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Snow is important in Türkiye especially in the mountainous eastern areas where it may stay on the ground for more than half of the year. This region plays a vital role in feeding the water resources of the trans-boundary Euphrates-Tigris Basin, supporting crucial dams for water supply, irrigation and energy production. Thus, easy, frequent, correct and economical ways of measuring the snowpack is crucial. The snow properties at specific locations in the mountainous eastern regions over the two snow seasons (2018 and 2019) were studied by using different instruments and techniques, snow pit (box/cylinder/wedge cutter types), snow tube (Federal Sampler) and SnoTel (Snowpack Analyzer). The results point out a 1.7%-7.1% variation between different cutter type snow density measurements within snow pit analysis and the long-term utilized snow tube observations show a closer relation to box/cylinder type cutters. As for the continuous SnoTel observations, a variation of 2.4%-9.8% with various cutter types and a 5.9% difference regarding the snow tube density results are detected. These findings indicate a close range among different instruments, but it is the best when all three systems complement each other to characterize the snowpack effectively in the complex terrain since each has its own advantages. [ABSTRACT FROM AUTHOR]

Snow density is one of the basic properties used to describe snow cover characteristics, and it is critical for remote sensing retrieval, water resources assessment and modeling inputs. There are many instruments available to measure snow density in situ. However, there are measurement errors of snow density for bulk and layers or gravimetric and electronic instruments, which may affect the accuracy of remote sensing retrieval and model simulation. Especially in China, due to the noticeable heterogeneity of snowpacks, it is necessary to evaluate in detail the performance and applicability of snow density instruments in different snowpack conditions. This study evaluated the performance of different snow density instruments: the Federal Sampler, the model VS–43 snow density cylinder (VS–43), the wedge snow density cutter (WC1000 and WC250), and the Snow Fork. The average bulk snow density of all instrument measurements was set as the reference value for evaluation. The results showed that as compared with the reference, the VS–43 cylinder presented the best performance for bulk snow density measurement in the measured range with the lowest RMSE (11 kg m−3), BIAS (3 kg m−3), and MRE (1.6%). For layer observation, bulk snow density was overestimated by 8.1% with WC1000 and underestimated by 11.4% with Snow Fork which was the worst performance compared with the reference value, and there were greater measurement errors of snow density in the depth hoar than other snow layers. Compared with grassland, the uncertainty of snow density measurements was slightly lower in forests. Overall, the Federal Sampler and VS–43 cylinder are more suitable for bulk snow density measurement in deep snowpack regions across China, and it is recommended to use WC1000, WC250 and Snow Fork to measure the snow density of snow layers in the snow stratigraphy.

Low snow density causes snow to melt quickly, so there is no runoff during the warmer months of the year. Therefore, knowing the snow density can be useful in determining the amount of water. To predict snow density, this study used seven machine learning methods, including adaptive neural‐fuzzy inference system (ANFIS), M5P, multivariate adaptive regression spline (MARS), random forest (RF), support vector regression (SVR), gene expression programming (GEP) and eXtreme gradient boosting (XGBoost). Nine factors expected to affect snow density were considered. These factors were extracted using Google Earth Engine (GEE) from 1983 to 2022. The results showed that the surface temperature had the highest correlation (coefficient = −0.7), and the wind speed had the lowest correlation (coefficient = 0.3) among the considered factors on the snow density. Also, the best method was XGBoost (Nash–Sutcliffe efficiency [NSE] = 0.978, R = 0.957), and the worst method is SVR (NSE = 0.7, R = 0.9). Therefore, snow density can be estimated with good accuracy using a combination of machine learning methods and remote sensing. Highlights: This study used seven machine learning methods to estimate snow density.The surface temperature had the highest influence (R = −0.77) on the snow density.The best method for predicting snow density was XGBoost (NSE = 0.978).The worst method for predicting snow density was SVR (NSE = 0.71). [ABSTRACT FROM AUTHOR]

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]

Background: Snow forms when tiny ice crystals in clouds stick together to become snowflakes. If enough crystals stick together, they become heavy enough to fall to the ground. Where background includes Precipitation falls as snow when the air temperature is below 2 °C (275.15 K). The falling snow does begin to melt as soon as the temperature rises above freezing, but as the melting process begins, the air around the snowflake is cold. It is a myth that it needs to be below 0 °C (273.15) K to snow. Objective: In Iraq, the heaviest snowfalls tend to occur when the air temperature is between (273.15-275.15) K (0-2) °C. Methods: The data for this study, which includes Temperature (T), Snow Albedo (SA), and Snow Density (SD) as monthly-daily mean, taken from the European Center for Medium-Range Weather Forecasts (ECMWF) for fifteen years from 2008 to 2022 for several selected stations over northern Iraq. The method was to take the monthly rates of snow density, snow albedo, and temperature for the stations of Erbil, Sulaymaniyah, Zakho, Dohuk, and Amadiyah, and the type of relationship and strength of the connection between them was also known. Results: The study found an inverse relationship between snow albedo and snow density across the selected stations, indicating that an increase in snow density leads to a decrease in snow albedo. Notably, Duhok City exhibited the strongest relationship between snow albedo and density, with a regression coefficient of 0.9699 compared to other regions. Conclusions: This study highlights the complex relationship between snow albedo and density in northern Iraq. The strong correlation observed in Duhok City suggests the importance of further research to understand the factors influencing snow properties in this region. [ABSTRACT FROM AUTHOR]

Background: Snow forms when tiny ice crystals in clouds stick together to become snowflakes. If enough crystals stick together, they become heavy enough to fall to the ground. Where background includes Precipitation falls as snow when the air temperature is below 2 °C (275.15 K). The falling snow does begin to melt as soon as the temperature rises above freezing, but as the melting process begins, the air around the snowflake is cold. Objective: It is a myth that it needs to be below 0 °C (273.15) K to snow. In Iraq, the heaviest snowfalls tend to occur when the air temperature is between (273.15-275.15) K (0-2) °C. Methods: The data for this study, which includes Temperature (T), Snow Albedo (SA), and Snow Density (SD) as monthly-daily mean, taken from the European Center for Medium-Range Weather Forecasts (ECMWF) for fifteen years from 2008 to 2022 for several selected stations over northern Iraq. The method was to take the monthly rates of snow density, snow albedo, and temperature for the stations of Erbil, Sulaymaniyah, Zakho, Dohuk, and Amadiyah, and the type of relationship and strength of the connection between them was also known. Results: The study found an inverse relationship between snow albedo and snow density across the selected stations, indicating that an increase in snow density leads to a decrease in snow albedo. Notably, Duhok City exhibited the strongest relationship between snow albedo and density, with a regression coefficient of 0.9699 compared to other regions. Conclusions: This study highlights the complex relationship between snow albedo and density in northern Iraq. The strong correlation observed in Duhok City suggests the importance of further research to understand the factors influencing snow properties in this region.

