key Points: Snow thermal inertia resembles the different phases of snowmelt processesAn empirical model is proposed for estimating snow density from apparent thermal inertiaThis study may open new perspectives in remote sensing of snowmelt processes [ABSTRACT FROM AUTHOR]

• Time-lapse structure-from-motion photogrammetry method were firstly used to estimate daliy snow surface sublimation rate through precise measurement of snow depth and volume change. • The results are comparable to measurements from eddy covariance and pan observation. • The advantage of O-T-SfM can provide plot scale spatial and temporal daily surface sublimation amount variation. • This method has the advantages of low cost, less maintenance and convenient installation. Snow sublimation is one of the major water and energy balance processes for the snow water cycle, which induced a significant snow loss. The precise monitoring of snow sublimation is essential for quantifying the snow processes. However, the conventional measurement methods (e.g., pan observation with sporadic data or eddy covariance [EC]) are not feasible for long-term observations in harsh alpine environments). Here, two different one-camera time-lapse structure from motion (O-T-SfM) photogrammetry methods, were adopted to monitor daily snow surface sublimation at two different periods in the cold, harsh conditions at top of the August-one ice cap in the Qilian Mountains of China. The results revealed that the O-T-SfM-based surface sublimation rate estimates has a good agreement with two different methods such as pan observation and EC measurement in snowfall-free conditions and snow bedforms were abundant. The estimated sublimation rate varied from 0.05 to 2.19 mm/day at the study site. The total surface sublimation was about 16.3 mm snow water equivalent in 32 days which is a significant loss of moisture from glacier in a short period. Our results suggest that by apply appropriate estimation methods, remote O-T-SfM photogrammetry derived DEM sequence can be successfully used to monitor the daily snow surface sublimation in winter. [ABSTRACT FROM AUTHOR]

A new snow layer probing device with a rectangular section is described in the paper. This device allows receiving snow samples with the undisturbed structures of snow layers, and more accurately reflects the actual value of the snow water equivalent. Application of a new snow probing device in snow measurements provides a new way to organize the monitoring dynamics of snow density layer variability, to estimate density of certain snow types in the snow cover, including the total or vertically averaged one, and to visualize the features of the snow stratigraphy structure. Snow layer probing device with a rectangular section as opposed to the cylindrical weight snow devices has constructive advantages. It also can be used in the calibration weight snow measurement devices that will ensure the comparability of the snow shooting results made in different climatic zones.

Spring snowpack in the mountains of the western United States has not declined substantially since the 1980s, despite significant global and regional warming. Here we show that this apparent insensitivity of snowpack to warming is a result of changes in the atmospheric circulation over the western United States, which have reduced snowpack losses due to warming. Climate model simulations indicate that the observed circulation changes have been driven in part by a shift in Pacific sea surface temperatures that is attributable to natural variability, and not part of the simulated response to anthropogenic forcing. Removing the influence of natural variability reveals a robust anthropogenic decline in western U.S. snowpack since the 1980s, particularly during the early months of the accumulation season (October–November). These results suggest that the recent stability of western U.S. snowpack will be followed by a period of accelerated decline once the current mode of natural variability subsides. Plain Language Summary: Melting snowpack is a vital source of water in the western United States during the summer, when rainfall is usually scarce. Although the amount of water contained in the snowpack has declined over the past century, it has been surprisingly stable since the 1980s, despite 1 °C of warming over the same period. At first glance, this result might appear to indicate that the snowpack is quite resilient to warming. However, here we show that the contribution of global warming to western U.S. snowpack loss has in reality been large and widespread since the 1980s, but mostly offset by natural variability in the climate system. This result points to a faster rate of snowpack loss in coming decades, when the impact of global warming is more likely to be amplified, rather than offset, by natural variability. Key Points: Circulation changes have offset most western U.S. snowpack loss from global warming since the 1980sCirculation changes are likely a result of natural variability, not anthropogenic forcingSnowpack loss will likely accelerate in coming decades as the phase of natural variability subsides [ABSTRACT FROM AUTHOR]

