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Snow is an important environmental variable and a primary water resource in many areas of the world. Monitoring seasonal snowpack properties is also crucial for properly managing snow-related hazards such as snow avalanches and snowmelt floods. Recently, an innovative radar architecture, based on the use of two receivers, has been proposed for snowpack monitoring for the case of dry snow, where the snowpack depth and bulk density can be calculated with one single radar measurement, without any kind of external aid. This article presents the extension of this innovative radar architecture for the case of wet snow. The approach to determine, not only the snowpack depth and bulk density but also the liquid water content, is outlined and discussed in detail, along with the experimental validation of the operating principle for two cases., This work was supported in part by the Italian Ministry of Education, University and Scientific Research (MIUR) under Project SIR2014 "SNOWAVE" RBSI148WE5. © 2022, IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other work.

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 R2CV and RMSECV 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.

The data in this repository was used for the calibration and validation of the SWE2HS model in the following publication:Aschauer, J., Michel, A., Jonas, T., & Marty, C. (2023). An empirical model to calculate snow depth from daily snow water equivalent: SWE2HS 1.0. Geoscientific Model Development Discussions, 1-19. https://doi.org/10.5194/gmd-2022-258Contains daily snow water equivalent and snow depth timeseries from stations in the European Alps.

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 Dm value is smaller and the Nw 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 Ze−SR relationship fitted in this paper is more consistent with the Ze−SR relationship of dry snow in Nanjing, China. The fitted ρs−Dm 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.