The comparison of snow density in small open spaces adjacent to forest stands and under the canopy of spruce stands located on the macroslopes of the Middle Urals of the eastem end westem expositions was carried out. It was found that the density of snow in open spaces on the western macroslope is usually higher than on the eastem one. The forest canopy somewhat reduces the difference in the amount of snow density observed between forest clearings, but maintains a tendency of increased density on the western slope. On the western and eastern slopes, there is a higher variation in snow density under the canopy of stands than in forest clearings and a more significant variation under the crowns of spruce (Picea A. Dietr.) than in the aisles. On the eastern slope, the dynamics of snow density in forest clearings over the years of observations has no indisputable connection with the sum of temperatures and precipitation of winter periods. On the western slope, when comparing between the observation seasons, the snow density in the forest clearing differs at a reliable significant level (p < 0.05). The canopy of the forest can regulate the peculiarities of weather conditions, and most often, there is no statistically significant inter-seasonal dynamics of snow density on the permanent trial square of both slopes. A comparison of the snow density under the crowns and in the aisles showed that both on the eastern and western slopes the snow in the aisles is denser. Also, within the season, both on the eastern slope and on the western slope, the peculiarity of the canopy structure of the stand does not create conditions for the formation of snow density significantly different between the permanent trial square. Except for some years when differences are observed on the eastern slope between the density of snow on the permanent trial square with a predominance of the birch proportion in the composition formula and permanent trial square with pure spruce forests.

We present the surface mass balance (SMB) dataset from Vostok Station's accumulation stake farms which provide the longest instrumental record of its kind obtained with a uniform technique in central Antarctica over the last 53 years. The snow build-up values at individual stakes demonstrate a strong random scatter related to the interaction of wind-driven snow with snow micro-relief. Because of this depositional noise, the signal-to-noise ratio (SNR) in individual SMB time series derived at single points (from stakes, snow pits or firn cores) is as low as 0.045. Averaging the data over the whole stake farm increases the SNR to 2.3 and thus allows us to investigate reliably the climatic variability of the SMB. Since 1970, the average snow accumulation rate at Vostok has been 22.5 ± 1.3 kg m−2 yr−1. Our data suggest an overall increase of the SMB during the observation period accompanied by a significant decadal variability. The main driver of this variability is local air temperature with an SMB temperature sensitivity of 2.4 ± 0.2 kg m−2 yr−1 K−1 (11 ± 2% K−1). A covariation between the Vostok SMB and the Southern Oscillation Index is also observed.

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]

We present the surface mass balance (SMB) dataset from Vostok Station's accumulation stake farms which provide the longest instrumental record of its kind obtained with a uniform technique in central Antarctica over the last 53 years. The snow build-up values at individual stakes demonstrate a strong random scatter related to the interaction of wind-driven snow with snow micro-relief. Because of this depositional noise, the signal-to-noise ratio (SNR) in individual SMB time series derived at single points (from stakes, snow pits or firn cores) is as low as 0.045. Averaging the data over the whole stake farm increases the SNR to 2.3 and thus allows us to investigate reliably the climatic variability of the SMB. Since 1970, the average snow accumulation rate at Vostok has been 22.5 ± 1.3 kg m−2 yr−1. Our data suggest an overall increase of the SMB during the observation period accompanied by a significant decadal variability. The main driver of this variability is local air temperature with an SMB temperature sensitivity of 2.4 ± 0.2 kg m−2 yr−1 K−1 (11 ± 2% K−1). A covariation between the Vostok SMB and the Southern Oscillation Index is also observed. [ABSTRACT FROM AUTHOR]

Depth‐based and radar‐based remote sensing methods (e.g., lidar, synthetic aperture radar) are promising approaches for remotely measuring snow water equivalent (SWE) at high spatial resolution. These approaches require snow density estimates, obtained from in‐situ measurements or density models, to calculate SWE. However, in‐situ measurements are operationally limited, and few density models have seen extensive evaluation. Here, we combine near‐coincident, lidar‐measured snow depths with ground‐penetrating radar (GPR) two‐way travel times (twt) of snowpack thickness to derive >20 km of relative permittivity estimates from nine dry and two wet snow surveys at Grand Mesa, Cameron Pass, and Ranch Creek, Colorado. We tested three equations for converting dry snow relative permittivity to snow density and found the Kovacs et al. (1995) equation to yield the best comparison with in‐situ measurements (RMSE = 54 kg m−3). Variogram analyses revealed a 19 m median correlation length for relative permittivity and snow density in dry snow, which increased to >30 m in wet conditions. We compared derived densities with estimated densities from several empirical models, the Snow Data Assimilation System (SNODAS), and the physically based iSnobal model. Estimated and derived densities were combined with snow depths and twt to evaluate density model performance within SWE remote sensing methods. The Jonas et al. (2009) empirical model yielded the most accurate SWE from lidar snow depths (RMSE = 51 mm), whereas SNODAS yielded the most accurate SWE from GPR twt (RMSE = 41 mm). Densities from both models generated SWE estimates within ±10% of derived SWE when SWE averaged >400 mm, however, model uncertainty increased to >20% when SWE averaged <300 mm. The development and refinement of density models, particularly in lower SWE conditions, is a high priority to fully realize the potential of SWE remote sensing methods. [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]

Lidar multiple scattering measurements provide the probability distribution of the distance laser light travels inside snow. Based on an analytic two-stream radiative transfer solution, the present study demonstrates why/how these lidar measurements can be used to derive snow depth and snow density. In particular, for a laser wavelength with little snow absorption, an analytical radiative transfer solution is leveraged to prove that the physical snow depth is half of the average distance photons travel inside snow and that the relationship linking lidar measurements and the extinction coefficient of the snow is valid. Theoretical formulas that link lidar measurements to the extinction coefficient and the effective grain size of snow are provided. Snow density can also be derived from the multi-wavelength lidar measurements of the snow extinction coefficient and snow effective grain size. Alternatively, lidars can provide the most direct snow density measurements and the effective discrimination between snow and trees by adding vibrational Raman scattering channels.

