Snow Area Research Articles

Discriminating Wet Snow and Firn for Alpine Glaciers Using Sentinel-1 Data: A Case Study at Rofental, Austria

Continuous monitoring of glacier changes supports our understanding of climate related glacier behavior. Remote sensing data offer the unique opportunity to observe individual glaciers as well as entire mountain ranges. In this study, we used synthetic aperture radar (SAR) data to monitor the recession of wet snow area extent per season for three different glacier areas of the Rofental, Austria. For four glaciological years (GYs, 2014/2015–2017/2018), Sentinel-1 (S1) SAR data were acquired and processed. For all four GYs, the seasonal snow retreated above the elevation range of perennial firn. The described processing routine is capable of discriminating wet snow from firn areas for all GYs with sufficient accuracy. For a short in situ transect of the snow—firn boundary, SAR derived wet snow extent agreed within an accuracy of three to four pixels or 30–40 m. For entire glaciers, we used optical remote sensing imagery and field data to assess reliability of derived wet snow covered area extent. Differences in determination of snow covered area between optical data and SAR analysis did not exceed 10% on average. Offsets of SAR data to results of annual field assessments are below 10% as well. The introduced workflow for S1 data will contribute to monitoring accumulation area extent for remote and hazardous glacier areas and thus improve the data basis for such locations.

Quantifying the contributions of snow/glacier meltwater to river runoff in the Tianshan Mountains, Central Asia

Abstract This study investigated the contributions of snow/glacier meltwater to river runoff in the northern and central Tianshan Mountains (Central Asia). Based on end- member mixing analysis (EMMA), the hydrograph separation was carried out in six benchmark catchments with different glacier area ratios (GARs) and snow area ratios (SARs) during a typical snow melt period (TSMP) and a typical glacier melt period (TGMP). The results indicate that the contribution of snow/glacier meltwater to river runoff is positively correlated with GARs or maximum SARs. The contribution ratios of snowmelt water vary from 22% to 49% in TSMP. The contribution ratios of glacier meltwater vary from 12% to 59% in TGMP. The contribution ratios of snow meltwater in the northern Tianshan Mountains (36%) are higher than central Tianshan Mountains (31%), while the contribution ratios of glacier meltwater in the northern Tianshan Mountains (36%) are lower than central Tianshan Mountains (42%). The contribution of rainfall to river runoff is higher in TGMP (9%–23%) than in TSMP (5%–9%). Baseflow is the most important recharge source to river runoff, with the contribution ratios vary from 44% to 73% in TSMP, and vary from 30% to 75% in TGMP. The spatial and temporal variations in tracer concentrations in streamflow components were found to be responsible to the uncertainties in hydrograph separation.

A Novel Multitemporal Cloud and Cloud Shadow Detection Method Using the Integrated Cloud Z-Scores Model

Accurate cloud and cloud shadow detection in multispectral remote sensing imager due to the high spectral variations of clouds and the complexities of underlying landscapes, especially for images without thermal bands. In this paper, we proposed a multitemporal integrated cloud z-score (MTICZ) method for cloud and cloud shadow detection for multitemporal optical images. First, an integrated cloud z-score (ICZ) index was designed to identify clouds and cloud shadows, and measure the likelihood of a pixel being either a cloudy or shadowed pixel. Clouds and cloud shadows were then detected by differencing the ICZ values between the reference image and the cloudy image, and they were refined using a cloud and shadow matching algorithm. Finally, the MTICZ method was evaluated through cloud fraction estimation and 3,000 random validation samples from the cloudy Landsat scenes. The results indicate that MTICZ method achieved a significant agreement with reference cloud fractions $({{R^2} = {\text{0.97}},{\text{RMSE}} = {\text{4.31}\%}})$ and performed well under complicated land surface conditions, with an average overall accuracy of 91.65%. In addition, the MTICZ method was compared with two popular cloud detection methods, the Fmask method and the multitemporal cloud detection (MTCD) algorithm. This comparison reveals some improvements for cloud identification in complex landscapes, such as impervious surfaces and mixed areas of snow and cloud. Therefore, we argue that MTICZ provides an effective multitemporal method to detect cloud and cloud shadow for optical imagery in complicated landscapes.

Alternative approach for optical monitoring of arbitrarily distributed substances.

