Total Snow Cover Research Articles

Spatiotemporal Variations in Snow Cover on the Tibetan Plateau from 2003 to 2020

The variations in snow cover on the Tibetan Plateau play a pivotal role in comprehending climate change patterns and governing hydrological processes within the region. This study leverages daily snow cover data and the NASA Digital Elevation Model (DEM) from 2003 to 2020 to analyze spatiotemporal snow cover days and assess their responsiveness to climatic shifts by integrating meteorological data. The results reveal significant spatial heterogeneity in snow cover across the Plateau, with a slight decreasing trend in annual average snow cover duration. Snow cover is predominantly observed during the spring and winter seasons, constituting approximately 32% of the total snow cover days annually. The onset and cessation of snow cover occur within a range of 120–220 days. Additionally, an increasing trend in snow cover duration below 5000 m altitude was observed, in addition to a decreasing trend above 5000 m altitude. Sub-basin analysis delineates the Tarim River Basin as exhibiting the lengthiest average annual snow cover duration of 83 days, while the Yellow River Basin records the shortest duration of 31 days. The decreasing trend in snow cover duration closely aligns with climate warming trends, characterized by a warming rate of 0.17 ± 0.54 °C per decade, coupled with a concurrent increase in precipitation at a rate of 3.09 ± 3.81 mm per year. Temperature exerts a more pronounced influence on annual snow cover duration variation compared to precipitation, as evidenced by a strong negative correlation (CC = −0.67). This study significantly augments the comprehension of hydrological cycle dynamics on the Tibetan Plateau, furnishing essential insights for informed decision-making in water resource management and ecological conservation efforts.

An empirical model to calculate snow depth from daily snow water equivalent: SWE2HS 1.0

Abstract. Many methods exist to model snow densification in order to calculate the depth of a single snow layer or the depth of the total snow cover from its mass. Most of these densification models need to be tightly integrated with an accumulation and melt model and need many forcing variables at high temporal resolution. However, when trying to model snow depth (HS) on climatological timescales, which is often needed for winter tourism-related applications, these preconditions can cause barriers. Often, for these types of applications, empirical snow models are used. These can estimate snow accumulation and snowmelt based on daily precipitation and temperature data only. To convert the resultant snow water equivalent (SWE) time series into snow depth, we developed the empirical model SWE2HS. SWE2HS is a multilayer densification model which uses daily snow water equivalent as sole input. A constant new snow density is assumed and densification is calculated via exponential settling functions. The maximum snow density of a single layer changes over time due to overburden and SWE losses. SWE2HS has been calibrated on a data set derived from a network of manual snow stations in Switzerland. It has been validated against independent data derived from automatic weather stations (AWSs) in the European Alps (Austria, France, Germany, Switzerland) and against withheld data from the Swiss manual observer station data set which was not used for calibration. The model fits the calibration data with root mean squared error (RMSE) of 8.4 cm, coefficient of determination (R2) of 0.97, and bias of −0.3 cm; it is able to achieve RMSE of 20.5 cm, R2 of 0.92, and bias of 2.5 cm on the validation data set from automatic weather stations and RMSE of 7.9 cm, R2 of 0.97, and bias of −0.3 cm on the validation data set from manual stations. The temporal evolution of the bulk density can be reproduced reasonably well on all three data sets. Due to its simplicity, the model can be used as post-processing tool for output of any other snow model that provides daily snow water equivalent output. Owing to its empirical nature, SWE2HS should only be used in regions with a similar snow climatology as the European Alps or has to be recalibrated for other snow climatological conditions. The SWE2HS model is available as a Python package which can be easily installed and used.