Snow density varies spatially, temporally, and vertically within the snowpack and is the key to converting snow depth to snow water equivalent. While previous studies have demonstrated the feasibility of retrieving snow density using a multiple-angle L-band radiometer in theory and in ground-based radiometer experiments, this technique has not yet been applied to satellites. In this study, the snow density was retrieved using the Soil Moisture Ocean Salinity (SMOS) satellite radiometer observations at 43 stations in Quebec, Canada. We used a one-layer snow radiative transfer model and added a τ - ω vegetation model over the snow to consider the forest influence. We developed an objective method to estimate the forest parameters (τ , ω) and soil roughness ( S D ) from SMOS measurements during the snow-free period and applied them to estimate snow density. Prior knowledge of soil permittivity was used in the entire process, which was calculated from the Global Land Data Assimilation System (GLDAS) soil simulations using a frozen soil dielectric model. Results showed that the retrieved snow density had an overall root-mean-squared error (RMSE) of 83 kg/m3 for all stations, with a mean bias of 9.4 kg/m3. The RMSE can be further reduced if an artificial tuning of three predetermined parameters (τ , ω , and S D ) is allowed to reduce systematic biases at some stations. The remote sensing retrieved snow density outperforms the reanalysis snow density from GLDAS in terms of bias and temporal variation characteristics. [ABSTRACT FROM AUTHOR]

In many settings, data acquisition generates outliers that can obscure inference. Therefore, practitioners often either identify and remove outliers or accommodate outliers using robust models. However, identifying and removing outliers is often an ad hoc process that affects inference, and robust methods are often too simple for some applications. In our motivating application, scientists drill snow cores and measure snow density to infer densification rates that aid in estimating snow water accumulation rates and glacier mass balances. Advanced measurement techniques can measure density at high resolution over depth but are sensitive to core imperfections, making them prone to outliers. Outlier accommodation is challenging in this setting because the distribution of outliers evolves over depth and the data demonstrate natural heteroscedasticity. To address these challenges, we present a two-component mixture model using a physically motivated snow density model and an outlier model, both of which evolve over depth. The physical component of the mixture model has a mean function with normally distributed depth-dependent heteroscedastic errors. The outlier component is specified using a semiparametric prior density process constructed through a normalized process convolution of log-normal random variables. We demonstrate that this model outperforms alternatives and can be used for various inferential tasks. [ABSTRACT FROM AUTHOR]

This study aims to present and analyze the time series of the snow parameters focusing on representative geographical areas of the Eastern Mediterranean (i.e., Greece and Italy) and to examine their seasonal variability, in terms of region and geography. The satellite retrievals were firstly validated against in-situ retrievals for 67 common days, with a mean bias equal to −0.018 cm, with a near-Gaussian distribution, showing the good performance of the satellite snow detection. The satellite-based analysis resulted in increasing trends of snow water equivalent, attributed to the enhanced values between 2000 and 2009; however, decreasing trends are found starting from 2010 until now of −1.79 × 10−17 and −2.31 × 10−18 over the two representative areas of Greece (e.g., Thessaloniki and Kozani). A similar pattern is found for the snow water equivalent in the Italian study area, with a decreasing trend of −4.45 × 10−18. The presented results contribute to a better understanding of the spatial snow distribution and the snow coverage seasonality that could be crucial for the long-term groundwater management, by combining snow data trends from in-situ data and satellite statistics. [ABSTRACT FROM AUTHOR]

In semi-arid climate conditions of South of Russia (Lower Volga region), where farming is complicated by a lack of atmospheric moisture, the preservation of snow in an agroforest landscape serves as an additional source of moisture for the growth and development of tree and shrub vegetation. The paper investigates the role of forest belts of a combined structure on the nature of snow deposition depending on different patterns of shrub placement (along the top edge, along the lower edge, on both sides) during the winter of 2020-2021 in Volgograd region, Russia. The results of the conducted snow surveys show that experimental sites with shrubs along the top edge were characterized by the highest level of snow accumulation both in the forest belt and in the adjacent field. The snow-retaining function in the forest belt zone was weaker in the presence of shrubs on both sides. It has been established that the values of snow density increase with approaching the forest stand. The highest values were recorded in the forest belt with shrubs along the top edge (up to 0.5 g cm-3). The accumulation of snow and its density eventually affected the amount of snow reserves. The highest values of snow reserves were observed in the forest belt with shrubs along the top edge with a row width of up to 1 m. This contributed to the accumulation of 82-203 mm of snow in the forest belt area (at 43 mm of snowfall). Shrub placement along the lower edge provoked a loss of moisture in the forest belt itself, which made this pattern ineffective. The results obtained can be applied in the design of protective forest belts in the areas with insufficient moisture. [ABSTRACT FROM AUTHOR]

Snowpack water equivalent (SWE) is a key variable for water resource management in snow‐dominated catchments. While it is not feasible to quantify SWE at the catchment scale using either field surveys or remotely sensed data, technologies such as airborne LiDAR (light detection and ranging) support the mapping of snow depth at scales relevant to operational water management. To convert snow depth to water equivalent, models have been developed to predict SWE or snowpack density based on snow depth and additional predictor variables. This study builds upon previous models that relate snowpack density to snow depth by including additional predictor variables to account for (1) long‐term climatologies that describe the prevailing conditions influencing regional snowpack properties, and (2) the effect of intra‐ and inter‐year variability in meteorological conditions on densification through a cumulative degree‐day index derived from North American Regional Reanalysis products. A non‐linear model was fit to 114 506 snow survey measurements spanning 41 years from 1166 snow courses across western North America. Under spatial cross‐validation, the predicted densities had a root‐mean‐square error of 47.1 kg m−3, a mean bias of −0.039 kg m−3, and a Nash‐Sutcliffe Efficiency of 0.70. The model developed in this study had similar overall performance compared to a similar regression‐based model reported in the literature, but had reduced seasonal biases. When applied to predict SWE from simulated depths with random errors consistent with those obtained from LiDAR or Structure‐from‐Motion, 50% of the SWE estimates for April and May fell within −45 to 49 mm of the observed SWE, representing prediction errors of −15% to 20%. [ABSTRACT FROM AUTHOR]