In this paper, we propose a novel approach, to the best of our knowledge, for optical monitoring of integral properties of arbitrarily distributed substances by means of a specially designed optical element radially quadratic transmittance-apodizing filter. The developed remote sensing method deals with nonmatrix detectors and requires only one-step conversion of light energy into an electrical signal to investigate the substance under study. Due to the fact that detectors work in the linear sensitivity range, the nonlinear uncertainty of measurements is minimal. Theoretical studies have been performed to derive formulas characterizing the substance parameters: distribution center, drift and drift speed of the center, radial standard deviation and its changing rate, and diffusion. The technique is applied to remotely monitor the process of reduction of the artificial snow area on the mock-up surface covered with a soil.

A method for monthly mapping of wet and dry snow using Sentinel-1 and MODIS: Application to a Himalayan river basin

Satellite Remote Sensing, with both optical and SAR instruments, can provide distributed observations of snow cover over extended and inaccessible areas. Both instruments are complementary, but there have been limited attempts at combining their measurements. We describe a novel approach to produce monthly maps of dry and wet snow areas through application of data fusion techniques to MODIS fractional snow cover and Sentinel-1 wet snow mask, facilitated by Google Earth Engine. The method is demonstrated in a 55,000 km2 river basin in the Indian Himalayan region over a period of ∼2.5 years, although it can be applied to any areas of the world where Sentinel-1 data are routinely available. The typical underestimation of wet snow area by SAR is corrected using a digital elevation model to estimate the average melting altitude. We also present an empirical model to derive the fractional cover of wet snow from Sentinel-1. Finally, we demonstrate that Sentinel-1 effectively complements MODIS as it highlights a snowmelt phase which occurs with a decrease in snow depth but no/little decrease in snowpack area. Further developments are now needed to incorporate these high resolution observations of snow areas as inputs to hydrological models for better runoff analysis and improved management of water resources and flood risk.

Shape feature-assisted discrimination of cloud and snow in GF-1 spectral images of mountainous areas

ABSTRACTIn this study, we propose a new method for cloud and snow discrimination, which is based on spectral, texture, and shape features and combined with support vector machine multi-classification strategy. A new feature called curvature histogram is designed to describe edge shape. First, the region of interest test is applied to extract cloud and snow area. Then, the extracted area is combined with the segmentation results using the mean shift algorithm to obtain the object of interest while calculating the feature values. The complexity of surfaces in the cloud and snow area is classified into four types, namely, thick cloud, thin cloud, snow, and snow-covered land, such that six kinds of classifiers are obtained by designing a classifier between every two categories. Through six classifiers and calculating the sum of confidence coefficients for each category, every superpixel is classified into the class with the highest confidence coefficient and a rough cloud and snow mask is obtained. Finally, the GrabCut algorithm is applied to optimize the classification results at the pixel level. The experiments on the images of China’s GF-1 satellite, indicate that the proposed method is effective for cloud and snow discrimination on multispectral high-resolution satellites images in mountainous area.

Early-stage ecological influences of population recovery of large mammals on dung beetle assemblages in heavy snow areas

Past conservation initiatives and rapidly decreasing human populations in modern Japan have contributed to population recoveries of Sika deer (Cervus nippon), wild boar (Sus scrofa), and Japanese macaque (Macaca fuscata) throughout the country. Ironically, however, these recoveries have not always received a favorable reception, because these mammals can also be agricultural pests. To open public debate on the recoveries, based on a thorough understanding of their multifaceted roles in sustaining the local ecosystem, we evaluated the initial stage ripple effects caused by the mammalian population recovery on the community assembly of dung beetles, which are keystone decomposer organisms in terrestrial ecosystems. For the evaluation, we conducted manipulative snapshot experiments, using camera and pitfall traps, for mammal and dung beetle assemblages, respectively, in four different mountain ranges within the heavy snow areas of northern Japan, where the recovery of three mammal populations was at an early stage. The current findings implied that, although the feces of every recovering mammal species could provide valuable resources for most beetles, the ripple effects from the mammal population recoveries were subject to hysteresis of the local ecosystem, i.e., catastrophic shifts in ecosystems originating from the historical background of regional mammal defaunation. In particular, the abundance of tunnelers that could benefit from positive ripple effects decreased with an increase in past disturbances, which resulted in emptier forests, i.e., an ecosystem with fewer large mammals. The findings suggested that recovering populations of large mammals do not always contribute to the restoration of the original dung beetle communities, at least initially.