Effect of shadow on atmospheric and topographic processed NDSI values in Chenab basin, western Himalayas

The present study was undertaken in the Chenab basin, western Himalayas to explore the effect of shadow on atmospherically and topographically corrected normalized difference snow index (NDSI) values. The research was conducted using the Landsat-8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) datasets of two time periods i.e., February 2015 and February 2018. Our results suggested that total snow cover area (SCA) computed using NDSI from raw satellite images, under estimated SCA (viz. 10,938.66 and 7739.97 Km2); relative to atmospherically corrected (AC) (14,819.01 and 11,199.7 Km2) and topographically corrected (TC) (14,882.07 and 11,188.18 Km2) images during both the time periods (i.e., 2015 and 2018, respectively). This is mainly due to the non-identification of snow pixels in the shadow regions of raw imageries. Maximum SCA lies in the higher NDSI range in case of AC and TC images, unlike raw image where maximum SCA lies in lower NDSI range for both sunlit and shadow test sites. In the sunlit and shadow terrains, minimum NDSI values were found to be higher for TC images as compared to AC images, except few observations which exhibited similar values for both AC and TC images. In case of maximum NDSI values, most of the observations exhibited similar values for both AC and TC images, except few observations which depicted higher maximum NDSI value in AC images as compared to TC images in both the terrains. This observation ascertains the narrow range of NDSI values for TC images as compared to AC images, which could be attributed to reduced background reflection and low atmospheric scattering in the TC images. Comparison among shadow and sunlit snow terrains, reveals low values of minimum and maximum NDSI in the shadow site, which is ascertained by significant (p < 0.001) t-values of means difference between shadow and sunlit terrains. Moreover, snow surface temperature (SST) values computed for both the time periods, reveal low SST values for shadow sites (t-values −5.1 and − 10.57 for minimum SST, and − 9.1 and − 12.61 for maximum SST for the years 2015 and 2018, respectively) as compared to sunlit sites, and this observation validates that minimum amount of solar radiation reaches the shadow sites. Thus, it is concluded that AC and TC are helpful for delineation of snow pixels under shadow, but shadow has a significant impact on the spectral reflectance of snow, even in the AC and TC images. The influence of shadow should be taken into account for accurate estimation of physical properties and broadband albedo of the snow.

Changes in snow cover occurrence and the atmospheric circulation impact in Pozna\u0144 (Poland)

The variability of occurrence of snow cover and the impact of atmospheric circulation on the snow cover occurrence in the period 1966/1967–2019/2020 in Poznań (Poland) have been examined. The implementation of the primary study objective covers the comprehensive analysis of the winter snow and thermal conditions using various indicators. This paper is based on daily data from the years 1966–2020 concerning the winter period. Winters in Poznań are highly variable and differentiated, considering the duration of particular seasons, number of days with snow cover, mean snow cover thickness, winter snowiness coefficient, or winter severity index. Negative trends concerning days with snow cover total snow cover depth winter snowiness coefficient and winter severity index in Poznań prove statistically significant. A higher probability of occurrence of snow cover was determined during cyclonic than anticyclonic circulation. The westerly and northerly types especially favoured the occurrence of days with snow cover. The increase of snow cover was associated with the northerly inflow mainly. Westerly types of circulation caused the decrease of snow cover predominantly.

On the Value of Available MODIS and Landsat8 OLI Image Pairs for MODIS Fractional Snow Cover Mapping Based on an Artificial Neural Network

This article investigates how to select the optimal Moderate-Resolution Imaging Spectroradiometer (MODIS) and Landsat 8 OLI image pairs for MODIS fractional snow cover (FSC) mapping using an artificial neural network (ANN). Four issues are discussed, including date selection, location selection, priority of date and location, and global and regional monitoring of MODIS FSC with ANNs. We propose using the histogram quadratic distance to define the similarity between the ANN training and the target test scene, which was used to quantify the representativeness of the training samples. We use the case study of MODIS FSC mapping of North Xinjiang, China, in the 2014–2015 snow season as an example. Thirty-eight experiments were designed. The experimental results demonstrate that the ANN-based FSC estimation accuracy outperformed the MODIS FSC product, with an average RMSE of 0.17, ${R}$ exceeds 0.8, and the total snow cover area was estimated more accurately in most cases. For a target test scene, we preliminarily inferred that the best method is to develop an ANN using image pairs of another location with the highest similarity in the same acquisition time, using historical image pairs of the target scene with the highest similarity is the second choice, and using historical image pairs from another location with a high similarity is the third choice. For global- and regional-scale MODIS FSC mapping with ANNs, we formulated the strategy of initially determining a reasonable location and subsequently selecting the acquisition date of the image pairs to guarantee that the training data set represents the entire study area well.