Accurate snowfall forecasting and quantitative snowfall estimation remain challenging due to the complexity and variability of snow microphysical properties. In this paper, the microphysical characteristics of snowfall in the Yanqing mountainous area of Beijing are investigated by using a Particle Size and Velocity (PARSIVEL) disdrometer. Results show that the high snowfall intensity process has large particle-size distribution (PSD) peak concentration, but the distribution of its spectrum width is much smaller than that of moderate or low snowfall intensity. When the snowfall intensity is high, the corresponding D m value is smaller and the N w value is larger. Comparison between the fitted μ − Λ relationship and the relationships of different locations show that there are regional differences. Based on dry snow samples, the Z e − S R relationship fitted in this paper is more consistent with the Z e − S R relationship of dry snow in Nanjing, China. The fitted ρ s − D m relationship of dry snow is close to the relationship in Pyeongchang, Republic of Korea, but the relationship of wet snow shows greatly difference. At last, the paper analyzes the statistics on velocity and diameter distribution of snow particles according to different snowfall intensities. [ABSTRACT FROM AUTHOR]

In this study, we analyzed the accuracy of snow observations at weather stations compared to the data of snow measurements in the vicinity of these stations. Also, the variations of measurement errors were estimated considering the inter-annual snowpack variability and landscape heterogeneity of the river basins. The studies were conducted in three catchments in the south of Western Siberia: forest-steppe the Kasmala River (2011–2020), low mountain the Mayma River (2015–2020), and steppe the Kuchuk River (2019–2020). The results showed that the accuracy of snow measurements at the weather stations was higher in the low mountain catchment than in the plain basins. Interannual differences in precipitation combined with wind transport influenced the most significant errors in the Kasmala catchment (relative error of snow depth on the snow gauge – 46,3%, and SWE on the permanent course – 17,3%). However, in the Mayma catchment, the snow depth measurements on the snow gauge agreed well with the catchment means in all years (mean relative error 7,7%). The relative error of snow depth measurements on the snow gauge in the Kuchuk catchment was 7,5%, and of SWE on the permanent snow course was 19,1%. The small snow depth error occurred due to the composition of the error distribution and large differences between open and forested areas.

This study reports on four consecutive winter campaigns (2016–2020) at the “Davos-Laret Remote Sensing Field Laboratory” in the Swiss Alps to gain insight into the L-band microwave emission of ground covered with seasonal snow. Close-range L-band Brightness temperatures $T_\mathrm{{B}}^{p,\phi }(\theta)$ were measured over the site scanning different observation nadir angles $\theta$ and azimuth angles $\phi$ at horizontal and vertical polarization p = {H,V}. State parameters (SPs) of the snowpack (e.g., height, density, and snow water equivalent) and the subnivean soil (permittivity, temperature) were measured quasi-simultaneously using in-situ sensors and sampling, as well as meteorological data. In each campaign, $T_\mathrm{{B}}^{p,\phi }(\theta)$ were measured over a “natural area” and a “reflector area” with a metal mesh reflector laid on the ground before snow accumulation. The radiometer measurements over “reflector area” allowed to retrieve the time-series of Snow liquid Water-content $W_\mathrm{{S}}$ and Snow liquid Water-Column ${WC}_\mathrm{{S}}$, which are employed as “derived measurements” to support interpretation of $T_\mathrm{{B}}^{p,\phi }(\theta)$ measured over “natural areas” during different winter phases. The detailed approach for the estimation of $W_\mathrm{{S}}$ and ${WC}_\mathrm{{S}}$ using L-band radiometer data is presented. The data and analyses in this article address the following major points: 1) determination of the characteristic features of measured $T_\mathrm{{B}}^{p,\phi }(\theta)$ during different periods in each of the four winter campaigns; 2) effects of dry and wet snow precipitation on L-band radiometer data compared to corresponding simulations; 3) effect of removal and compression of the snowpack on $T_\mathrm{{B}}^{p,\phi }(\theta)$; 4) effects of spatial heterogeneity on brightness temperatures. Finally, the study is concluded with recommendations relevant for future close-range remote sensing campaigns.

In semi-arid climate conditions, where farming is complicated by a lack of atmospheric moisture, the preservation of snow in an agroforest landscape serves as an additional source of moisture for the growth and development of tree and shrub vegetation. The paper investigates the role of forest belts of a combined structure on the characteristics of snow deposition depending on different patterns of shrub placement (along the top edge, along the lower edge, on both sides). The results of the conducted snow surveys show that experimental sites with shrubs along the top edge are characterized by the highest level of snow accumulation both in the forest belt and in the adjacent field. The snow-retaining function in the forest belt zone is weaker in the presence of shrubs on both sides. It has been established that the values of snow density increase with approaching the forest stand. The highest values were recorded in the forest belt with shrubs along the top edge (up to 0.5 g cm-3). The accumulation of snow and its density eventually affected the amount of snow reserves. The highest values of snow reserves were observed in the forest belt with shrubs along the top edge with a row width of up to 1 m. This contributed to the accumulation of 82-203 mm of snow in the forest belt area (at 43 mm of snowfall). Shrub placement along the lower edge provoked a loss of moisture in the forest belt itself, which made this pattern ineffective. The results obtained can be applied in the design of protective forest belts in the areas with insufficient moisture.