[ES] El cambio climático indudablemente afectará los eventos de nieve ya que se espera que la temperatura y la precipitación cambien en el futuro. Las montañas españolas se ven especialmente afectadas por esta situación, ya que el almacenamiento de nieve se concentra en periodos muy concretos del año hidrológico y juega un papel muy importante en la gestión de los recursos hídricos. En este estudio se realiza en primer lugar un análisis del comportamiento de los fenómenos complejos relacionados con la nieve en las cuatro principales regiones montañosas de España en los próximos 50 años. El modelo hidrológico ASTER se aplica utilizando como insumo básico datos de temperatura y precipitación, estimados bajo un escenario de cambio climático. Los resultados muestran diferentes cambios en los caudales máximos y promedio esperados, dependiendo de la muy diferente magnitud y signo de los cambios en la precipitación. Puede producirse un aumento de los episodios de inundación como consecuencia de una compleja relación entre los cambios en las precipitaciones y un aumento de las intensidades máximas de deshielo que oscilan entre el 2,1% en los Pirineos y el 7,4% en la Cordillera Cantábrica. Sin embargo, los patrones comunes se muestran en una menor duración de las reservas de masa de nieve, que se espera que ocurra 45 días antes para la Cordillera Cantábrica, y alrededor de 30 días para el resto de las regiones montañosas estudiadas. Los cambios observados también conducen a una disminución preocupante del efecto regulador de los fenómenos relacionados con la nieve en los ríos españoles, con una disminución de la acumulación media de nieve que oscila entre un 28% para Pirineos y Sierra Nevada y un 42% para el Sistema Central y la Cordillera Cantábrica. Se espera una disminución del caudal medio, que fluctúe desde el 2,4% en los Pirineos hasta el 7,3% en la Cordillera Cantábrica, aumentando únicamente en el Sistema Central un 4,0%, siendo necesario desarrollar nuevas medidas de adaptación al cambio climático. Por otro lado, con el fin de lograr una mejor estimación del Equivalente de agua de nieve (SWE) utilizando una red meteorológica y de profundidad de nieve (SD) económica y extensa; y que mejore la calibración del modelo hidrológico ASTER, se proporcionan nuevos modelos de regresión de densidad de nieve (SDEN). A partir del gran banco de datos de densidad de nieve (SDEN) existente para los Pirineos españoles, siendo una de las zonas más importantes y mejor monitorizadas del mundo, se evalúan modelos de regresión lineal simple y múltiple que relacionan SDEN con la intra-dependencia del tiempo anual y otros factores como la precipitación acumulada estacional, las temperaturas promedio de 7 días, la profundidad de la nieve (SD) y la elevación. Los resultados mostraron tasas de densificación similares sin mostrar un patrón espacial. La tasa de densificación para el conjunto de muestras manuales se fijó en 1,2 x 10-3 kg/L/día, muy similar al conjunto de medidas automáticas. Los resultados aumentan el conocimiento sobre SDEN en los Pirineos, aunque hay que tener en cuenta la alta variabilidad espacial encontrada. Finalmente, se estudian los efectos que el cambio climático puede tener en las inundaciones para un caso de estudio de una cuenca nival de la Cordillera Cantábrica. Usando diferentes modelos climáticos, considerando un escenario de emisiones de gases de efecto invernadero comparativamente altas (RCP8.5), con datos diarios de temperatura y precipitación entre los años 2007-2070, y comparando los resultados en términos relativos, se estiman el caudal y la variación del riesgo de inundación debido al cambio climático. En el caso concreto de Reinosa, el modelo climático MRI-CGCM3 muestra que el cambio climático provocará un aumento significativo de habitantes potenciales afectados y daños económicos por riesgo de inundaciones., [CA] El canvi climàtic afectarà indubtablement els esdeveniments de neu ja que s'espera que la temperatura i la precipitació canvien en el futur. Les muntanyes espanyoles estan especialment afectades per aquesta situació, ja que l'emmagatzematge de neu es concentra en períodes molt concrets de l'any hidrològic i juga un paper molt important en la gestió dels recursos hídrics. En aquest estudi es fa una anàlisi del comportament dels fenòmens complexos relacionats amb la neu a les quatre principals regions muntanyoses d'Espanya en els propers 50 anys. El model hidrològic ASTER s'aplica utilitzant com a insum bàsic dades de temperatura i precipitació, estimades sota un escenari de canvi climàtic. Els resultats mostren diferents canvis en els cabals màxims i la mitjana esperada, depenent de la molt diferent magnitud i signe dels canvis en la precipitació. Es pot produir un augment dels episodis d'inundació com a conseqüència d'una relació complexa entre els canvis en les precipitacions i un augment de les intensitats màximes de desglaç que oscil¿len entre el 2,1% als Pirineus i el 7,4% a la Serralada. Cantàbrica. Tot i això, els patrons comuns es mostren en una menor duració de les reserves de massa de neu, que s'espera que ocórreguen 45 dies abans per a la Serralada Cantàbrica, i al voltant de 30 dies per a la resta de les regions muntanyoses estudiades. Els canvis observats també condueixen a una disminució preocupant de l'efecte regulador dels fenòmens relacionats amb la neu als rius espanyols, amb una disminució de l'acumulació mitjana de neu que oscil¿la entre un 28% per als Pirineus i Sierra Nevada i un 42% per al Sistema Central i la Serralada Cantàbrica. S'espera una disminució del cabal mitjà, que fluctua des del 2,4% als Pirineus fins al 7,3% a la Serralada Cantàbrica, augmentant únicament al Sistema Central un 4,0%, i cal desenvolupar noves mesures d'adaptació al canvi climàtic. D'altra banda, per tal d'aconseguir una estimació millor de l'Equivalent d'aigua de neu (SWE) utilitzant una xarxa meteorològica i de profunditat de neu (SD) econòmica i extensa que condueix a millorar el calibratge del model hidrològic ASTER, es proporcionen nous models de regressió de densitat de neu (SDEN). A partir del banc de dades més significatiu de densitat de neu (SDEN) als Pirineus espanyols, i sent una de les zones més importants i millor monitorizades del mon per a mostrejos manuals in situ i mesures automàtiques, s'avaluen models de regressió lineal simple i múltiple que relacionen SDEN amb intra-dependència del temps anual i altres factors com la precipitació acumulada estacional, les temperatures mitjana de 7 dies, la profunditat de la neu (SD) i l'elevació. La precipitació estacional acumulada va presentar una influència més dominant que la precipitació diària, sent usualment el segon factor determinant de SDEN més dominant, seguit per la temperatura. Les temperatures mitjanes van mostrar el millor ajustament a SDEN. Els resultats van mostrar taxes de densificació similars, sense mostrar un patró espacial. La taxa de densificació per al conjunt de mostres manuals es va fixar en 1,2 x 10-3 kg/L/dia, molt semblant al conjunt de mesures automàtiques. Els resultats augmenten el coneixement sobre SDEN als Pirineus, encara que cal tindre en compte l'alta variabilitat espacial trobada. Finalment, s'estudien els efectes que el canvi climàtic pot tindre en les inundacions per a un cas d'estudi d'una conca nival de la Serralada Cantàbrica. Usant diferents models climàtics, considerant un escenari d'emissions de gasos d'efecte hivernacle comparativament altes (RCP8.5), amb dades diàries de temperatura i precipitació dels anys 2007-2070, i comparant els resultats en termes relatius, la taxa de flux i la variació del ri, [EN] Climate change undoubtedly will affect snow events as temperature and precipitation are expected to change in the future. Spanish mountains are especially affected by that situation since snow storage is there focused on very specific periods of the hydrological year and plays a very important role in the management of water resources. In this study, an analysis of the behaviour of the complex snow-related phenomena in the four main mountain regions of Spain in the next 50 years is conducted. The ASTER hydrological model is applied using temperature and precipitation data as basic input, estimated under a climate change scenario. Results show different changes in the maximum and average expected flows, depending on the very different magnitude and sign of changes in precipitation. An increase of flooding episodes may occur as a result of a complex relationship between changes in precipitation and an increase in maximum snowmelt intensities that range from 2.1% in the Pyrenees to 7.4% in the Cantabrian Mountains. However, common patterns are shown in a shorter duration of the snow bulk reserves, expected to occur 45 days earlier for the Cantabrian Mountains and about 30 days for the rest of the studied mountain regions. Changes observed also lead to a concerning decrease in the regulatory effect of the snow-related phenomena in the Spanish rivers, with a decrease in the average snow accumulation that ranges from about 28% for the Pyrenees and Sierra Nevada to 42% for the Central System and the Cantabrian Mountains. A decrease in average flow is expected, fluctuating from 2.4% in the Pyrenees to 7.3% in Cantabrian Mountains, only increasing in the Central System by 4.0%, making all necessary to develop new adaptation measures to climate change. To achieve a better estimation of Snow Water Equivalent (SWE) using an economical and extensive snow depth (SD) and meteorological network that leads to improving the calibration of ASTER hydrological model, new snow density (SDEN) regression models are given in this work. Based on the most significant dataset of snow density (SDEN) in the Spanish Pyrenees for on-site manual samples and automatic measurements, as one of the most important and best-monitored areas in the world, single and multiple linear regression models are evaluated that relate SDEN with intra-annual time dependence and other drivers such as the seasonal accumulated precipitation, 7-day average temperatures, snow depth (SD) and elevation. The seasonal accumulated precipitation presented a more dominant influence than daily precipitation, usually the second most dominant SDEN driver, followed by temperature. Average temperatures showed the best fitting to SDEN. The results showed similar densification rates without showing a spatial pattern. The densification rate for the set of manual samples was 1.2 x 10-3 kg/L/day, very similar to the set of automatic measurements. The results increase knowledge on SDEN in the Pyrenees, although the high spatial variability that has been found must be regarded. Finally, climate change's effects on floods are studied in a case study of a snow basin in the Cantabrian Mountains. Using different climate models, regarding a scenario of comparatively high greenhouse gas emissions (RCP8.5), with daily temperature and precipitation data from the years 2007-2070, and comparing results in relative terms, flow rate and flood risk variation due to climate change are estimated. In the specific case of Reinosa, the MRI-CGCM3 climate model shows that climate change will cause a significant increase of potentially affected inhabitants and economic damage due to flood risk

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.

In the northwest Greenland ice sheet, in-situ measurements of near-surface snow physical conditions, which can be used to estimate surface mass balance, are crucially lacking. Herein, we performed a glaciological field traverse expedition with a traditional Greenlandic dog-sled to obtain in-situ near-surface snow density and stratigraphy data in 2018 spring. We succeeded in obtaining these data from snow pits at four locations. Moreover, we observed the spatial variability of snow density at the SIGMA-A site. These data are in the easy-to-read excel format.

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 (SD) 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 SD) 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.

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.