Frequency and thickness of snow cover at the foot of the Babia Góra Massif in the winter seasons 1960/61 to 2014/15

Abstract The aim of the study was to gain knowledge about changes in the nival conditions prevailing in the lower part of the Babia Góra massif in the winter, during the period from 1960/61 to 2014/15. The performed analyses concerned the daily snow cover thickness in the winter seasons of the years 1960/61–2014/15, and were based on data recorded by a meteorological station established by the Institute of Meteorology and Water Management – National Research Institute (IMGW-PIB) in Zawoja (697 m altitude). The study was focused on the period from the first to the last day of the appearance of the snow cover in the season. Based on long-term measurements carried out in in Zawoja, it was found that the average length of the occurrence of snow cover with the thickness of ≥ 1 cm was 104 days, which showed variability in individual seasons that ranged from 47 days in 2013/14 to 145 days in 1993/94. It was noted that the period of snow cover occurrence was shortened – from 118 days in the decade 1960/61–1969/70 to 96 days in the recent decade (2000/01–2009/10). The potential duration of snow cover was on average 157 days. The snowiest months were February and January, when snow cover occurred for 81% and 75% of all the observation days, respectively. The largest thickness of snow cover was recorded during these two months, with the maximum of 178 cm (January 30, 1976). In contrast, snow cover was not observed in July, August and September. The studied multi-year period was characterised by high variability of the winter snowiness index: from 1.61 in 2013/14 to 8.00 in 1962/63. In the period 1960/61–2014/15, in Zawoja, there was observed the distinct periodicity (16–19 years) with regard to the number of days with snow cover, the average and total snow cover and the winter snowiness.

Ecogeochemical estimation of environmental pollution by monitoring data of heavy metals contamination in soil and snow cover (at the example of Vasileostrovsky district of Saint Petersburg)

An ecogeochemical assessment of the state of the environment of the Vasileostrovsky district of St. Petersburg based on the monitoring of the content of heavy metals (Pb, Zn, Cu, Cr, Ni) in snow cover and soil was carried out. It was established that in soils the average concentration coefficient of the elements under study decreases in the series Zn > Cu> Pb> Cr > Ni. Authors considers the method of estimating the spatial heterogeneity of distribution of heavy metals in urban soils within the area of the “envelope” sampling method. It was revealed that, in general, the variation within the standard “envelope” area is equal to 30 %. Authors proposed a “principle of minimizing the possible harmful effects of chemical contamination of soils” based on the measured value variations. For example, the distribution of total index of soil contamination within the Vasileostrovsky district of St. Petersburg shows that the accounting errors of sampling soil using the “envelope” sampling method associated with natural uneven areal distribution of heavy metals, can significantly affect the definition of the category of soil contamination. The difference between soil and soil contamination in the northern, central and western parts of the Vasilievsky island became more clearly visible — the latter turned out to be less contaminated. It was established that zinc determines the total snow cover contamination index, as well as in the case of soil. However, the series of concentration decrease looks different: Zn > Ni > Cu > Cr > Pb. A high positive correlation was found between the coefficients of zinc concentration in the snow cover and soil, which may indicate a constant supply of this pollutant to the components of the environment. A similar pattern was noticed for the total pollution index. The most noticeable difference was noted in the contamination of soil and snow cover by lead. For soils lead concentration determines the total pollution index. In snow lead content is at the background level. This may indicate that at present the intake of lead into the atmosphere is noticeably lower than 10 years ago.