The boreal forest covers a significant portion of the Northern Hemisphere and is snow‐covered for over half of the year. Understanding the interactions between the forest canopy and snow is essential in hydrological, meteorological, and climate modelling. However, this is challenging because the density of a forest can range from closed canopies to open gaps. In winter 2018–2019, we assessed differences in snowpack microstructure in small forest gaps and under the canopy of a humid boreal site in eastern Canada. Our experimental approach consisted of quasi‐continuous weekly observations of stratigraphy and measurements of density profiles and temperature in a series of snow pits in both environments. High‐resolution specific surface area (SSA) profiles were measured twice, allowing for an estimation of snow permeability and hydraulic conductivity. The shallower snowpack under the canopy displayed a stronger vertical temperature gradient and less compaction than in forest gaps. This resulted in the dominance of faceted snow crystals with a small SSA. In contrast, we observed that small, rounded grains with a larger SSA than that of faceted crystals prevailed in the gaps. Due to denser snow and higher SSA, snow permeability inside gaps was found to be lower than under the canopy. Implicitly, the estimated hydraulic conductivity was also lower in gaps. Following rain‐on‐snow events, snow under the canopy displayed layers of melt‐freeze polycrystals, while in the gaps, well‐defined ice layers were formed. The combination of low snow permeability and ice layers is likely to affect liquid water transport in the gap snowpack as compared to the canopy. Although observed at relatively small scales in our study, if these differences are confirmed at a catchment scale, they are likely to impact the hydrology of forested areas during snowmelt. [ABSTRACT FROM AUTHOR]

Gravimetric and dielectric permittivity measurement systems (DMS) are applied to measure snow density, but few studies have addressed differences between the two measurement systems under complex snowpack conditions. A field experiment was conducted to measure the snow density using the two measurement systems in stratigraphical layers of different densities, liquid water content (LWC), hardness, and shear strength, and the performance of the two measurement systems was analyzed and compared. The results showed that the snow density from the DMS tended to underestimate by 9% in the dry snowpack and overestimate by 3% in the wet snowpack, expressed as the percentage of the mean density from the gravimetric measurement system (GMS). Compared with the GMS, the DMS has relatively low precision and accuracy in the dry snowpack and similar precision and accuracy in the wet snowpack. The accuracy and precision of the two measurement systems increased with the increase of hardness and shear strength of snow in the dry snowpack, but the accuracy and precision measured of the DMSs increased with the decrease of hardness and shear strength of snow in wet snowpack. The results will help field operators to choose a more reasonable measurement system based on snowpack characteristics to get reliable density data and optimize field measurements. HIGHLIGHTS The performances of the gravimetric measuring system and the dielectric permittivity measuring system at different snow stratigraphies have been compared.; The precision and accuracy of the snow density measurement systems are found to be sensitive to the shear strength and hardness of snow.; The snow density measurement systems show relatively low precision and accuracy in low-density snow.;

Seasonal alpine snow contributes significantly to the water resource. It plays a crucial role in regulating the environmental feedback and from the perspective of socio-economic sustainability in the alpine regions. While most nations are pursuing renewable energy sources, hydropower generated from snowmelt runoff is one of the primary sources. Additionally, alpine regions with snow cover are major tourist destinations that are often affected by natural disasters such as avalanches. The snowmelt runoff and early avalanche warning require timely information on the spatio-temporal aspects of the snow geophysical parameters. In this regard, advances in remote sensing of snow have been observed to be significant. Recent developments in remote sensing technology in the visible, infrared, and microwave spectrum have significantly improved our understanding of snow geophysical processes. This paper provides a review concerning the qualitative and quantitative studies of alpine snow. The electromagnetic characteristics of the alpine snow are largely dependent upon its inherent geophysical structure and the properties of the snow. Snow behaves differently with respect to the wavelength of the incident radiation. In this paper, we provide a categorical review of the remote sensing techniques for estimating the snow geophysical properties, inclusive of permittivity, density, and wetness corresponding to the wavelength used in the remotely sensed data: (1) visible-infrared spectrum including multispectral/hyperspectral, (2) active and passive microwave spectrums. We also discuss the recent advancements in the remote sensing techniques for approximating the volumetric snowpack parameters such as the snow depth and the snow water equivalent based on active and passive microwave remote sensing. This review further discusses the limitations of the techniques reviewed and future prospects for the retrieval of snow geophysical parameters (SGP) corresponding to the recent progress in remote sensing technology. In summary, the recent advances have laid down a foundation for rigorous assessment of seasonal snow using spaceborne remote sensing, particularly at a regional scale. Yet, the scope for improvements in the methods and payload design exists.

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]

Snow density is an essential property of snowpack. To obtain the spatial variability of snow density and estimate it in different periods of the snow season remain challenging, particularly in the mountainous area. This study analysed the spatial variability of snow density with in‐situ measurements in three different periods (i.e., accumulation, stable and melt periods) of the snow seasons of 2017/2018 and 2018/2019 in the middle Tianshan Mountains, China. The simulation performances of the multiple linear regression (MLR) model and three machine learning (random forest [RF], extreme gradient boosting [XGB] and light gradient boosting machine [LGBM]) models were evaluated. Results showed that snow density in the melt period (0.27 g cm−3) was generally greater than that in the stable (0.20 g cm−3) and accumulation periods (0.18 g cm−3), and the spatial variability of snow density in the melt period was slightly smaller compared to that in other two periods. The snow density in the mountainous areas was generally higher than that in the plain or oasis areas. It increased significantly (p < 0.05) with elevation during the accumulation and stable periods. In addition to elevation, latitude and ground surface temperature also had critically impacted the spatial variability of snow density in the study area. In the current study, the machine learning models, especially RF, performed better than MLR for simulating snow density in the three periods. Based on the key environmental variables identified by the machine learning model and correlation analysis, this study also provides practical MLR equations to estimate the spatial variance of snow density during different snow periods in the middle Tianshan Mountains. This method can be used for regional snow mass and snow water equivalent prediction, leading to a better understanding of local snow resources. [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.