The aim of this project was to examine if a machine learning model could be used to predict snow density from six different weather parameters. These were artificially generated snow density, air temperature, ground temperature, relative humidity, windspeed and the snow depth change. The questions asked were what parameters correlates to the snow density, what model will perform best and could this approach be a better alternative to measure snow density manually. The research was performed in the application Regression Learner in MATLAB by testing five different premade machine learning models on a dataset. The premade models were, Linear Regression, GPR Matern 5/2, SVM Medium Gaussian, Wide Neural Network and Trilayered Neural Network. Also, the project includes data collection, data cleaning, data modification, data generation, training, testing, and evaluating the models. The results show that air temperature and windspeed overall are the most important parameters and the GPR Matern 5/2 and the Wide Neural Network had the highest performance. Lastly, it was concluded that the machine learning model could be a better alternative to measuring snow density with a real sensor. Målet med detta arbete var att undersöka om en maskininlärningsmodell kunde användas för att förutse snödensitet utifrån sex olika väderparametrar. Dessa var artificiell genererad snödensitet, lufttemperatur, marktemperatur, relativ luftfuktighet, vindhastighet och snödjupsförändring. Frågeställningarna som skulle besvaras var vilka väderparametrar som korrelerar med snödensiteten, vilken eller vilka modeller som presterade bäst samt om maskininlärningsmodellen skulle kunna vara att bättre alternativ till att mäta snödensitet manuellt. Undersökningen utfördes i applikationen Regression Learner i MATLAB genom att testa fem olika förhandsgjorda modeller vilka var Linear Regression, GPR Matern 5/2, SVM Medium Gaussian, Wide neural network och Trilayered neural network. Projektet inkluderar även datainsamling, städning av data, datamodifiering, datagenerering, träning, testning och evaluering av modellerna. Resultaten visar att lufttemperaturen och vindhastigheten över lag är viktigast för modellerna och att GPR Matern 5/2 samt Wide neural network presterade bäst. Slutligen kunde man argumentera för att maskininlärningsmodellen är ett bättre alternativ till att mäta snödensitet manuellt.

Improving the estimation of snow density is a key task in current snow research. Characterization of the variability of density in time and space is essential for the estimation of water equivalent, hydroelectric power production, assessment of natural hazards (avalanches, floods, etc.). Hyperspectral imaging is proving to be a promising and reliable tool for monitoring and estimating this physical property. Indeed, the spectral reflectance of snow is partly controlled by changes in its physical properties, particularly in the near-infrared (NIR) part of the spectrum. For this purpose, several models have been designed to estimate snow density from spectral information. However, none has yet achieved significant performance. One of the major difficulties is that the relationship between snow density and spectral reflectance is non-bijective (surjective). Indeed, several reflectance amplitudes can be associated with the same density and vice versa, so the correlation between density and spectral reflectance can be very poor. To resolve this issue, a hybrid snow density estimation model based on spectral data is proposed in this work. The principle behind this model is to classify the snow density prior to its estimation by means of a specific estimator corresponding to a predetermined snow density class. These additional steps eliminate the surjective relation by converting it into three bijective relations between density and spectral reflectance. The calibration step showed that the densities included within the three classes are sensitive to different spectral regions, with R2 &gt, 0.80. The results of the cross-validation for the specific estimators were also satisfactory with R2 &gt, 0.78 and RMSE &lt, 36.36 kg m−3. The overall performance of the hybrid model (HM), when tested with independent data, demonstrated the effectiveness of using proximal NIR hyperspectral imagery to estimate snow density (R2 = NASH = 0.93).

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.

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.

Monitoring the internal structure of the snowpack is crucial for managing snow-related hazards such as snow avalanches and snowmelt floods. Recently, the availability of low cost, low power and low-profile frequency-modulated continuous-wave (FMCW) radars at 24 GHz has grown thanks to its potential use the automotive sector. This paper proposes the use of a compact and low-cost FMCW radar as an instrument for improving the study of the snowpack, delivering Snow Density and Liquid Water Content in a fast way. The radar is intended to be used as a snowpit instrument, creating density and Liquid water content (LWC) snow profiles and trying to overcome the customary density cutters (slower and operator-dependent). The theoretical equations of the principle are presented and a preliminary validation by means of a laboratory test is done using dry snow mimicking material, providing encouraging results., NO ACK.

На основании 16 пробных площадей проанализировано накопление зимних осадков под пологом древостоев различного состава и под линиями электропередач в условиях подзоны южной тайги Урала. Данные о высоте и плотности снежного покрова получены на основе снегомерной съемки, выполненной в конце марта, т.е. при максимальной высоте снежного покрова. При проведении исследований использовались снегомерная рейка и весовой снегомер ВС-43. Установлено, что максимальный запас снеговой воды к моменту начала снеготаяния накапливается под пологом лиственничников с примесью сосны в составе древостоя. Значительные запасы снеговой воды накапливаются также под пологом березняков и под линиями электропередач при их расположении с севера на юг. Средняя высота снежного покрова варьируется от 33,5 до 54,9 см, что свидетельствует о защите нижних ярусов насаждения от вымерзания. В целях накопления зимних осадков в насаждениях подзоны южной тайги целесообразно формировать смешанные насаждения с примесью к сосне лиственницы и березы. Указанные насаждения в лесопарках будут способствовать не только повышению устойчивости и рекреационной привлекательности насаждений, но и обеспечат максимальное накопление зимних осадков. Последнее будет способствовать накоплению грунтовых вод и водности реки Исеть., The study conducts an analysis of the accumulation of winter precipitation under the canopy of stands of various compositions and on power lines in 16 sample areas in the southern taiga subzone of the Urals. The data on the height and density of the snow cover were obtained on the basis of a snow measurement survey carried out at the end of March, i.e. at the maximum height of the snow cover. The authors of the study used a snow measuring rail and a weight snow meter VS-43. The research establishes that the maximum supply of snow water at the time of the beginning of snowmelt accumulates under the canopy of larch trees with an admixture of pine in the composition of the stands. Significant reserves of snow water also accumulate under the canopy of birch forests and on power lines when they are located from north to south. The average height of the snow cover varies from 33.5 to 54.9 cm, which indicates the protection of the lower layers of vegetation from freezing. In order to accumulate winter precipitation in the stands of the southern taiga subzone, it is advisable to form mixed plantings with an admixture of larch and birch to pine. These plantings in forest parks will not only increase the stability and recreational attractiveness of the stands but also ensure the maximum accumulation of winter precipitation. The latter, in turn, will contribute to the accumulation of groundwater and the water Level of the Iset river., Международный научно-исследовательский журнал, Выпуск 7 (109) 2021, Pages 128-131

The use of unmanned aerial vehicle (UAV)-mounted radar for obtaining snowpack parameters has seen considerable advances over recent years. However, a robust method of snow density estimation still needs further development. The objective of this work is to develop a method to reliably and remotely estimate snow water equivalent (SWE) using UAV-mounted radar and to perform initial field experiments. In this paper, we present an improved scheme for measuring SWE using ultra-wide-band (UWB) (0.7 to 4.5 GHz) pseudo-noise radar on a moving UAV, which is based on airborne snow depth and density measurements from the same platform. The scheme involves autofocusing procedures with the frequency–wavenumber (F–K) migration algorithm combined with the Dix equation for layered media in addition to altitude correction of the flying platform. Initial results from field experiments show high repeatability (R > 0.92) for depth measurements up to 5.5 m, and good agreement with Monte Carlo simulations for the statistical spread of snow density estimates with standard deviation of 0.108 g/cm3. This paper also outlines needed system improvements to increase the accuracy of a snow density estimator based on an F–K migration technique.