Arctic plant ecophysiology and water source utilization in response to altered snow: isotopic (δ18O and δ2H) evidence for meltwater subsidies to deciduous shrubs

Warming-linked woody shrub expansion in the Arctic has critical consequences for ecosystem processes and climate feedbacks. The snow-shrub interaction model has been widely implicated in observed Arctic shrub increases, yet equivocal experimental results regarding nutrient-related components of this model have highlighted the need for a consideration of the increased meltwater predicted in expanding shrub stands. We used a 22-year snow manipulation experiment to simultaneously address the unexplored role of snow meltwater in arctic plant ecophysiology and nutrient-related components of the snow-shrub hypothesis. We coupled measurements of leaf-level gas exchange and leaf tissue chemistry (%N and δ13C) with an analysis of stable isotopes (δ18O and δ2H) in soil water, precipitation, and stem water. In deeper snow areas photosynthesis, conductance, and leaf N increased and δ13C values decreased in the deciduous shrubs, Betula nana and Salix pulchra, and the graminoid, Eriophorum vaginatum, with the strongest treatment effects observed in deciduous shrubs, consistent with predictions of the snow-shrub hypothesis. We also found that deciduous shrubs, especially S. pulchra, obtained much of their water from snow melt early in the growing season (40-50%), more than either E. vaginatum or the evergreen shrub, Rhododendron tomentosum (Ledum palustre). This result provides the basis for adding a meltwater-focused feedback loop to the snow-shrub interaction model of shrub expansion in the Arctic. Our results highlight the critical role of winter snow in the ecophysiology of Arctic plants, particularly deciduous shrubs, and underline the importance of understanding how global warming will affect the Arctic winter snowpack.

Assessments of the Northern Hemisphere snow cover response to 1.5 and 2.0 °C warming

Abstract. The 2015 Paris Agreement set a goal to pursue a global mean temperature below 1.5 °C and well below 2 °C above preindustrial levels. Although it is an important surface hydrology variable, the response of snow under different warming levels has not been well investigated. This study provides a comprehensive assessment of the snow cover fraction (SCF) and snow area extent (SAE), as well as the associated land surface air temperature (LSAT) over the Northern Hemisphere (NH) based on the Community Earth System Model Large Ensemble project (CESM-LE), the CESM 1.5 and 2 °C projects, and the CMIP5 historical RCP2.6 and RCP4.5 products. The results show that the spatiotemporal variations in those modeled products are grossly consistent with observations. The projected SAE magnitude change in RCP2.6 is comparable to that in 1.5 °C, but lower than that in 2 °C. The snow cover differences between 1.5 and 2 °C are prominent during the second half of the 21st century. The signal-to-noise ratios (SNRs) of both SAE and LSAT over the majority of land areas are greater than 1, and for the long-term period, the dependences of SAE on LSAT changes are comparable for different ensemble products. The contribution of an increase in LSAT to the reduction of snow cover differs across seasons, with the greatest occurring in boreal autumn (49–55 %) and the lowest occurring in boreal summer (10–16 %). The snow cover uncertainties induced by the ensemble variability are invariant over time across CESM members but show an increase in the warming signal between the CMIP5 models. This feature reveals that the physical parameterization of the model plays the predominant role in long-term snow simulations, while they are less affected by internal climate variability.

Large-scale lava-ice interactions on Mars: Investigating its role during Late Amazonian Central Elysium Planitia volcanism and the formation of Athabasca Valles