The Arctic has warmed at twice the global average over recent decades, which has led to a reduction in the spatial extent and mass balance of snow. The increase in occurrence of winter extreme events such as rain-on-snow, blizzards, and heat waves has a significant impact on snow thickness and density. Dense snowpack conditions can decrease or completely prevent foraging by Peary caribou (Rangifer tarandus pearyi) by creating "locked pastures," a situation where forage is present but not accessible under snow or ice. Prolonged and severe weather events have been linked to poor body condition, malnutrition, high adult mortality, calf losses, and major population die-offs in Peary caribou. Previous work has established the link between declines in Peary caribou numbers in the Canadian Arctic Archipelago and snow conditions, however these efforts have been limited by the quality and resolution of data describing snow physical properties in the Arctic. Here, we 1) investigate whether a snow model adapted for the Antarctic (SNOWPACK) can produce snow simulations relevant to Canadian High Arctic conditions, and 2) test snow model outputs to determine their utility in predicting Peary caribou occurrence with MaxEnt modelling software. We model Peary caribou occurrence across three seasons: July - October (summer forage and rut), November - March (fall movement and winter forage), and April - June (spring movement and calving). Results of snow simulations using the Antarctic SNOWPACK model demonstrated that both top and bottom density values were greatly improved when compared to simulations using the original version developed for alpine conditions. Results were also more consistent with field measurements using the Antarctic model, though it underestimated the top layer density compared to on-site measurements. Modelled outputs including snow depth and CT350 (cumulative thickness of snow layers surpassing the critical density value of 350 kg-m-3; a density threshold relevant to caribou) proved to be important predictors of Peary caribou space use in each of the top seasonal models along with vegetation and elevation. All seasonal models were robust in that they were able to predict reasonably well the occurrence of Peary caribou outside the period used to develop the models. This work highlights the need for continued monitoring of snow conditions with climate change to understand potential impacts to the species' distribution. [ABSTRACT FROM AUTHOR]

Snow production results in high volume of snow that is remaining on the low-elevation ski pistes after snowmelt of natural snow on the off-piste sites. The aim of this study was to identify snow/ice depth, snow density, and snow water equivalent of remaining ski piste snowpack to calculate and to compare snow ablation water volume with potential infiltration on the ski piste area at South-Central Slovak ski center Košútka (Inner Western Carpathians; temperate zone). Snow ablation water volume was calculated from manual snow depth and density measurements, which were performed at the end of five winter seasons 2010–2011 to 2015–2016, except for season 2013–2014. The laser diffraction analyzes were carried out to identify soil grain size and subsequently the hydraulic conductivity of soil to calculate the infiltration. The average rate of water movement through soil was seven times as high as five seasons’ average ablation rate of ski piste snowpack; nevertheless, the ski piste area was potentially able to infiltrate only 47% of snow ablation water volume on average. Limitation for infiltration was frozen soil and ice layers below the ski piste snowpack and low snow-free area at the beginning of the studied ablation period.

Ku-band Scatterometers are best used for mapping snowmelt/freeze as normalized backscatter is sensitive to the water content of the snow. In this study, the potential of Ku-band scatterometer SCATSAT-1 at 13.515 GHz is explored with regard to the estimation of snow density, which is an important geophysical parameter of the snowpack behavior. The density calculation is done using horizontal–horizontal (HH) and vertical–vertical (VV) polarized data available at 2.25 km with incidence angles of 49° and 57° and snow dielectric constant inversion algorithm. The algorithm is tested over part of Indian Himalayas and Antarctica. Looyenga’s semiempirical formula is used to estimate snow density in Antarctica, and a modified formula is used for the Himalayas. The validation of the analysis is done using reanalysis density data from European Centre for Medium Range Weather Forecasts (ECMWF) at Indian Himalayas during January 2017 and observed field density data during 2016–2017 at Antarctica. The correlation and mean absolute error between derived and modeled densities were 0.788 and 0.006 g/cm3 for the Himalayas. A correlation of 0.81 and mean absolute error of 0.079 g/cm3 at 10 cm depth was found out between observed and derived densities for Antarctica. The derived density varied with the temperature at a study site in Antarctica. [ABSTRACT FROM AUTHOR]

Retrieval of snow density using hybrid polarimetric RISAT-1 synthetic aperture radar (SAR) data is implemented in this study. The C-band datasets were acquired in the fine resolution stripmap mode-1 (FRS-1) in which microwaves EM are transmitted in the right circular polarization from the sensor and are received in linear (horizontal or vertical) polarizations resulting in hybrid polarizations-RH and RV. The datasets were acquired for the date of February 23, 2014 covering the Dhundi region in Manali district of Himachal Pradesh. In this season, the area experiences regular snowfall, hence the whole area was covered with the dry snow layer. Hybrid decomposition technique along with the integral equation model (IEM) is utilized for snow density extraction of the snowpack in the study area. The modeled and theoretical approaches are used to retrieve the dielectric constant of the snowpack. Fresnel coefficients, utilized in the IEM modeling approach, are the function of snowpack dielectric constant and the local incidence angle of the incident wave from the sensor. The retrieved snow density map is generated for the area. The validation of the retrieved results is done using ground data collected during the period.

Snow geophysical parameters such as wetness, density and permittivity are a significant input in hydrological models and water resource management. In this paper, we utilize the triangle method based on a feature space developed with the near-infrared (NIR) reflectance and the Normalized Differenced Snow Index (NDSI) for the estimation of surface snow wetness, permittivity and density. The triangular feature space based on NIR reflectance and NDSI is parameterized to yield a linear relationship between the snow wetness and the NIR reflectance. Snow density and permittivity are derived based on the least squares solution of empirical relations based on the observations of surface snow wetness. The proposed methodology was evaluated using Sentinel-2 data, and the modeled snow geophysical parameters were validated with respect to field measurements. Based on the results, it was inferred that the NIR reflectance varies linearly with the liquid water content in the snow. A good agreement was determined between the modeled and measured parameters for wet snow conditions as observed by the coefficient of determination of 0.968, 0.521 and 0.969 for the snow wetness, density and permittivity (real part), respectively. The proposed approach can be significantly utilized with unmanned aerial sensors for monitoring of physical properties of fresh or wet snow and is thus expected to contribute considerably in hydrological applications and avalanche studies.