[EN] Spanish latitudes and meteorological conditions cause the snow phenomena to mainly take place in mountainous areas, playing a key role in water resource management, with the Pyrenees as one of the most important and best monitored areas. Based on the most significant dataset of snow density (SDEN) in the Spanish Pyrenees for on-site manual samples and automatic measurements, in this study, single and multiple linear regression models are evaluated that relate SDEN with intra-annual time dependence and other drivers such as the seasonal accumulated precipitation, 7-day average temperatures, snow depth (SD) and elevation. The seasonal accumulated precipitation presented a more dominant influence than daily precipitation, usually being the second most dominant SDEN driver, followed by temperature. Average temperatures showed the best fitting to SDEN. The results showed similar densification rates ranging widely from 0.7 x 10(3) kg/L/day to 2 x 10(3) kg/L/day without showing a spatial pattern. The densification rate for the set of manual samples was set to 1.2 kg/L/day, very similar to the set of automatic measurements (1.3 kg/L/day). The results increase knowledge on SDEN in the Pyrenees. The SDEN regression models that are given in this work may allow us, in the future, to estimate SDEN, and consequently Snow Water Equivalent (SWE), using an economical and extensive SD and meteorological network, although the high spatial variability that has been found must be regarded. Estimating a relationship between SDEN and several climate drivers enables us to take into account the impact of climate variability on SDEN., The authors acknowledge F. Pastor and F. J. Sanchez (Spanish Ministry for Ecological Transition and the Demographic Challenge); M. L. Moreno (EbroWater Authority); the Ebro Water Authority and field engineers A. Pedrero-Munoz and M. Motes (SPESA Ingenieria). The authors fully acknowledge the financial support provided by the Department of Geological and Geotechnical Engineering of the UPV.

В обзоре проводится сравнительный анализ физических и физико-географических параметров на территории района “Старое Марьино” г. Москвы, с условно эталонной территорией участком национального парка “Угра”. Рассматриваются основные причины, влияющие на вариацию указанных параметров в пределах района “Старое Марьино”., The review provides a comparative analysis of physical and physical-geographical parameters on the territory of the "Staroe Maryino" district of Moscow, with a conditionally reference territory-a section of the national park “Ugra". The main reasons affecting the variation of these parameters within the “Old Maryino " district are considered.

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. © 2021, Saudi Society for Geosciences., European Space Agency, ESA: 39890; Aksaray Üniversitesi: 2017-010, This study was supported by Aksaray University Scientific Research Projects Coordination Unit in 2017-010 and European Space Agency under the project number 39890 entitled “Calculation of Snow Depth and Volume from Radar Images; Erciyes Mountain Example.”

Climate change affects snowpack properties indirectly through the greater need for artificial snow production for ski centers. The seasonal snowpacks at five ski centers in Central Slovakia were examined over the course of three winter seasons to identify and compare the seasonal development and inter-seasonal and spatial variability of depth average snow density of ski piste snow and uncompacted natural snow. The spatial variability in the ski piste snow density was analyzed in relation to the snow depth and snow lances at the Ko&scaron, &uacute, tka ski center using GIS. A special snow tube for high-density snowpack sampling was developed (named the MM snow tube) and tested against the commonly used VS-43 snow tube. Measurements showed that the MM snow tube was constructed appropriately and had comparable precision. Significant differences in mean snow density were identified for the studied snow types. The similar rates of increase for the densities of the ski piste snow and uncompacted natural snow suggested that the key density differences stem from the artificial (machine-made) versus natural snow versus processes after and not densification due to snow grooming machines and skiers, which was relevant only for ski piste snow. The ski piste snow density increased on slope with decreasing snow depth (18 kg/m&sup3, per each 10 cm), while snow depth decreased 2 cm per each meter from the center of snow lances. Mean three seasons maximal measured density of ski piste snow was 917 &plusmn, 58 kg/m&sup3, the density of ice. This study increases the understanding of the snowpack development processes in a manipulated mountainous environment through examinations of temporal and spatial variability in snow densities and an investigation into the development of natural and ski piste snow densities over the winter season.

Monitoring the evolution of snow on the ground and lake ice&mdash, two of the most important components of the changing northern environment&mdash, is essential. In this paper, we describe a lightweight, compact and autonomous 24 GHz frequency-modulated continuous-wave (FMCW) radar system for freshwater ice thickness and snow mass (snow water equivalent, SWE) measurements. Although FMCW radars have a long-established history, the novelty of this research lies in that we take advantage the availability of a new generation of low cost and low power requirement units that facilitates the monitoring of snow and ice at remote locations. Test performance (accuracy and limitations) is presented for five different applications, all using an automatic operating mode with improved signal processing: (1) In situ lake ice thickness measurements giving 2 cm accuracy up to &asymp, 1 m ice thickness and a radar resolution of 4 cm, (2) remotely piloted aircraft-based lake ice thickness from low-altitude flight at 5 m, (3) in situ dry SWE measurements based on known snow depth, giving 13% accuracy (RMSE 20%) over boreal forest, subarctic taiga and Arctic tundra, with a measurement capability of up to 3 m in snowpack thickness, (4) continuous monitoring of surface snow density under particular Antarctic conditions, (5) continuous SWE monitoring through the winter with a synchronized and collocated snow depth sensor (ultrasonic or LiDAR sensor), giving 13.5% bias and 25 mm root mean square difference (RMSD) (10%) for dry snow. The need for detection processing for wet snow, which strongly absorbs radar signals, is discussed. An appendix provides 24 GHz simulated effective refractive index and penetration depth as a function of a wide range of density, temperature and wetness for ice and snow.

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.

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.

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.