Ground‐penetrating radar (GPR) has become a promising technique in the field of snow hydrological research. It is commonly used to measure snow depth, density, and water equivalent over large distances or along gridded snow courses. Having built and tested a mobile lightweight set‐up, we demonstrate that GPR is capable of accurately measuring snow ablation rates in complex alpine terrain. Our set‐up was optimized for efficient measurements and consisted of a multioffset radar with four pairs of antennas mounted to a plastic sled, which was small enough to permit safe and convenient operations. Repeated measurements at intervals of 2 to 7 days were taken during the 2014/2015 winter season along 10 profiles of 50 to 200 m length within two valleys located in the eastern Swiss Alps. Resulting GPR‐based data of snow depth, density, and water equivalent, as well as their respective change over time, were in good agreement with concurrent manual measurements, in particular if accurate alignment between repeated overpasses could be achieved. Corresponding root‐mean‐square error (RMSE) values amounted to 4.2 cm for snow depth, 17 mm for snow water equivalent, and 22 kg/m3 for snow density, with similar RMSE values for corresponding differential data. With this performance, the presented radar set‐up has the potential to provide exciting new and extensive datasets to validate snowmelt models or to complement lidar‐based snow surveys. [ABSTRACT FROM AUTHOR]

Abstract: In regions where snowfall historically has been a defining seasonal characteristic of the landscape, warming winters have reduced the depth, duration, and extent of snowpack. However, most management and conservation has focused on how aboveground wildlife will be affected by altered snow conditions, even though the majority of species that persist through the winter do so under the snowpack in a thermally stable refugium: the subnivium. Shortened winters, forest management practices, and winter recreation can alter subnivium conditions by increasing snow compaction and compromising thermal stability at the soil–snow interface. To help slow the loss of the subnivium in the face of rapidly changing winter conditions, we suggest managers adopt regional conservation plans for identifying threatened snow‐covered environments; measure and predict the effects land cover and habitat management has on local subnivium conditions; and control the timing and distribution of activities that disturb and compact snow cover (e.g., silvicultural practices, snow recreation, and road and trail maintenance). As a case study, we developed a spatially explicit model of subnivium presence in a working landscape of the Chequamegon National Forest, Wisconsin. We identified landscapes where winter recreation and management practices could threaten potentially important areas for subnivium persistence. Similar modeling approaches could inform management decisions related to subnivium conservation. Current climate projections predict that snow seasons will change rapidly in many regions, and as result, we advocate for the immediate recognition, conservation, and management of the subnivium and its dependent species. [ABSTRACT FROM AUTHOR]

• Snow density among different snow classes has a clear spatial heterogeneity. • The longitudinal trend of the variability in snow density is more evident. • The seasonal evolution of snow density is apparent. • High snow density is related to a humid and warm climate and deep and wet snow. As a fundamental physical property of snowpack, snow density is used to describe many essential features of snowpack behaviour. However, the variability in snow density across the Northern Hemisphere (NH) is largely unknown. Here, we investigate snow density variability in conjunction with snow classes and geographic elements in the NH based on 6,954 snow sites from 1909 to 2019. Precipitation, air temperature, and snowfall based on meteorological sites, as well as the aridity index (AI) and wind speed from reanalysis data, are also applied to describe the effect of climate on snow density. The results present that the long-term mean snow density is 246 ± 70 kg/m3 considering all in-situ measurement sites. Considerable spatial heterogeneity in snow density exists with contrasting snow densities among differing snow classes. The values range from 198 ± 79 kg/m3 for ephemeral snow to 363 ± 63 kg/m3 for maritime snow. For the seasonal evolution of snow density, the different snow classes share a general characteristic with the overall NH, a slight decrease from October to September, followed by a sustained increase. Moreover, the densification rate in the snow stable period varies over a much smaller range than that during the snowmelt period. Furthermore, the longitudinal trends in the variability of snow density are more pronounced compared to altitudinal and latitudinal trends. High snow densities are typically associated with adequate precipitation, warm air temperature, large aridity index, a long snow season, and heavy snowfall for different snow classes. The results will deepen the understanding of the snow density distribution at hemispherical scale, and provide basic data for the remote sensing of snow water equivalent and parameterization of snow models. [ABSTRACT FROM AUTHOR]