The Central Elysium Planitia region contains evidence for large-scale, Late Amazonian volcanic and fluvial activity, the youngest on Mars. The region consists of a population of geologically recent, Late Amazonian-aged flood basalt volcanic plains which are incised by several large fluvial outflow channel systems. Recent understanding of the distribution of paleo-glaciation on Mars, informed by geomorphic indicators and model predictions, suggests that Central Elysium Planitia was an area of likely surface snow and ice accumulation throughout the Amazonian period. Furthermore, the volcanic plains of Central Elysium Planitia contain wide areas with preserved rootless cones, representing among the strongest evidence for past lava-ice interactions on Mars. Consequently, Central Elysium Planitia is of significant interest to understanding the recent Amazonian thermal and hydrologic history of Mars.Here we perform an exploratory assessment of the influence of surficial large-scale lava-ice interactions on the geomorphology of the Central Elysium Planitia volcanic plains and their potential contribution to the formation of the fluvial outflow channels contained within the region. The outflow channels of Central Elysium Planitia are generally interpreted to have formed as a result of catastrophic outbursts of groundwater released from pressurized, confined subsurface aquifers by magmatic dike emplacement. However, the formation of the relatively young Central Elysium Planitia outflow channels by this mechanism is subject to several demanding conditions which are not required of a lava-ice interaction origin. Therefore we revisit the origin of the Central Elysium Planitia outflow channels and explore large-scale lava-ice interactions as a possible alternative mechanism to contribute to, or account for, their formation. This assessment is performed through a case study of Athabasca Valles, a well-characterized outflow channel system which appears to emanate from the Cerberus Fossae fissure system.We find that surface ice and lava-ice interactions could have influenced the geomorphology of Central Elysium Planitia by contributing to the formation of the regions platy-ridged volcanic plains texture and rootless cones. Large-scale lava-ice interactions in Central Elysium Planitia are found to be able to readily supply the water volumes needed to form the Athabasca Valles system, but additional mechanisms are required to cause meltwater confinement and release to supply requisite discharges. Of the several potential mechanisms which could have led to meltwater confinement and release, the most likely are supra/englacial melt accumulation with outflow triggered by the flotation of a confining ice dam, and supra/englacial meltwater accumulation released by top-down supraglacial overflow. A simple analysis of these processes indicates that the rates of discharge necessary to form Athabasca Valles can be supplied while adhering to the geomorphic constraints of the system. Therefore, we conclude that the formation of the Athabasca Valles outflow channel system (and possibly other outflow channels in the region) could plausibly have been the result of surficial lava-ice interactions through meltwater confinement, accumulation, and release, providing an advantageous alternative to classical groundwater outburst models.

Retreat of Pindari glacier and detection of snout position using remote sensing technology

Pindari glacier is the source to Pindar River, one of the major tributary to the River Alaknanda. This study aims to assess the retreat of the Pindari glacier in four decades using visual interpretation of remote sensing data. The GPS technology (coordinates) was used to determine the latest snout position in 2014. The location of snout was marked using series of periodic Landsat imagery with manual digitization process. We marked the snow area and snout point for the different years by using satellite data. Further, these datasets were compared to measure the changes in the glacier position and the shift distances. The cumulative glacial retreat was recorded 1569.01 m with average glacier retreat rate of 51.23 m/year within the study duration (1976–2014). The glacier retreat rate/year was faster during 1976–94 than 1999–2014. The finding of the study may bring attention to the scientific society and trigger more vigorous study on quantifying glacier retreat in Pindari as well as other glaciers of the Himalaya.

CLOUD DETECTION BY FUSING MULTI-SCALE CONVOLUTIONAL FEATURES

Abstract. Clouds detection is an important pre-processing step for accurate application of optical satellite imagery. Recent studies indicate that deep learning achieves best performance in image segmentation tasks. Aiming at boosting the accuracy of cloud detection for multispectral imagery, especially for those that contain only visible and near infrared bands, in this paper, we proposed a deep learning based cloud detection method termed MSCN (multi-scale cloud net), which segments cloud by fusing multi-scale convolutional features. MSCN was trained on a global cloud cover validation collection, and was tested in more than ten types of optical images with different resolution. Experiment results show that MSCN has obvious advantages over the traditional multi-feature combined cloud detection method in accuracy, especially when in snow and other areas covered by bright non-cloud objects. Besides, MSCN produced more detailed cloud masks than the compared deep cloud detection convolution network. The effectiveness of MSCN make it promising for practical application in multiple kinds of optical imagery.

Capacity of Caragana microphylla shrub on counteracting snow movement and its influence on snow morphology in the Xilinhot Steppe, China.

The aim of this study was to examine the impacts of characteristics of the shrub (shrub height, shrub width on the windward side, shrub length on the downwind side) on the snow morphology (snow height, snow width, and snow tail length) in the typical steppe of Xilinhot, China. The relationship between shrub height and parameters of snow morphology showed significant quadratic polynomial regression. The relationship between shrub width on the windward side, length on the downwind side and parameters of snow morphology showed significant power function (exponent was less than 1). The morphology and development characteristics of shrub snow were affected by the shrub characteristic parameters. Shrub height had the greatest influence on snow height. Shrub width at the windward side had the greatest influence on snow width and snow tail length. The snow morphology developed faster when the shrub was small, and then tended to be stable. The two-dimensional snow retention range model of shrub directly reflected the disturbance range of shrub to wind and the potential range of snow, indirectly reflected the capacity of shrub to retard snow movement. The three-dimensional snow blocking volume model of shrub snow directly reflected the snow resistance capacity of shrub under certain snow sources and wind conditions. Both models established here would provide a theoretical basis for estimating snow resources and preventing snow disaster in the wind blown snow area of typical steppe.