Spatiotemporal patterns of snow cover retrieved from NOAA-AVHRR LTDR: a case study in the Tibetan Plateau, China

ABSTRACTSnow cover plays an important role in hydrological processes and seasonal water balance. Especially in the Tibetan Plateau (TP), snow cover is an important source of the Yangtze River, Yellow River and Lancang River (SRYYL), which greatly influences regional water balance. In this study, we quantified the temporal trend and spatial variation of snow cover across the TP by calibrating and developing the Advance Very High Resolution Radiometer (AVHRR) Long Term Data Record (LTDR)-derived snow cover products during 1982-2011. We also examined the relationship of snow cover with temperature and precipitation over the TP during 1982–2011. The results indicate that seasonal snow cover generally starts to accumulate from central plateau in October, while significant melting starts to occur from the southeastern plateau in May of following year. The long-term variability of snow cover is characterized by the tendency for a slight decrease in the mean snow coverage during the period of hydrological year (HY) 1982–1993 and a slight increase from HY2001 to 2011, but the total snow cover area remains relatively stable over the past 30 years. The results also show that temperature plays a critical role in controlling the snow cover days.

Improving Mountainous Snow Cover Fraction Mapping via Artificial Neural Networks Combined With MODIS and Ancillary Topographic Data

A multilayer feedforward artificial neural network (ANN) is developed for mountainous fractional snow cover (FSC) mapping. This is trained with back propagation to learn the relationship between FSC and Moderate Resolution Imaging Spectroradiometer (MODIS) products (reflectance at seven bands, normalized difference snow index, land surface temperature (LST), and FSC) and elevation. In this paper, images from Landsat Enhanced Thematic Mapper Plus (ETM+) and MODIS products from three periods are chosen to test and validate the proposed method at the Heihe River Basin. Three binary snow cover maps derived from Landsat ETM+ images are used to calculate FSC. Two of these maps are first used to train, calibrate, and test the ANN. The other independent image is used to test the generalization ability of network. Results show that the ANN can easily incorporate auxiliary information to improve the accuracy of snow cover mapping effectively. It is also capable of mapping snow cover fraction in a complicated mountainous area with considerable generalization. For the nonindependent test set, the performance evaluation results show that the improvements of ANN-based methods are apparent compared with MODIS FSC products (higher correlation coefficient, lower root-mean-square error, and more accurate total snow cover area). For the temporal/temporal-spatial independent test set, ANN-based methods perform slightly worse than the nonindependent test set, but the accuracy of the ANN methods still shows some improvement. Elevation, LST, and FSC play more important roles in the training process of the ANN. Overall, experiment 8, which integrated all input information, is approved the best in all test sets.

Application of incoherent target decomposition theorems to classify snow cover over the Himalayan region

Snow cover is an important parameter for hydrological modelling and climate change modelling. Various methods are available only for wet snow-cover mapping using conventional synthetic aperture radar (SAR) data. Total snow (wet + dry) cover mapping with SAR data is still a topical research area. Therefore, incoherent target decomposition theorems have been implemented on fully polarimetric SAR data to characterize the scattering of various targets. Further classification techniques – both unsupervised and supervised – have been applied for accurate mapping of total snow cover. For this purpose, Advanced Land Observing Satellite – phased array-type L-band SAR (ALOS–PALSAR) data (12 May 2007) have been analysed for snow classification of glaciated terrain in and around Badrinath region in Himalaya. An ALOS-Advanced Visible and Near Infrared Radiometer (AVNIR)-2 image (6 May 2007) was also used to provide assistance in the selection of different training classes. It has been found that the application of incoherent target decomposition theorems such as H/A/α and four-component scattering mechanism models are good for extracting the desired information of snow cover from fully polarimetric PALSAR data. Finally, based on these target decomposition theorems and the Wishart classifier, PALSAR data have been classified into snow or non-snow cover, and the user accuracy of snow classes was found to be better than the user accuracy of other classes. Hence, the application of incoherent target decomposition theorems with full polarimetric ALOS-PALSAR data is useful for snow-cover mapping.