Snow physical properties, snow cover and glacier facies are important parameters which are used to quantify snowpack characteristics, glacier mass balance and seasonal snow and glacier melt. This study has been done using C-band synthetic aperture radar (SAR) data of Indian radar imaging satellite, radar imaging satellite-1 (RISAT)-1, to estimate the seasonal snow cover and retrieve snow physical properties (snow wetness and snow density), and glacier radar zones or facies classification in parts of North West Himalaya (NWH), India. Additional SAR data used are of Radarsat-2 (RS-2) satellite, which was used for glacier facies classification of Smudra Tapu glacier in Himachal Pradesh. RISAT-1 based snow cover area (SCA) mapping, snow wetness and snow density retrieval and glacier facies classification have been done for the first time in NWH region. SAR-based inversion models were used for finding out wet and dry snow dielectric constant, dry and wet SCA, snow wetness and snow density. RISAT-1 medium resolution scan-SAR mode (MRS) in HV polarization was used for first time in NWH for deriving time series of SCA maps in Beas and Bhagirathi river basins for years 2013-2014. The SAR-based inversion models were implemented separately for RISAT-1 quad pol. FRS2, for wet snow and dry snow permittivity retrieval. Masks for layover and shadow were considered in estimating final snow parameters. The overall accuracy in terms of R value comes out to be 0.74 for snow wetness and 0.72 for snow density based on the limited ground truth data for subset area of Manali sub-basin of Beas River up to Manali for winter of 2014. Accuracy for SCA was estimated to be 95 % when compared with optical remote sensing based SCA maps with error of ±10 %. The time series data of RISAT-1 MRS and hybrid data in RH/RV mode based decompositions were also used for glacier radar zones classification for Gangotri and Samudra Tapu glaciers. The various glaciers radar zones or facies such as debris covered glacier ice, clean or bare glacier ice radar zone, percolation/refreeze radar zone and wet snow, ice wall etc., were identified. The accuracy of classified maps was estimated using ground truth data collected during 2013 and 2014 glacier field work to Samudra Tapu and Gangotri glaciers and overall accuracy was found to be in range of 82-90 %. This information of various glacier radar zones can be utilized in marking firn line of glaciers, which can be helpful for glacier mass balance studies. [ABSTRACT FROM AUTHOR]

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

Snow, as a fundamental reservoir of freshwater, is a crucial natural resource. Specifically, knowledge of snow density spatial and temporal variability could improve modelling of snow water equivalent, which is relevant for managing freshwater resources in context of ongoing climate change. The possibility of estimating snow density from remote sensing has great potential, considering the availability of satellite data and their ability to generate efficient monitoring systems from space. In this study, we present an innovative method that combines meteorological parameters, satellite data and field snow measurements to estimate thermal inertia of snow and snow density at a catchment scale. Thermal inertia represents the responsiveness of a material to variations in temperature and depends on the thermal conductivity, density and specific heat of the medium. By exploiting Landsat 8 data and meteorological modelling, we generated multitemporal thermal inertia maps in mountainous catchments in the Western European Alps (Aosta Valley, Italy), from incoming shortwave radiation, surface temperature and snow albedo. Thermal inertia was then used to develop an empirical regression model to infer snow density, demonstrating the possibility of mapping snow density from optical and thermal observations from space. The model allows for estimation of snow density with R2 CV and RMSE CV of 0.59 and 82 kg m−3, respectively. Thermal inertia and snow density maps are presented in terms of the evolution of snow cover throughout the hydrological season and in terms of their spatial variability in complex topography. This study could be considered a first attempt at using thermal inertia toward improved monitoring of the cryosphere. Limitations of and improvements to the proposed methods are also discussed. This study may also help in defining the scientific requirements for new satellite missions targeting the cryosphere. We believe that a new class of Earth Observation missions with the ability to observe the Earth's surface at high spatial and temporal resolution, with both day and night-time overpasses in both optical and thermal domain, would be beneficial for the monitoring of seasonal snowpacks around the globe. • Snow thermal inertia reflects the status of the snowpack evolution. • An empirical model is presented for mapping snow density from thermal inertia. • This study can pave new perspectives in remote sensing of the cryosphere. [ABSTRACT FROM AUTHOR]

The present standards for the determination of the ground snow load are inadequate in micro-climate regions that exhibit certain differences from their surroundings in terms of snowfall. In the regions where ground snow loads cannot be estimated accurately, structures may suffer serious damage. In the present study ground snow loads in the micro-climate zone in the eastern part of Turkey that receives heavy snowfall, are estimated through statistical analysis, and the results are compared with the present standards and the literature. In addition, examined were the relations for the best estimation of ground snow density which is the most important parameter for the determination of ground snow load. [ABSTRACT FROM AUTHOR]