Key Algorithms And Its Realization About Snowmelt Flood Disaster Model Based On Remote Sensing And GIS

Based on the remote sensing and GIS techniques, the relationships of the variables influencing the snowmelt flood such as the snow area, the snow depth, the air temperature, the precipitation, the land topography and land covers are analyzed and a prediction and damage assessment model for snowmelt floods is developed. This model analyzes and predicts the flood submerging range, flood depth, flood grade, and the damages of different underlying surfaces in the study area in a given time period based on the estimation of snowmelt amount, the snowmelt runoff, the direction and velocity of the flood. Then it was used to predict a snowmelt flood event in the Ertis River Basin in northern Xinjiang, China, during March and June, 2017 and to assess its damages including the damages of roads, transmission lines, settlements caused by the floods and the possible landslides using the hydrological and meteorological data, snow parameter data, DEM data and land use data. A comparison was made between the prediction results from this model and flood measurement and its disaster loss data, which suggests that this model performs well in predicting the strength and impact area of snowmelt flood and its damage assessment.

Decline ofSibbaldia procumbens(Rosaceae) on Mount Washington, White Mountains, NH, USA

abstract Sibbaldia procumbens (Rosaceae) is globally widespread in alpine and subalpine habitats of the Northern Hemisphere, where it thrives in areas of late-lying snow that persist into summer. Despite its widespread circumboreal distribution and local abundance, Sibbaldia is one of the rarest plants in New England, USA, occurring only in a single ravine on Mount Washington, New Hampshire. The New Hampshire population has evidently been declining for several decades, possibly the result of a combination of interspecific competition, over-collection, and changes in environmental conditions. We assessed historical trends, current status, and potential causes of decline of the New Hampshire population through evaluation of herbarium and historical records, field surveys, and photographic comparisons. Our research at seven New England herbaria revealed 236 plants with roots, including many large adult plants, on 65 herbarium sheets. Most of these plants were collected between 1846 and 1908, representing tro...

The peculiar morphological features of the blood stains on the snow over of different density

The objective of the present study was to elucidate the peculiar morphological features of the blood stains on the snow over of different density. We have undertaken the modeling and investigation of the blood stains on the loose snow cover (ρ=0.35±0.03 g/cm3) and at the areas of snow compacted by a motor-vehicle wheel (ρ=0.96±0.03 g/cm3) with the use of the visual, metric, descriptive, and statistical methods. The analysis of the blood stain patterns on the loose snow showed that a blood drop tends to form a depressed cylindrical canal at the bottom of which an irregularly shaped spherical element can be seen; it is composed of small snow crystals soaked with blood. The upper end of the canal has the fine-scalloped edge tending to shed unevenly. The morphological picture of the blood stains at the compacted snow cover is similar to that on the hard wettable rough surfaces; they have the oval shape and the unevenly serrated edge with the well apparent signs of sputtering.at the periphery.

Fractional Snow-Cover Mapping Based on MODIS and UAV Data over the Tibetan Plateau