The relationship between the results of calculations of the dynamics of the temperature regime of the in freezing and thawing soil profile with the heating effect of the snow cover is considered. To analyze this connection, two coupled models are used: the model of formation and degradation of snow cover in winter and the model of heat transfer and soil moisture transport in underlying vadoze zone profile. Parametrization of the influence of the snow cover, which at each calculated moment of time has the current average density and depth, on the dynamics of the temperatures of the soil profile is due to the use of its specific thermal resistance, which depends on its current depth and the thermal conductivity coefficient. The coefficient of thermal conductivity of the snow cover is related with its density using six different published empirical relationships. Modeling of heat transfer in freezing and thawing soil is carried out on the example of the field site for monitoring the thermal regime located on the territory of the Zvenigorod Biological Station of Moscow State University. It is shown that the well-known relationships give similar curves for the dynamics of the depth of seasonal freezing, including the degradation of the seasonal freezing layer in the spring period, with the same dynamics of the snow cover. However, the maximum penetration depth of the zero isotherm differs significantly for different snow conductivity-snow density relationships. The tested six relationships were divided into three groups. Minimal freezing is provided by the Sturm model and the effective medium model. The average and rather poorly differentiating freezing from each other is given by the Pavlov, Osokin et al. and Jordan relationships. The greatest value of the freezing depth is obtained with using Pavlov’s relationship with a temperature correction.

In this study, it is aimed to calculate snow density, depth, and snow water equivalent with the help of single polarization TerraSAR-X data. In this context, Mount Erciyes/Turkey was chosen as the pilot study area and TerraSAR-X data with HH polarization were used. In addition, in situ measurements were performed simultaneously with the satellite pass to be used as input and validation data for the model to be used. Also, snow densities were obtained by inverse approach, Kriging, and ISO-4355 methods. Snow densities, in situ measurements, and SAR data were integrated into the produced D-InSAR snow depth model; snow depth, snow volume, and snow water equivalent were estimated. Consequently, it has been revealed that the snow depth, snow volume, and snow water equivalent parameters vary according to the snow density-calculated methods. Also, in this study performed with a single polarization, it is revealed that snow parameters can be accessed without multiple polarization. Snow densities were evaluated separately for 0.31g/cm3 and 0.36 g/cm3, and snow depth, snow volume, and snow water equivalent maps were produced. Our study, which is supported by in situ measurements, has been shown to be consistent with the snow depth results in the region. Besides, the results of the model produced in the study were found to be compatible with in situ measurements. [ABSTRACT FROM AUTHOR]

This study demonstrates the potential value of a combined unmanned aerial vehicle (UAV) Photogrammetry and ground penetrating radar (GPR) approach to map snow water equivalent (SWE) over large scales. SWE estimation requires two different physical parameters (snow depth and density), which are currently difficult to measure with the spatial and temporal resolution desired for basin‐wide studies. UAV photogrammetry can provide very high‐resolution spatially continuous snow depths (SD) at the basin scale, but does not measure snow densities. GPR allows nondestructive quantitative snow investigation if the radar velocity is known. Using photogrammetric snow depths and GPR two‐way travel times (TWT) of reflections at the snow‐ground interface, radar velocities in snowpack can be determined. Snow density (RSN) is then estimated from the radar propagation velocity (which is related to electrical permittivity of snow) via empirical formulas. A Phantom‐4 Pro UAV and a MALA GX450 HDR model GPR mounted on a ski mobile were used to determine snow parameters. A snow‐free digital surface model (DSM) was obtained from the photogrammetric survey conducted in September 2017. Then, another survey in synchronization with a GPR survey was conducted in February 2019 whilst the snowpack was approximately at its maximum thickness. Spatially continuous snow depths were calculated by subtracting the snow‐free DSM from the snow‐covered DSM. Radar velocities in the snowpack along GPR survey lines were computed by using UAV‐based snow depths and GPR reflections to obtain snow densities and SWEs. The root mean square error of the obtained SWEs (384 mm average) is 63 mm, indicating good agreement with independent SWE observations and the error lies within acceptable uncertainty limits. [ABSTRACT FROM AUTHOR]

Through the collision and freezing of supercooled droplets on the surface of snow particles, riming can change the density, fall velocity and aspect ratio of snow particles. Riming plays an important role in the formation of precipitation in cold clouds. To our knowledge, the impact of riming on snow microphysics is yet well understood, and there is a lack of observations obtained in China addressing this issue. For the first time in East China, the connection between riming and snow microphysical properties was investigated quantitatively during snow events in two winters. To quantify the degree of riming, the rime mass fraction (FR) is derived using the combination of a 2‐D video disdrometer (2DVD) and a weighing gauge. FR is added to the density‐diameter and fall velocity‐diameter relations, and a quantitative relation between riming and shapes of snowflakes is established based on the in situ observation of the 2DVD. The results show that riming can well explain the variability in the density and fall velocity of snowflakes. The changes in the shape of snowflakes can be divided into two distinct stages with increasing riming: in the initial stage (FR < 0.5), the aspect ratio increases very slowly, while in the later stage (FR > 0.5), the aspect ratio increases rapidly. Direct observations of the continuous changes in the shapes of snowflakes with riming are in good agreement with the retrieval results of radar. Key Points: The impact of riming on snow microphysical properties is reported for the first time in East ChinaA quantitative relation between riming and the shapes of snowflakes is established based on in situ observations with a 2‐D video disdrometerThe changes in the shapes of snowflakes can be divided into two distinct stages as riming progresses [ABSTRACT FROM AUTHOR]

In high‐latitude and mountain regions, local processes such as redistribution by wind, snow metamorphism, and percolation of water produce a complex spatial distribution of snow depths and snow densities. With its strong control on the ground thermal regime, this snow distribution has pronounced effects on ground temperatures at small spatial scales which are typically not resolved by land surface models (LSMs). This limits our ability to simulate the local impacts of climate change on, for example, vegetation and permafrost. Here, we present a tiling approach combining the CryoGrid permafrost model with snow microphysics parametrizations from the CROCUS snow scheme to account for subgrid lateral exchange of snow and water in a process‐based way. We demonstrate that a simple setup with three coupled tiles, each representing a different snow accumulation class with a specific topographic setting, can reproduce the observed spread of winter‐time ground surface temperatures (GST) and end‐of‐season snow distribution for a high‐Arctic site on Svalbard. For the 3‐year study period, the three‐tile simulations showed substantial improvement compared to traditional single‐tile simulations which naturally cannot account for subgrid variability. Among others, the representation of the warmest and coldest 5% of the observed GST distribution was improved by 1–2°C, while still capturing the average of the distribution. The simulations also reveal positive mean annual GSTs at the locations receiving the greatest snow cover. This could be an indication for the onset of localized permafrost degradation which would be obscured in single‐tile simulations. Key Points: In high‐Arctic areas, wind redistribution of snow leads to a strong variability in snow depths and hence ground surface temperaturesA parametrization for lateral transport of snow between three model tiles is implemented in the CryoGrid 3 permafrost modelThe three‐tile setup reproduces the observed spatial variability of snow depths and ground surface temperatures in a process‐based fashion [ABSTRACT FROM AUTHOR]