Snow instability data provide information about the mechanical state of the snow cover and are essential for forecasting snow avalanches. So far, direct observations of instability (recent avalanches, shooting cracks or whumpf sounds) are complemented with field tests such as the rutschblock test, since no measurement method for instability exists. We propose a new approach based on snow mechanical properties derived from the snow micropenetrometer that takes into account the two essential processes during dry-snow avalanche release: failure initiation and crack propagation. To estimate the propensity of failure initiation we define a stress-based failure criterion, whereas the propensity of crack propagation is described by the critical cut length as obtained with a propagation saw test. The input parameters include layer thickness, snow density, effective elastic modulus, strength and specific fracture energy of the weak layer - all derived from the penetration-force signal acquired with the snow micro-penetrometer. Both instability measures were validated with independent field data and correlated well with results from field tests. Comparisons with observed signs of instability clearly indicated that a snow-pack is only prone to avalanche if the two separate conditions for failure initiation and crack propagation are fulfilled. To our knowledge, this is the first time that an objective method for estimating snow instability has been proposed. The approach can either be used directly based on field measurements with the snow micro-penetrometer, or be implemented in numerical snow cover models. With an objective measure of instability at hand, the problem of spatial variations of instability and its causes can now be tackled. [ABSTRACT FROM AUTHOR]

The article offers information on outdoor activities for students in which they can learn insulative value or thermal index of snow cover and its effect on surface climate. Topics discussed include high thermal index of snow due to its low thermal conductivity value, predicting fluctuations in temperature at ground interface, collecting data on snow depth and its density, calculating thermal index by using snow data, and discussing the role of snow reflectivity in climate system.

This study maps the geographic extent of intermittent and seasonal snow cover in the western United States using thresholds of 2000–2010 average snow persistence derived from moderate resolution imaging spectroradiometer snow cover area data from 1 January to 3 July. Results show seasonal snow covers 13% of the region, and intermittent snow covers 25%. The lower elevation boundaries of intermittent and seasonal snow zones increase from north-west to south-east. Intermittent snow is primarily found where average winter land surface temperatures are above freezing, whereas seasonal snow is primarily where winter temperatures are below freezing. However, temperatures at the boundary between intermittent and seasonal snow exhibit high regional variability, with average winter seasonal snow zone temperatures above freezing in west coast mountain ranges. Snow cover extent at peak accumulation is most variable at the upper elevations of the intermittent snow zone, highlighting the sensitivity of this snow zone boundary to climate conditions. [ABSTRACT FROM PUBLISHER]

This work provides an overview of various methods for estimating snow cover and properties in high mountains using remote sensing techniques involving microwaves. Satellite-based remote sensing with its characteristics such as synoptic view, repetitive coverage and uniformity over large areas has great potential for identifying the temporal snow cover. Many sensors have been used in the past with various algorithms and accuracies for this purpose. These methods have been improving with the use of Synthetic Aperture Radar sensors, working in different microwave frequencies, polarisation and acquisition modes. The limitations, advantages and drawbacks are illustrated while error sources and strategies on how to ease their impacts are also reviewed. An extensive list of references, with an emphasis on studies since 1990s, allows the reader to delve into specific topics. [ABSTRACT FROM PUBLISHER]

Quantifying snow grain size is crucial to analyze radiative transfer and mechanical interactions in the snow cover. We present a nondestructive method for fast measurements of snow optically equivalent diameter (OED). The method consists of diffuse near-infrared reflectance measurements by a compact integrating sphere setup to derive OED. This principle is realized in the handheld InfraSnow instrument. The correlation between snow OED and reflectance is calculated by applying Monte Carlo ray tracing to a 3-D implementation of the measurement geometry. Including the geometrical boundary conditions is essential to obtain a good agreement between modeled and measured InfraSnow reflectance values. In addition to InfraSnow reflectance, snow density is required as second input parameter to the OED analysis. Our InfraSnow OED measurements agree with reference OED measurements by micro computed tomography (micro-CT) within 25% for seven of the ten tested snow blocks. Furthermore, the relative differences between both measurement methods are close to the estimated uncertainties of the InfraSnow methodology. If density is measured by micro-CT and then used as InfraSnow model input to derive OED, an average agreement with the reference micro-CT OED values within 13% is found. If density is measured by a permittivity sensor, the average agreement is within 20%. [ABSTRACT FROM PUBLISHER]