Moderate-resolution imaging spectroradiometer (MODIS) snow-cover products have relatively low accuracy over the Tibetan Plateau because of its complex terrain and shallow, fragmented snow cover. In this study, fractional snow-cover (FSC) mapping algorithms were developed using a linear regression model (LR), a linear spectral mixture analysis model (LSMA) and a back-propagation artificial neural network model (BP-ANN) based on MODIS data (version 006) and unmanned aerial vehicle (UAV) data. The accuracies of the three models were validated against Landsat 8 Operational Land Imager (OLI) snow-cover maps (Landsat 8 FSC) and compared with the MODIS global FSC product (MOD10A1 FSC, version 005) for the purpose of finding the optimal algorithm for FSC extraction for the Tibetan Plateau. The results showed that (1) the overall retrieval results of the LR and BP-ANN models based on MODIS and UAV data were relatively similar to the OLI snow-cover maps; the accuracy and stability were greatly improved, with even some reduction in errors; compared to the Landsat 8 FSC, the correlation coefficients (r) were 0.8222 and 0.8445 respectively and the root-mean-square errors (RMSEs) were 0.2304 and 0.2201, respectively. (2) The accuracy and stability of the fully constrained LSMA model using the pixel purity index (PPI) endmember extraction method based only on MODIS data suffered the worst performance of the three models; r was only 0.7921 and the RMSE was as large as 0.3485. There were some serious omission phenomena in the study area, specifically for the largest mean absolute error (MAE = 0.2755) and positive mean error (PME = 0.3411). (3) The accuracy of the MOD10A1 FSC product was much lower than that of the LR and BP-ANN models, although its accuracy slightly better that of the LSMA based on comprehensive evaluation of six accuracy indices. (4) The optimal model was the BP-ANN model with combined inputs of surface reflectivity data (R1–R7), elevation (DEM) and temperature (LST), which can easily incorporate auxiliary information (DEM and LST) on the basis of (R1–R7) during the relationship training period and can effectively improve the accuracy of snow area monitoring—it is the ideal algorithm for retrieving FSC for the Tibetan Plateau.

Reduction of snow albedo from vehicle emissions at Portillo, Chile

Only scarce literature exists on the effect of direct deposition of vehicle particulate matter emissions onto snow surfaces with well-quantified sources and atmospheric conditions. Local emissions from vehicles in the surroundings of ski resorts not only reduce the whiteness of the snowy landscape affecting the attractiveness to visitors, but also modify the onset of snowmelt and thus the environmental equilibrium of the local area and of the surrounding region. The changes in albedo observed at Portillo, in the Chilean Andes, were an increase of around 0.17units after a heavy snowfall (20cm accumulation), an increase of around 0.07 after a prolonged lighter snowfall (10cm accumulation), and a mean decrease of around 0.08units per day with heavy traffic (around 2000 vehicles per day). Other parameters such as wind velocity and direction did not greatly affect the snow albedo during this study because the wind direction was fairly constant due to the terrain restriction. It is difficult to estimate how much the snow metamorphism and melting contributed to the observed decrease. The albedo changes observed are helpful to confirm the close cause-effect relationship between these parameters and the snow albedo, and to foresee that traffic restriction may allow for more stable snowpack conditions. The case study presented here can be extrapolated to other vehicle-contaminated snow areas, thus examining their contribution to snow radiative forcing and climate change at multiple scale.

Water extraction technique in mountainous areas from satellite images

Water monitoring is an important part of water resource management and has become an essential aspect of remote sensing. A number of indices have been developed for water extraction using satellite images. Even though all indices can extract the extent of a water body, none can do so without including a noise component, such as topographic shadows, cloud shadows, snow, ice, and buildup areas, all of which have spectrally similar characteristics under certain circumstances. In order to select the best index for water body extraction, several water indices have been compared. This paper proposes a method for extracting water bodies called the water extraction surface temperature index (WESTI). This method uses normalized difference water index (NDWI) and land surface temperature to eliminate the noise components, especially in mountainous and cold areas where other indices have very low accuracy. The results have shown that WESTI improves the NDWI results by removing more than 80% of topographic shadows, with an overall accuracy of 99% in all cases.

Distinguishing the Role of Wind in Snow Distribution by Utilizing Remote Sensing and Modeling Data: Case Study in the Northeastern Tibetan Plateau

Snow distribution has a profound impact on natural processes such as the hydrological cycle, the climate system, and ecological evolution. Many studies suggest that elevation, temperature, and precipitation are the three major factors controlling snow distribution. Our study explores the influence of wind on the snow distribution and finds that wind is another important factor controlling the snow distribution in the northeastern Tibet Plateau. We select the Qilian Mountains in the northeastern Tibetan Plateau as the study area, and the data include the moderate-resolution imaging spectroradiometer snow area product and the atmosphere dataset generated by the Weather Research and Forecasting model. The results indicate that there is a threshold elevation for the correlation between the fractional snow cover (FSC) area and the wind speed in the study area. At elevations above 3900 m, the FSC and wind speed exhibit a significant negative correlation, and at elevations below 3900 m, they exhibit a significant positive correlation. Our analyses indicate that the probability for the occurrence of snowdrifts is higher in regions above 3900 m and that the wind transports snow from regions above 3900 m to lower elevations.