Use of NOAA-AVHRR data to monitor snow cover and spring meltoff in the wildlife habitats in Jameson Land, East Greenland

NOAA-AVHRR satellite data have been used for a multi-year analysis of the seasonal variation of snow cover on the peninsula of Jameson Land located from 70°30?N to 72°N on the eastern coast of Greenland. Monitoring programmes for muskoxen and geese populations in Jameson Land wert started in the early 1980s to assess the impact of oil exploration in the area. This study was conducted to provide snow cover data to the wildlife monitoring programmes because snow cover is an important factor which can affect the numbers and distribution of muskoxen and other wildlife in the area. Snow cover mapping is based on the geometric correction and radiometric calibration of each satellite scene before the visual bands are corrected for the terrain-effects and high solar zenith angle by using a simple radiation model. The atmospheric effect on the thermal infrared bands is corrected by using the “split-window” method. An automatic filtering routine with empirically determined thresholds is used to determine cloud-and haze-covered areas before the snow cover is computed from a linear expression using albedos of bare ground and full snow cover. By combining the satellite information with ground measurements and a digital terrain model, both the spatial distribution of snow cover and the total snow cover depletion can be obtained. The method makes it possible to analyse the variation in regional and seasonal snow cover in wildlife habitats. The variation in snow cover depletion has been analysed in five subareas in the muskox area, and the results indicate that the snow cover pattern is an important factor which influences muskox calf crop and regional distribution of the muskox population. The method has proved useful for monitoring the duration of snow cover, and its application ought to facilitate the future study of wildlife habitats in extensive alpine and arctic areas.

Snow depths and grain-size relationships with relevance for passive microwave studies

The application of passive microwave radiometry to the remote sensing of snow properties is based on the ratio of emitted to scattered portions of the upwelling radiation. Increased scattering is indicative of increased snow amount, i.e. the number of snow grains present. However, scattering is also directly proportional to snow grain-size for a given snow amount. Current snow cover retrieval algorithms produce inaccurate results when snow grain-sizes are unusually large. Therefore, it is necessary to characterize snow grain-size on a regional scale (and perhaps local scale in extreme situations) in order to adjust passive microwave algorithms. Preliminary analysis indicates that: (1) algorithms are not as sensitive to the presence of large grain-sizes as the initial theory had indicated; (2) standard deviation of grain-size diameters throughout the total snow cover may often be less than 0.5 mm, thus average grain-size data may often serve to characterize the detailed stratigraphy of the total snow cover; (3) conditions in subfreezing snow which produce grain-sizes that greatly exceed a mean diameter value of 1–2 mm result from snow cover/climate relationships which can be modelled/monitored on a regional scale. A preliminary method is investigated for selecting snow retrieval algorithms according to prevailing regional-scale grain-size.

Snow depths and grain-size relationships with relevance for passive microwave studies

The application of passive microwave radiometry to the remote sensing of snow properties is based on the ratio of emitted to scattered portions of the upwelling radiation. Increased scattering is indicative of increased snow amount, i.e. the number of snow grains present. However, scattering is also directly proportional to snow grain-size for a given snow amount. Current snow cover retrieval algorithms produce inaccurate results when snow grain-sizes are unusually large. Therefore, it is necessary to characterize snow grain-size on a regional scale (and perhaps local scale in extreme situations) in order to adjust passive microwave algorithms. Preliminary analysis indicates that: (1) algorithms are not as sensitive to the presence of large grain-sizes as the initial theory had indicated; (2) standard deviation of grain-size diameters throughout the total snow cover may often be less than 0.5 mm, thus average grain-size data may often serve to characterize the detailed stratigraphy of the total snow cover; (3) conditions in subfreezing snow which produce grain-sizes that greatly exceed a mean diameter value of 1–2 mm result from snow cover/climate relationships which can be modelled/monitored on a regional scale. A preliminary method is investigated for selecting snow retrieval algorithms according to prevailing regional-scale grain-size.