Rising temperatures and changes in the precipitation regime will have a strong impact on the quality of the snow cover in the Arctic. A snow cover of good quality protecting lemmings from cold temperatures and predators is thought to be an important factor for maintaining the cyclic dynamic of their populations in the tundra. We examined if the characteristics of annual fluctuations (amplitude and shape of phases) in brown lemming ( Lemmus trimucronatus) density could be determined by snow depth, snow density, sub-nivean temperature and persistence of snow. Using an 18-year time series of brown lemming abundance on Bylot Island in the Canadian Arctic, we tested if snow variables could explain the residual variation between the observed lemming density and the one predicted by models where cyclicity had been accounted for. Our analysis provides support for the hypothesis that snow cover can affect the amplitude and possibly also the periodicity of lemming population cycles in the High Arctic. Summer abundance of brown lemmings was higher following winters with a deep snow cover and a low-density snow pack near the ground but was unaffected by the date of establishment or melting and duration of the snow cover. Two snow variables showed a temporal trend; mean winter snow depth tended to increase and date of establishment of the hiemal threshold occurred earlier over time. These temporal trends, which should be favourable to lemmings, may explain why healthy population cycles have apparently been maintained at our study site contrary to other Arctic sites. [ABSTRACT FROM AUTHOR]

Abstract: This study analyzes spatial variability of snow depth and density from measurements made in February and April of 2010 and 2011 in three 1–2km2 areas within a valley of the central Spanish Pyrenees. Snow density was correlated with snow depth and different terrain characteristics. Regression models were used to predict the spatial variability of snow density, and to assess how the error in computed densities might influence estimates of snow water equivalent (SWE). The variability in snow depth was much greater than that of snow density. The average snow density was much greater in April than in February. The correlations between snow depth and density were generally statistically significant but typically not very high, and their magnitudes and signs were highly variable among sites and surveys. The correlation with other topographic variables showed the same variability in magnitude and sign, and consequently the resulting regression models were very inconsistent, and in general explained little of the variance. Antecedent climatic and snow conditions prior to each survey help highlight the main causes of the contrasting relation shown between snow depth, density and terrain. As a consequence of the moderate spatial variability of snow density relative to snow depth, the absolute error in the SWE estimated from computed densities using the regression models was generally less than 15%. The error was similar to that obtained by relating snow density measurements directly to adjacent snow depths. [Copyright &y& Elsevier]

The Crocus snowpack model within the Interactions between Soil-Biosphere-Atmosphere (ISBA) land surface model was run over northern Eurasia from 1979 to 1993, using forcing data extracted from hydrometeorological datasets and meteorological reanalyses. Simulated snow depth, snow water equivalent, and density over open fields were compared with local observations from over 1000 monitoring sites, available either once a day or three times per month. The best performance is obtained with European Centre for Medium-Range Weather Forecasts (ECMWF) Interim Re-Analysis (ERA-Interim). Provided blowing snow sublimation is taken into account, the simulations show a small bias and high correlations in terms of snow depth, snow water equivalent, and density. Local snow cover durations as well as the onset and vanishing dates of continuous snow cover are also well reproduced. A major result is that the overall performance of the simulations is very similar to the performance of existing gridded snow products, which, in contrast, assimilate local snow depth observations. Soil temperature at 20-cm depth is reasonably well simulated. The methodology developed in this study is an efficient way to evaluate different meteorological datasets, especially in terms of snow precipitation. It reveals that the temporal disaggregation of monthly precipitation in the hydrometeorological dataset from Princeton University significantly impacts the rain-snow partitioning, deteriorating the simulation of the onset of snow cover as well as snow depth throughout the cold season. [ABSTRACT FROM AUTHOR]