Самым экологически чистым решением по утилизации снега на территориях крупных городов является использование тепловых установок. Для проведения инженерных расчетов геометрических и кинематических параметров таких установок необходимо выявление динамики рабочего процесса в зависимости от основных факторов. Изложены методика и результаты экспериментальных исследований, проведенных с целью определения закономерностей, необходимых для последующего физического моделирования механизма плавления., The most environmentally friendly solution for snow disposal in the territories of large cities is the use of heat installations. To carry out engineering calculations of the geometric and kinematic parameters of such facilities, it is necessary to identify the dynamics of the wor king process depending on the main factors. The article describes the methodology and results of experimental studies conducted to determine the patterns necessary for the subsequent physical modeling of the melting mechanism., №7 (2019)

This paper presents examples of the application of the TDR (Time-Domain Reflectometry) sensor in terramechanical research. Examples include the determination of soil moisture content during off-road vehicle mobility tests, the determination of snow density before and after the wheeling of a snow grooming machine and an airplane, as well as the monitoring of turf moisture on a grassy airfield for the analysis and prediction of safe and efficient flight operations (takeoff and landing). A handheld TDR meter was used in these experiments. Soil moisture data were correlated with the vehicle mobility index and a simple model for this correlation was derived. Using grassy airfield research, soil moisture data were related to meteorological impacts (precipitation, sunlight, etc.). Generally, it was concluded that the TDR meter, in its handheld version, was a useful tool in the performed research, but a field sensor that operates autonomically would be an optimal solution for the subject applications.

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.

本研究の目的は，融雪に伴う土砂災害危険度評価を行うことであり，合わせてそのための実用的な融雪量推定法を提案するものである．近年，日本の至る所で大雨に伴う土砂災害が発生しているが，北海道のような積雪地域では，融雪を誘因とする土砂災害も頻発している．本研究では，積雪重量計による観測データに基づき積雪密度の設定に改良を加えた雪面低下法の他に，熱収支法，デグリーアワー法の３つの手法を用いて融雪量の推定を行った．また，それらによって土壌雨量指数を推算し，スネーク曲線で土砂災害発生危険度を評価できるかを検証した．この結果，土砂災害発生危険度の評価において熱収支法の優位性を確認するとともに，実際の道路管理等の観点からの雪面低下法の改良について提案した．, This study aims to conduct risk assessments for snowmelt-related landslide disasters and to propose a practical snowmelt estimation method that accurately assesses the landslide disaster risk. In recent years, landslide disasters have occurred during heavy rainfall events in many areas in Japan. In snowy areas such as Hokkaido, landslide disasters caused by snowmelt have frequently occurred. In this study, snowmelt was estimated by using three methods. One was a snow surface lowering measurement method, which was improved in its setting of snow cover density based on data gathered by using snow weight meters. Another was a heat balance method and the third was a degree-hour method. We estimated the soil water index by using the above three methods and examined whether it was possible to use snake curves to assess the risk of landslide occurrence. The examination clarified that landslide disaster risk can be assessed by using the snow surface lowering measurement method, which has been used in actual road management, when the snow cover density is set by using the method proposed in this study. As a result, we confirmed the superiority of the heat balance method in evaluating the risk of landslide disasters, and proposed improvement of the snow surface lowering method from the viewpoint of actual road management.

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.

We have performed extensive georadar surveys for mapping the snow depth in the basin of Breuil-Cervinia (Aosta Valley) in the Italian Alps, close to the Matterhorn. More than 9 km of georadar profiles were acquired in April 2008 and 15 km in April 2009, distributed on an hydrological basin of about 12 km 2 . Radar surveys were carried out partially on the iced area of Ventina glacier at elevation higher than 3000 m a.s.l. and partially at lower elevation (2500 m–3000 m) on the gently slopes of the basin where the winter snow accumulated directly on the ground surface. The snow distribution on the basin, at the end of the season, could vary significantly according to the elevation range, exposition and ground morphology. In small catchment the snow depth reached 6–7 m. At higher elevation, on the glacier, a more homogeneous distribution is usually observed. A descriptive statistical analysis of the dataset is discussed to demonstrate the high spatial variability of the snow depth distribution in the area. The probability distribution of the snow depth fits the gamma distribution with a good correlation. Instead we didn't found any satisfactory relationship of the snow depth with the main morphological parameters of the terrain (elevation, slope, curvature). This suggests that the snow distribution, at the end of the winter season, is mainly conditioned by the transport phenomena and re-distribution of the wind action. The comparison of the results of georadar surveys with the hand probe measurements points out the low accuracy of the snow depth estimate in the area by using conventional hand probing approach only, encouraging to develop technology for fast and accurate mapping of the snow depth at the scale of basin.

Рассмотрены результаты экспериментальных исследований коэффициента теплопроводности снега на арх. Шпицберген (Свальбард). Теплопроводность снега и его твердость зависят от контактов между кристаллами льда. Для слоев снежного покрова различной твердости получены зависимости теплопроводности снега от плотности. Это позволяет оценить коэффициент теплопроводности и определить термическое сопротивление снежного покрова в полевых условиях путем измерения плотности и твердости различных слоев снега., The results of experimental studies of the coefficient of thermal conductivity of snow on the Svalbard archipelago under conditions of natural occurrence, are considered. The thermal conductivity of snow and its hardness depend on the contacts among the ice crystals. For layers of snow cover of different hardness, the dependences of the thermal conductivity of snow on density, are obtained. The obtained formulas for the coefficient of thermal conductivity of very soft, soft, medium and hard snow (according to the international classification for seasonally falling snow) are compared with the data of other studies. They showed that with a snow density of 200 400 kg/m3, the obtained dependences cover the main range of variation in the thermal conductivity of snow. This makes it possible to estimate the thermal conductivity coefficient and determine the thermal resistance of the snow cover in the field conditions by measuring the density and hardness of various layers of snow., №3 (2019)

This paper presents one-dimensional numerical model for snowmelt/accumulation simulations, based on the equation of heat energy. It is assumed that the snow column is homogeneous at the current time step; however, its characteristics such as snow density and thermal conductivity are treated as functions of time. The equation of heat energy for snow column is solved using the implicit finite difference method. The incoming energy at the snow surface includes the following parts: conduction, convection, radiation and the raindrop energy. Along with the snow melting process, the model includes a model for snow accumulation. The Euler method for the numerical integration of the balance equation is utilized in the proposed model. The model applicability is demonstrated at the meteorological station Zlatibor, located in the western region of Serbia at 1,028 meters above sea level (m.a.s.l.) Simulation results of snowmelt/accumulation suggest that the proposed model achieved better agreement with observed data in comparison with the temperature index method. The proposed method may be utilized as part of a deterministic hydrological model in order to improve short and long term predictions of possible flood events.

During the 45 th –57 th Russian Antarctic Expeditions (1999–2012) an intensive program of glaciological and geophysical observations have been carried out in the region between the subglacial Lake Vostok and the Ridge B, Сentral Antarctica. In this work we present the instrumentally obtained snow accumulation data along the northern ( NVFL ) and southern ( VFL ) ice flow lines passing through the Lake Vostok. The mean accumulation rate at the NVFL and VFL profiles is 29–37 kg m -2 yr -1 and 21–24 kg m -2 yr -1 , respectively. Thus, this region is characterized by a strong latitudinal gradient of the snow accumulation rate, while the longitudinal one is an order of magnitude less. The spatial distribution of the accumulation rate along the VFL is characterized by a sharp variability attributed to the snow re-deposition due to the wind interplay with the local glacier surface slope anomalies. The new data on the stable water isotope concentration ( 18 O) along the VFL profile are also presented.