日本海沿岸から脊梁山脈にかけた新雪中の主要化学組成の分布

Field work was carried out to investigate the regional characteristics of the concentration of major chemical constituents of snow cover from the coastal area of the Japan Sea to the mountain ranges of central Japan during the winter (January) of 1985. The snow cover in the interior highlands, such as Sugadaira and Takamine, was dry, and therefore the total snow cover retained the concentration of chemical constituents in the snow fall there. In contrast, in the Takada plain, a heavily snow-covered plain of the coastal area in Japan, the lower part held granular snow which contained melt water, and only the upper new snow layers retained the condition of accumulation of chemical constituents. The ratios of concentration of each of the chemical constituents in the new snow suggested that the Na+, Mg2+, and Cl- originated from sea water. The spatial distribution of the concentration of eash chemical constituent in the new snow which accumulated during one series of snowfalls under a winter monsoon situation was claritied. The concentration of chemical constituents originating in sea water decreased farther inland. This distribution was related not only to the distance from the coast but also to topography and the regional differences in cumulus development due to the topography. Excess Ca2+ and SO42- from the expected value originating in sea water were found. In particular, the distribution of excess Ca2+ showed strong regional characteristic, which suggested that there was another source of Ca2+ besides the sea water in the inland areas. Meanwhile, most of the samples contain excess SO42-, which decreases with decreasing amounts of accumulated new snow. The spatial distribution of NO3- concentration in the new snow cover in Sugadaira showed a large variation. The results suggested that the source of the nitrogen oxide was in the neighborhood of the sampling points.

Chemical changes of snow cover by melting.

Chemical changes of snow cover were studied. The concentration of chemical constituents, pH and water equivalent of snow cover were observed together with other related factors in Sapporo during the winter of 1979-80. During the melting periods, the concentration of chemical constituents of the total snow cover decreased and pH increased. During the temporary snowmelts and the early phase of the snowmelt season, only the surface snow layer melted, and showed a decreased concentration of chemical constituents. In these phases the meltwater flowed downwards in the form of a water channel flow. When the meltwater began flowing downwards in the form of a water-film flow as the snowmelt progressed, the snow particles changed into large granular snow and the concentration of the chemical constituents of the inner layers began to decrease. The earlier meltwater was considered to contain extremely large amounts of chemical constituents and had a low pH. The mechanism of the decrease in the concentration of chemical constituents in the snow cover and that of the increased concentration in the meltwater were discussed.

Snowmelt delay by oversnow travel

Oversnow vehicles compact the snowpack several inches as a result of their passage; drifting snow tends to refill these tracks to ambient level. This repeated travel and drifting result in storage of much greater water content in the snowmobile trails than in the adjacent undisturbed snowpack. A brief field study was initiated to determine the magnitude of the increase in water content and the time delay of snowmelt in an area frequently traveled by oversnow vehicles.The area chosen for study was an abandoned farm with an elevation of 700–1100 feet msl near the confluence of the Schoharie and Mohawk valleys. The area is quite hilly, and the rather steep slopes face north or east. Average snowfall in this area is about 80 in./year. During the study year approximately 120 inches fell, and there was continuous total snow cover from December 4, 1970, until the first bare spots appeared on April 1, 1971. Snowmobile travel began with the first snow and continued through the winter, the last known passage occurring after the snowstorm of April 10. No accurate log of time or number of snow vehicle traverses was maintained; however, 25–50 passages per week would be a reasonable estimate of traffic over the most often used trail.