Antarctic surface snow has been studied by means of continuous measurements and observations over a period of 3 yr at Dome C. Snow observations include solid deposits in form of precipitation, diamond dust, or hoar, snow temperatures at several depths, records of deposition and erosion on the surface, and snow profiles. Together with meteorological data from automatic weather stations, this forms a unique dataset of snow conditions on the Antarctic Plateau. Large differences in snow amounts and density exist between solid deposits measured 1m above the surface and deposition at the surface. We used the snow-cover model SNOWPACK to simulate the snow-cover evolution for different deposition parameterizations. The main adaptation of the model described here is a new event-driven deposition scheme. The scheme assumes that snow is added to the snow cover permanently only during periods of strong winds. This assumption followed from the comparison between observations of solid deposits and daily records of changes in snow height: solid deposits could be observed on tables 1m above the surface on 94 out of 235 days (40 %) while deposition at the surface occurred on 59 days (25 %) during the same period, but both happened concurrently on 33 days (14 %) only. This confirms that precipitation is not necessarily the driving force behind non-temporary snow height changes. A comparison of simulated snow height to stake farm measurements over 3 yr showed that we underestimate the total accumulation by at least 33 %, when the total snow deposition is constrained by the measurements of solid deposits on tables 1m above the surface. During shorter time periods, however, we may miss over 50% of the deposited mass. This suggests that the solid deposits measured above the surface and used to drive the model, even though comparable to ECMWF forecasts in its total magnitude, should be seen as a lower boundary. As a result of the new deposition mechanism, we found a good agreement between model results and measurements of snow temperatures and recorded snow profiles. In spite of the underestimated deposition, the results thus suggest that we can obtain quite realistic simulations of the Antarctic snow cover by the introduction of event-driven snow deposition. [ABSTRACT FROM AUTHOR]

Abstract: Static snow depth retrieval algorithms tend to underestimate the snow depth at the beginning of the snow season and overestimate the snow depth at the end of the snow season because the snow characteristics vary with the age of snow cover. A novel snow depth/water equivalent (SWE) data retrieval algorithm from passive microwave brightness temperature is proposed based on a priori snow characteristics, including the grain size, density and temperature of the layered snowpack. The layering scheme was established based on the brightness temperature difference (TBD) at two different frequencies, which indicates volume scattering, whereas the snow grain size and density of each layer were parameterized according to the age of the snow cover, and the snow temperature and temperature at the snow/soil interface were determined by the air temperature and snow depth. Furthermore, the microwave emission model of layered snowpacks (MEMLS) was used to simulate the brightness temperature at 10GHz, 18GHz and 36GHz based on the a priori snow characteristics including snow grain size, density, depth and snow layering. Finally, three look-up tables (one layer, two layers and three layers) were generated for each day, which represent the relationship between the brightness temperatures at 10GHz, 18GHz and 36GHz and snow depth. To avoid underestimation caused by the saturation of the microwave signal at 36GHz, the TBD1 (the difference of brightness temperature at 18 and 36GHz) was used to estimate the snow depth if TBD1 was less than 40K, and TBD2 (the difference of the brightness temperature at 10 and 18GHz) was used if TBD1 was greater than 40K. The snow depth and SWE determined by this new algorithm were validated by snow measurements at thirteen meteorological stations in Xinjiang, China from 2003 to 2010 and compared with existing SWE products from the National Snow and Ice Data Center (NSIDC), the Environmental and Ecological Science Data Center for West China (WESTDC), the European Space Agency (ESA) and measurements with a snow course. The results showed that the root mean squared error (RMSE) and the bias from this new algorithm were greatly reduced compared to NSIDC, moderately reduced compared to ESA and slightly reduced compared to WESTDC. The understanding of a priori local snow characteristics can improve the accuracy of snow depth and snow water equivalent estimation from passive microwave remote sensing data. [Copyright &y& Elsevier]