Spatial variations of avalanche formation parameters in the relation with the explosive mass and its location have been experimentally studied. The charges were installed in five locations: on the ground, in the snow layer at a height of 0.6 m above the ground, on the snow surface, at a height of 1 m and 2 m above the snow surface. Mass of explosive was 0.5, 1, 2 and 4 kg. The experiments were in March 2012 on the Suoluayv plateau (600 m a.s.l.) in Khibiny Mountains. Studies have shown that at explosion sites the disturbance of snow structure, its density and shear strength occurs throughout the whole depth of snow cover, depending on the structure and density of snow layers as well as the explosive mass and its location.

This paper presents results of field investigations of coefficient of efficient thermal conductivity of snow with different structures and densities. Observations were performed in spring of 2013 in the vicinity of meteorological station Barentsburg. The data obtained were processed by means of the Fourier technique that allowed deriving relationship between thermal conductivity and snow temperature in regimes of cooling and warming of the snow cover surface. It was found that coefficient of efficient thermal conductivity increases with rising of the snow temperature in the regime of the snow surface cooling, and it does decrease under regime of warming. This can be possibly caused by the following: under the snow surface warming a water vapor flux moves inward, and when the snow temperature drops the water vapor condensation grows. That results in additional temperature rise and creates effect of growth of thermal conductivity at the lower temperature. When the snow surface cools down this effect is absent, and when the snow temperature drops a contribution of water vapor diffusion into thermal conductivity also decreases, and as a result, the coefficient drops too. Average value of coefficient of efficient thermal conductivity of the depth hoar with density of 280 kg/m 3 is 0.12 W/(mK) that in 3–4 times smaller than the same coefficient of granular and frozen together snow with density of 370–390 kg/m 3 .

Ground permittivity and snow density retrievals for the “snow-free period”, “cold winter period”, and “early spring period” are performed using the experimental L-band radiometry data from the winter 2016/2017 campaign at the Davos-Laret Remote Sensing Field Laboratory. The performance of the single-angle and multi-angle two-parameter retrieval algorithms employed during each of the aforementioned three periods is assessed using in-situ measured ground permittivity and snow density. Additionally, a synthetic sensitivity analysis is conducted that studies melting effects on the retrievals in the form of two types of “geophysical noise” (snow liquid water and footprint-dependent ground permittivity). Experimental and synthetic analyses show that both types of investigated “geophysical noise” noticeably disturb the retrievals and result in an increased correlation between them. The strength of this correlation is successfully used as a quality-indicator flag for the purpose of filtering out highly correlated ground permittivity and snow density retrievals. It is demonstrated that this filtering significantly improves the accuracy of both ground permittivity and snow density retrievals compared to corresponding reference in-situ data. Experimental and synthetic retrievals are performed in retrieval modes RM = “H”, “V”, and “HV”, where brightness temperatures from polarizations p = H, p = V, or both p = H and V are used, respectively, in the retrieval procedure. Our analysis shows that retrievals for RM = “V” are predominantly least prone to the investigated “geophysical noise”. The presented experimental results indicate that retrievals match in-situ observations best for the “snow-free period” and the “cold winter period” when “geophysical noise” is at minimum.

Passive microwave measurements from space are known to be sensitive to the freeze/thaw (F/T) state of the land surface. These measurements are at a coarse spatial resolution (~15–50 km) and the spatial variability of the microwave emissions within a pixel can have important effects on the interpretation of the signal. An L-band ground-based microwave radiometer campaign was conducted in the Canadian Prairies during winter 2014–2015 to examine the spatial variability of surface emissions during frozen and thawed periods. Seven different sites within the Kenaston soil monitoring network were sampled five times between October 2014 and April 2015 with a mobile ground-based L-band radiometer system at approximately monthly intervals. The radiometer measurements showed that in a seemingly homogenous prairie landscape, the spatial variability of brightness temperature (TB) is non-negligible during both frozen and unfrozen soil conditions. Under frozen soil conditions, TB was negatively correlated with soil permittivity (εG). This correlation was related to soil moisture conditions before the main freezing event, showing that the soil ice volumetric content at least partly affects TB. However, because of the effect of snow on L-Band emission, the correlation between TB and εG decreased with snow accumulation. When compared to satellite measurements, the average TB of the seven plots were well correlated with the Soil Moisture Ocean Salinity (SMOS) TB with a root mean square difference of 8.1 K and consistent representation of the strong F/T signal (i.e., TB increases and decreases when soil freezing and thawing, respectively). This study allows better quantitative understanding of the spatial variability in L-Band emissions related to landscape F/T, and will help the calibration and validation of satellite-based F/T retrieval algorithms.

The paper substantiates a necessity to create maps of snow cover characteristics in Belarus. The designed maps can be used by engineers to forecast spring floods on the rivers of Belarus and to assign snow load limits imposed on buildings and structures.

Climate change is the main factor that induces alterations in the hydrological cycle. In this context, mountains represent the first indicators of climate change, because they respond rapidly and intensely to climatic and environmental modifications. Obtaining reliable scenarios on water resources availability is a prerequisite to planning management measures as a matter of fact snowfall and the resulting seasonal snow cover represent an important source of water, including surface and subsurface flows.The applicability of terrestrial laser scanning (TLS) techniques to measure snow depth and snow cover was investigated in an area located in the Mascognaz Valley (Ayas municipality, Regione Autonoma Valle d'Aosta, Italy). First aim of this job is recognize the accumulation areas from melting areas through the generation of high dense digital snow elevation model. In this way is possible better understand the snowmelt process in mountain areas that contributes widely to the groundwater recharge. Secondly, the TLS was also used to analyze a snow cross section in order to define a correlation between the reflectance of laser signal and the density of snowpack. In fact, the snow density measure represents a crucial information for the snow water equivalent (S.W.E.) evaluation. Finally, analyzing the spatial snow-depth distribution is important to individuate the potential avalanche-starting zones.