The results of experimental investigation of thermal conductivity of snow on the Svalbard archipelago in the conditions of natural occurrence are considered. The observations were carried out in the spring of 2013–2015 in the vicinity of the meteorological station «Barentsburg». The obtained data were processed using the Fourier equation of thermal conductivity that allowed determination of the coefficient t of thermal conductivity of the snow with different structure and density. The thermal conductivity of snow depends on the contacts between ice crystals. The larger the contact area, the better the heat transfer from one layer to another. But the strength characteristics of snow, and especially its hardness, depend on the bonds between ice crystals, so the thermal conductivity and hardness of snow depend on the structure of snow. Note, that measurements of snow hardness are less laborious than measurements of its thermal conductivity. For layers of snow cover of different hardness the relationship between snow thermal conductivity and its density has been established. To verify the reliability of the approach to the determination of snow thermal conductivity, numerical experiments were performed on a mathematical model, which did show good convergence of the results. The obtained formulas for the coefficient of thermal conductivity of very loose, loose, medium and hard snow (according to the international classification of seasonal snow falls) are compared with the data of other studies. It was found that when the snow density is within the range 0.15–0.40 g/cm3 these formulas cover the main variety of thermal conductivity of snow. This allows estimating the coefficient of thermal conductivity and to determine the thermal resistance of snow cover in the field by measuring the density and hardness of different layers of snow.

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.

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Accurate estimation of snowfall rate during snowstorms is crucial. This estimate directly impacts the hydrological and atmospheric models. The snow density plays a very important role in estimating the snowfall rate. In this paper, the snow density is investigated during a huge snowstorm event during the International Collaborative Experiment held during the Pyeongchang 2018 Olympics and Paralympic winter games (ICE-POP 2018). The density is calculated using the terminal velocities and diameters of the snow particles measured by a disdrometer. In this study, we used not only radar reflectivity factor (Z) for snowfall rate (S) estimation, but also dual-frequency ratio (DFR). We derived S-Z and S-Z-DFR relations for snowfall estimation during this snowstorm event after considering the snow density. The comparisons are performed between the National Aeronautics and Space Administration dual-frequency dual-polarization Doppler radar and precipitation gauges using these two power-law relations. The results show that the two relations for snowfall rate estimation agree well with gauges, but the S-Z-DFR method performs the best, which has a lower normalized standard error. The error in the snowfall rate estimates decreases as the time scale becomes large. This shows that the S-Z-DFR algorithm is a promising way for snowfall quantitative precipitation estimation and can be used as a ground validation tool for global precipitation measurement snowfall production evaluations. [ABSTRACT FROM AUTHOR]

Intra and inter-annual variations in the sea ice thickness are highly sensitive indicators of climatic variations undergoing in the earth's atmosphere and oceans. This paper describes the method of estimating sea ice thickness using radar waveforms data acquired by SARAL/Altika mission during its drifting orbit phase from July 2016 onwards yielding spatially dense data coverage. Based on statistical analysis of return echoes, classification of the surface has been carried out in three different types, viz. floe, lead and mixed. Time delay correction methods were suitably selected and implemented to make corrections in altimetric range measurements and thereby freeboard. By assuming hydrostatic equilibrium, freeboard data were converted into sea ice thickness. Results show that sea ice thickness varies from 4 to 5 m near ice shelves and 1 to 2.5 m in the marginal sea ice regions. Freeboard and sea ice thickness estimates were also validated using NASA's Operation Ice Bridge (OIB) datasets. Freeboard measurements show very high correlation (0.97) having RMSE of 0.13. Overestimation of approximately 1–2 m observed in the sea ice thickness, which could be attributed to distance between AltiKa footprint and OIB locations. Moreover, sensitivity analysis shows that snow depth and snow density over sea ice play crucial role in the estimation of sea ice thickness. [ABSTRACT FROM AUTHOR]

Snow depth observations can be leveraged with data assimilation (DA) to improve estimation of snow density and snow water equivalent (SWE). A key consideration for mission and campaign design is how snow depth retrieval characteristics (including observation timing/frequency and sampling error) influence SWE accuracy and uncertainty in a DA framework. To quantify these effects, we implement a particle filter (PF) assimilation technique to assimilate depth and validate this approach against observed snow density and SWE at 49 snow telemetry sites across 9 years. We sample from continuous in situ snow depth records to test a range of measurement timing and sampling error scenarios representative of remote sensing capabilities. Assimilation reduces density bias by over 40% and SWE bias by over 70% across climate zones and in both wet and dry years. There is little incremental benefit to SWE accuracy when assimilating more than one depth observation near peak accumulation. SWE estimates are less sensitive to observation timing than sampling error. Alternatively, more frequent depth observations improve melt‐out date timing and reduce SWE uncertainty, a key consideration when evaluating the operational utility of DA. In matching depth observations, the PF mostly acts to increase model precipitation inputs, while not systematically shifting other parameter values or forcings across the climate zones represented with the study sites. This demonstrates that precipitation is the largest source of model error. With DA, density errors are still nontrivial (above 10%), illuminating the need for further improvements to modeled density to estimate SWE within specified error limits. Plain Language Summary: The amount of water stored in seasonal snowpack (snow water equivalent or "SWE") is an important variable for water management yet is currently difficult to measure in mountainous areas. One technique is to measure snow depth from airborne or satellite platforms and use that depth to guide a model that simulates snow density and SWE with a technique called data assimilation. We show that assimilation reduces errors in modeled SWE relative to control model runs, even when using a single depth observation to guide the model. However, more depth observations are helpful to reduce model uncertainty. Key Points: Data assimilation improves modeled snow depth, density, and SWE across a range of climates and wet/dry seasons in the western United StatesAssimilating one snow depth observation near maximum accumulation yields the majority of improvements to estimated snow density and SWEAssimilating frequent snow depth observations yields the lowest SWE uncertainty and smallest melt‐out date errors [ABSTRACT FROM AUTHOR]

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]