Möller, M., Möller, R., Beaudon, É., Mattila, O.-P., Finkelnburg, R., Braun, M., Grabiec, M., Jonsell, U., Luks, B., Puczko, D., Scherer, D. and Schneider, C., 2011. Snowpack characteristics of Vestfonna and De Geerfonna (Nordaustlandet, Svalbard) - a spatiotemporal analysis based on multiyear snow-pit data. Geografiska Annaler, Series A: Physical Geography, 93, 273-285. DOI: 10.1111/j.1468-0459.2011.00440.x ABSTRACT Extensive glaciological field measurements were carried out on the ice cap Vestfonna as well as on the minor ice body De Geerfonna (Nordaustlandet, Svalbard) within the framework of IPY Kinnvika. Field campaigns were conducted during the period 2007-2010 in spring (April/May) and summer (August). In this study we compile and present snow cover information obtained from 22 snow pits that were dug on Vestfonna during this period. Locations are along two transects on the northwestern, land terminating slope of the ice cap, on its central summit, Ahlmann Summit, and at a set of several other locations in the eastern and northern part of the ice cap. Snow-cover information acquired from four snow pits on adjacent De Geerfonna is also incorporated in this study. Field data are analysed regarding snow stratigraphy, snow density, snow hardness and snow temperature. Results reveal mean snow densities of around 400 kg m−3 for the snowpack of Vestfonna with no apparent spatial or interannual variability. A distinctly higher value of more than 450 kg m−3 was obtained for De Geerfonna. A spatial comparison of snow water equivalents above the previous end-of-summer surface serves for obtaining insights into the spatial distribution of snow accumulation across Vestfonna. Altitude was found to be the only significant spatial parameter for controlling snow accumulation across the ice cap. [ABSTRACT FROM AUTHOR]

Radar remote sensing has great potential to determine the extent and properties of snow cover. Availability of space-borne sensor dual-polarization C-band data of environmental satellite- (ENVISAT-) advanced synthetic aperture radar (ASAR) can enhance the accuracy in measurement of snow physical parameters as compared with single polarization data measurement. This study shows the capability of C-band synthetic aperture radar (SAR) data for estimating dry snow density over snow covered rugged terrain in Himalayan region. The snow density is an important parameter for the snow hydrology and avalanche forecasting related studies. An algorithm has been developed for estimating snow density, based on snow volume scattering and snow-ground scattering components. The radar backscattering coefficients of both horizontal-horizontal (hh) and vertical-vertical (vv) polarization and incidence angle are used as inputs in the algorithm to provide the snow dielectric constant which can be used to derive snow density using Looyenga's semi-empirical formula. Comparison was made between snow density estimated from algorithm using ENVISAT-ASAR hh and vv polarization data and the measured field value. The mean absolute error between estimated and measured snow density was found to be 0.024 g/cm3. [ABSTRACT FROM AUTHOR]

A radiative transfer model is used to simulate the sea ice radar altimeter effective scattering surface variability as a function of snow depth and density. Under dry snow conditions without layering these are the primary snow parameters affecting the scattering surface variability. The model is initialized with in situ data collected during the May 2004 GreenIce ice camp in the Lincoln Sea (73°W; 85°N). Our results show that the snow cover is important for the effective scattering surface depth in sea ice and thus for the range measurement, ice freeboard, and ice thickness estimation. [ABSTRACT FROM PUBLISHER]

The seasonal snow cover contributes significantly to the water resource in the Indian Himalayas, and snow density is one of the vital parameters in the determination of the hydrological potential of snow. The application of conventional methods for snow density retrieval using fully polarimetric SAR data is constrained by the properties of snow primarily due to the melt and frost cycles, as compared to fresh dry snow. The surface component of backscatter is significant in case of melt and frost. In the conventional decomposition based methods, the surface component is not considered for the inversion of permittivity, as proposed in this study. In this paper, we also propose a methodology for the estimation of snow density using bi-temporal fully polarimetric C-band RADARSAT-2 synthetic aperture radar (SAR) data. We utilize the relation between the differential modified Mueller matrix components, the attenuation constants, and the Fresnel transmission coefficients. The permittivity of snow is derived using the inversion of the Fresnel transmission coefficients which is used to determine the snow density using a state of the art empirical relation. The snow density estimates from the proposed method are compared with other methods based on coherency matrix decomposition and evaluated against in-situ measurements collected during a field campaign carried out in Dhundi in the state of Himachal Pradesh in India. The snow density estimates using the proposed method are observed to correlate with the in-situ measurements and were also found to be better than the decomposition based methods. • C-band microwave signal sensitivity to the mountainous snowpack • New method for snow density using bi-temporal RADARSAT-2 data • Inversion of snow density with partial information from Quad-PolSAR data • Comparison of existing PolSAR decomposition based snow density retrieval methods • Inversion of snow density without a prior knowledge of the study region [ABSTRACT FROM AUTHOR]