14 páginas, 9 figuras, [ES] Determinar con exactitud el espesor y densidad del manto de nieve resulta necesario para conocer su contenido de agua. La medición de la densidad resulta mucho más difícil y requiere más tiempo que la estimación de su espesor. En este trabajo se evalúa la variabilidad espacial de la densidad de la nieve en un tramo de 5.4 kilómetros en la cabecera del río Ésera (Pirineos Españoles). El sistema tradicional con un muestreador de nieve (snow tube sampler) es comparado con un sistema más laborioso, pero preciso, basado en la medición estratificada de la densidad a lo largo de toda la profundidad del manto de nieve. El primer método se basa en la extracción de una muestra de todo el manto de nieve con un tubo, y derivar su densidad mediante el conocimiento de el espesor y peso de la muestra. El segundo consiste en extraer muestras de un I-L de volumen de nieve cada diez centímetros, desde la superficie nevada hasta el suelo. A mediados de enero, la variabilidad del manto de nieve no resulta sistemática espacialmente, y sólo puede ser explicada en las zonas de menor altura, donde el manto de nieve está próximo a condiciones de fusión, dada su proximidad a condiciones isotermas (próximo a los O°C). A finales de abril, los puntos de medición situados a menor altura también mostraron la mayor densidad., [IN] An accurate assessment of snow depth and snow density is essential to determine that amount of water sto red in the snowpack, i. e., snow water equivalent (SWE). The measurement of snow density is much more difficult and time consuming than snow depth. The variability in snow density is evaluated for a 5.4-km stretch of the Rio Esera headwaters in the Spanish Pyrenees Mountains. The traditional snow tube method is compared to the more labourintensive but accurate snow pit method. The former method measures snow depth and extracts a snow core that is weighed. The latter method uses a wedge cutter to extract a l-L snow sample to estimate density at lO-cm intervals through the snowpack. The variability in snowpack density is not systematic and can only be explained at lower elevation when the snowpack is known to be melting, as identifY by an isothermal snowpack at zero degrees Celsius. This occurred during a mid-January survey. A late-April survey showed that these lower elevatíon sites were still more dense., This work was supported by research projects CGL2006-11619/HID, CGL2008- O 1189/BTE, and CGL2008-1083/CLI, fmanced by the Spanish Commission of Science and Techno10gy, and FEDER, EUROGEOSS (FP7-ENV-2008-1-226487), and the project "Las sequías climáticas en la cuenca del Ebro y su respuesta hidrológica" and "La nieve en el Pirineo aragonés: distribución espacial y su respuesta a las condiciones climáticas", financed by "Obra Social La Caixa" and the Aragón Government, and "Programa de grupos de investigación consolidados", fmanced by the Aragón Government. Data collected was assisted by Gonzalo López, Pablo Corella and Mario Morellón; their efforts are acknow1edged with thanks.

Increased temperatures, changed snow conditions, increasing demand for skiing and other winter sports lead to increased demand and production of artificial snow. Besides the positive aspects of artificial snow, it is important to understand potentially negative effects of artificial snow on the environment and vegetation. This study investigated the differences between artificial snow and natural snow in two adjacent ski slopes. This was done by measuring and comparing the snow depth, duration of the snow cover, snow density, and ground vegetation between the slopes. Snow depth and density showed differences between artificial snow and natural snow. The artificial snow cover stayed about 11 days longer than the natural snow. A few variations in the vegetation were also found; earlier flowering and more species and colors in the natural snow slope compared to in the slope using artificial snow. Therefore, it is clear that there is a difference between artificial snow and natural snow, and that this results in effects on the environment, but it is difficult to assess to what extent and how large the negative impact is. For more general conclusions, I think it is important to study the vegetation under artificial snow during a longer period and in several areas.

The paper evaluates the results of a 6-year-monitoring of the eco-hydrological influence of Norway spruce (Picea abies (L.) Karst.) and European beech (Fagus silvatica L.) forest stands on the hydro-physical properties of snow cover. The experiment was carried out in the artificially regenerated 20-25-year-old forest stands approaching the pole timber stage in the middle mountain region of the Polana Mts. - Biosphere reserve situated at about 600 m a.s.l. during the period of maximum snow supply in winters of years 2004 - -2009. Forest canopy plays a decisive role at both the snow cover duration and spring snow melting and runoff generation. A spruce stand is the poorest of snow at the beginning of winter. High interception of spruce canopy hampers the throughfall of snow to soil. During the same period, the soil surface of a beech stand accumulates greater amount of snow. However, a spruce stand accumulates snow by creating snow heaps during the periods of maximum snow cumulation and stand´s microclimate slows down snow melting. These processes are in detail discussed in the paper. The forest stands of the whole biosphere reserve slow down to a significant extent both the snow cover melting and the spring runoff of the whole watershed.

A nonlinear least squares fitting algorithm is used to estimate both snow depth and snow density for a snow-layer above a flat ground reflector. The product of these two quantities, snow depth and density, provides an estimate of the snow water equivalent. The input to this algorithm is a simple ray model that includes a speculary reflected signal along with a direct signal. These signals are transmitted from the global positioning system satellites at 1.57542 GHz with right-hand circularly polarization. The elevation angles of interest at the GPS receiving antenna are between 5° and 30°. The results from this nonlinear algorithm show potential for inferring snow water equivalent using GPS multipath signals.

International audience; We investigate the problem of balancing model complexity and input data requirements in snow hydrology. For this purpose, we analyze the performance of two models of different complexity in estimating variables of interest in snow hydrology applications. These are snow depth, bulk snow density, snow water equivalent and snowmelt run-off. We quantify the differences between data and model prediction using 18 years of measurements from an experimental site in the French Alps (Col de Porte, 1325 m AMSL). The models involved in this comparison are a one-layer temperature-index model (HyS) and a multilayer model (Crocus). Results show that the expected loss in performance in the one-layer temperature-index model with respect to the multilayer model is low when considering snow depth, snow water equivalent and bulk snow density. As for run-off, the comparison returns less clear indications for identification of a balance. In particular, differences between the models' prediction and data with an hourly resolution are higher when considering the Crocus model than the HyS model. However, Crocus is better at reproducing sub-daily cycles in this variable. In terms of daily run-off, the multilayer physically based model seems to be a better choice, while results in terms of cumulative run-off are comparable. The better reproduction of daily and sub-daily variability of run-off suggests that use of the multilayer model may be preferable for this purpose. Variation in performance is discussed as a function of both the calibration solution chosen and the time of year. Copyright © 2016 John Wiley & Sons, Ltd.

In this study, we quantified the spatiotemporal variability and trends in observations of multiple snow characteristics in High Arctic Zackenberg in Northeast Greenland through 18 years. Annual premelt snow-depth observations collected in 2005–2014 along an elevation gradient showed significant differences in snow depth between vegetation types. The seasonal snow cover was characterized by strong interannual variability in the Zackenberg region. Particularly the timing of snow-cover onset and melt, and the annual maximum accumulation, varied up to an order of magnitude between years. Hence, apart from the snow-cover fraction registered annually on 10 June, which exhibits a significant trend of -2.3% per year over the 18-year period, we found little evidence of significant trends in the observed snow-cover characteristics. Moreover, SnowModel results for the Zackenberg region confirmed that the pronounced interannual variability in snow precipitations has persisted in this High Arctic setting sinc...

Analysis of snowpack density in mountain catchments in Krušné Mountains and Šumava Mountains Abstract: Mountain catchments are very important sources of fresh water. These areas are characterized with long period of snow cover. The thesis presents analyses of snow density in mountain catchments, focusing mainly on factors and processes influencing its distribution. The data of data recorded in experimental catchments in the Krušné Mountains and the Šumava Mountains through 2010 - 2014 are compared with findings given in different scientific articles and studies. The thesis examines the influence of several factors on snow density. It is first of all the influence of snow depth, as well as the influence of vegetation, the development of density in time, and the development of snowpack density in relation to the snow water equivalent. The results of the research show that snowpack density gradually increases with snow depth. The highest density occurs on open plains and in damaged forests. Density also increases with time. The highest density can be observed towards the end of winter, in March and April. Keywords: snow density, snow depth, snow cover, mountain catchments

積雪の硬度は，表層雪崩のすべり面となる弱層を検出する上で目安になるため，雪崩災害調査などで迅速かつ細かく簡便に測定できる方法の必要性が高い．そこで新しい積雪硬度計として提案したデジタル式荷重測定器（プッシュゲージ）について，測定方法の検討を行った．プッシュゲージは円板状のアタッチメントを積雪断面に等速で1～2cm押し込み，抵抗力の最大値を測定する．測定値を既存の木下式高度計やラムゾンデと比較した結果，プッシュゲージは積雪用高度計として使用可能であるのみならず，特に薄くて高度の小さな弱層の検出に適していることが示された．アタッチメントの径が小さい場合には測定値が大きくなるが，直径を約8mm以上にすると，大きさによらず一定の硬度が得られるとみなせることがわかった．また雪質や粒径を限定した場合に乾き雪の硬度が密度の4乗に比例するという関係式が，地域によらず成り立つことが確かめられた．, A new method of snow-hardness measurement using a handy-type digital load-gauge (push-gauge) is demonstrated. The push-gauge can measure the maximum strength of a snow layer when it is destroyed by the circular plate attachment pushed horizontally into the side of the pit. Snow-hardness measured with the push-gauge was compared with measurements with a rammsonde and Kinosita-type hardness meter. The measured values agreed with each other, confirming that the push-gauge can be used as a snow-hardness meter. It is especially useful to detect thin weak layers because it can measure snow-hardness at very small space intervals. The measured hardness can be regarded as constant if the diameter of circular plate is about 8mm or more. It was confirmed by data in various regions that the hardness of a fine-grained compacted snow layer with grain-sizes less than 0.5mm is highly correlated with the foourth power of the snow density.

This paper deals particularly with the sensitivity of the wet snow backscattering coefficient on density change. The presented backscattering model is based on the approach used in the dry snow analysis [1], appropriately modified to account for the increased dielectric contrast caused by liquid water presence. It encircles our undertaking of simulating and analysing snow backscattering using fundamental scattering theories (IEM-B, QCA, QCA-CP). The wet snow parameters are chosen according to the area of the particular interest - the French Alps, while the choice of the SAR sensor parameters (frequency, polarization) is primarily conditioned by the initially settled goal - reaching qualitative conclusions concerning wet snow backscattering mechanism. Based on simulation results, we state the dominance of the snow pack surface backscattering component, causing the backscattering to be directly proportional to the volumetric liquid water content. This result is confirmed by the performed in situ measurements. We illustrate as well the decrease of this effect with the increase in operating frequency.

Snow load is one of the important climatic elements that are part of the technical building regulations, together with minimum and maximum temperatures and wind. In particular, the maximum snow load at the ground for the 50 years return period has to be estimated and map has to be supplied as an appendix to the national technical building regulations. The method for the estimation of this parameter at the station locations is presented, following with the geostatistical mapping procedure for estimating this parameter for the whole Croatian territory. Snow load is defined as a product of the snow density, snow depth and the gravitational acceleration. It is often considered as the largest design load for the roof system of the buildings. Hence, careful estimation of the snow load values is important in order to avoid both unnecessary construction cost but also the risk of roof failure (Luna et al. 2003). Although the snow depths are measured at large number of meteorological station, the snow loads are not easy to estimate because of small number of snow density measurements. To overcome this, snow density was fitted to snow depth data using linear regression according to the daily pairs of values from the 13 meteorological stations. Since snow density changes with elevations and seasons, the stations have been divided to two subsets according to the station elevations (lower than 600 m, and 600-1000 m height) and the regression equations are built also on monthly basis (Jonas et al. 2009). Including the highest Zavižan station (altitude: 1594 m) to the group of higher stations, downgraded the estimations by shifting the densities of the rest of the station in the group to to high values. So the Zavižan station has been used to model the snow densities only for one station that is higher than 1000 m. These modelling results allowed for estimating the snow load on the 98 low elevation stations and seven higher elevation stations based on the monthly maximum snow depths and the estimated snow densities. Based on the monthly maximum snow loads the series of maximum annual snow loads can be calculated for 118 locations. Maximum estimated snow load is 2.6 kNm-2 for lower elevation stations and 4.0 kNm-2 for higher elevation stations. Maximum measured snow load of 11.6 kNm-2 is from Zavižan station. Based on the occasional snow episodes on the coastal stations, the estimated snow load is below 0.3 kNm-2 what is consistent with results from other Mediterranean countries. This series of annual maximum snow loads are the basis for the estimation of the annual maximum snow load values for the 50 years return period, by means of the generalized extreme value (GEV) theory. The parameters of the GEV distributions have been estimated with VGAM package that fits many models and distributions by maximum likelihood estimation and is available in R open-source environment for statistical computing and visualisation. For most of the stations, data are close to the Gumbel family of the GEV distributions. Finally, the estimated annual maximum snow load values for the 50 years return period on 118 stations are the input for the geostatistical procedure of mapping this parameter for the 56 000 km2 of Croatian territory. Further on, snow water equivalent, which is an important parameter for the snow hydrological evaluations, can be calculated from the estimated snow load map, by dividing the snow load with gravitational acceleration.