Interannual Variability of Snowiness and Avalanche Activity in the Ile Alatau Ridge, Northern Tien Shan

Medeu, Akhmetkal; Blagovechshenskiy, V.; Gulyayeva, Tamara; Zhdanov, V. V.; Ranova, Sandugash

doi:10.3390/w14182936

Cited by 4 publications

(1 citation statement)

References 27 publications

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“…The thermal characteristics and mechanical indices of snow are crucial parameters to be considered in cold-region science and engineering. The process of how snow evolves will undoubtedly be significantly impacted by changing climate conditions as global warming progresses [4,5], requiring a careful examination of snow mechanics and snow hazard issues [6][7][8][9][10]. In the future, a crucial strategy for dealing with snow science and snow risks will be to link the microstructure and apparent physical properties of snow.…”

Section: Introductionmentioning

confidence: 99%

Research on the Evolution of Snow Crystal Necks and the Effect on Hardness during Snowpack Metamorphism

Wei,

Lu,

et al. 2023

Water

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To study the snow microstructure at various metamorphism times and extract the snow neck area, a constant density (200 kg/m3) snow metamorphism experiment was conducted. The findings show that the neck region is mostly influenced by temperature, sun radiation, snow density and specific humidity, with wind speed having little effect. Additionally, we developed a multiple linear regression equation for the neck area under atmospheric forcing: “S = 288T + 2E + 189ρ + 12,194V − 20,443RH − 42,729”. This equation accounts for solar radiation (E), temperature (T), snow density (ρ), specific humidity (RH) and wind speed (V). Notably, the above five factors can account for 84% of the factors affecting the neck area, making it a crucial factor. The relationship between snow hardness and neck area is correlated at 71%, and in later stages of metamorphism, the correlation may increase to 91%. Based on the neck area, the following hardness value prediction is made: “H = 0.002764S + 67.922837”. This study documents the growth variations in the neck region of the metamorphic snow cover and elucidates the process by which outside factors impact the microstructure and macroscopic physical characteristics of the snow cover.

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Section: Introductionmentioning

confidence: 99%

Research on the Evolution of Snow Crystal Necks and the Effect on Hardness during Snowpack Metamorphism

Wei,

Lu,

et al. 2023

Water

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On the problem of estimating the snowiness of winters by different methods by the example of the Yuzhno-Sakhalinsk city

Kazakova

2024

Lëd i sneg

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The problem of estimating a snowiness (a snow coverage) of a territory is considered by many authors, but still nowadays no common approach to its solution is reached. Different authors use different characteristics in their works for estimating the winter snowiness. In this work the snowiness of winters was estimated using different parameters to determine the most representative one. The area ща study is the Yuzhno-Sakhalinsk urban district. The data for the past 36 years (1986–2022) were used to determine the types of the winter snowiness in this district. The following nine methods have been applied: the maximum winter snow reserve; by the amount of solid precipitation; by the average winter thickness of snow cover from the weather station and by snow survey; by the average greatest ten-day thickness of snow cover; by the amount of precipitation in the form of snow; the method of N.N. Galakhov; the Schultz coefficient; and the maximum winter snow cover thickness. The results obtained are very contradictory. Thus, the types of snowiness completely coincided in only 17% of winters; while in 58% of winters the types of snowiness coincided by 2/3 of the above indicators. Estimation of snowiness using various parameters gives closer results when using data on snow reserve at the beginning of snowmelt and the average winter thickness of snow cover (coincidence in 78% of cases). The reason is that a major part of the above methods uses the values of only one parameter. But given that snowiness is a complex indicator, it would be reasonable to consider all possible parameters at the same time. It is impossible to develop a unified approach to estimating winter snowiness, since the parameters chosen for this problem depend on the goal of the determination. It would also be worthwhile to select a methodology for estimating winter snowiness depending on the tasks set, and on the availability and reliability of the initial meteorological data for the analysis. Such work is also complicated by the insufficient volume of meteorological data, as well as due to gaps in them.

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Remote Sensing Techniques for Assessing Snow Avalanche Formation Factors and Building Hazard Monitoring Systems

Denissova,

Nurakynov,

Petrova

et al. 2024

Atmosphere

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Snow avalanches, one of the most severe natural hazards in mountainous regions, pose significant risks to human lives, infrastructure, and ecosystems. As climate change accelerates shifts in snowfall and temperature patterns, it is increasingly important to improve our ability to monitor and predict avalanches. This review explores the use of remote sensing technologies in understanding key geomorphological, geobotanical, and meteorological factors that contribute to avalanche formation. The primary objective is to assess how remote sensing can enhance avalanche risk assessment and monitoring systems. A systematic literature review was conducted, focusing on studies published between 2010 and 2025. The analysis involved screening relevant studies on remote sensing, avalanche dynamics, and data processing techniques. Key data sources included satellite platforms such as Sentinel-1, Sentinel-2, TerraSAR-X, and Landsat-8, combined with machine learning, data fusion, and change detection algorithms to process and interpret the data. The review found that remote sensing significantly improves avalanche monitoring by providing continuous, large-scale coverage of snowpack stability and terrain features. Optical and radar imagery enable the detection of crucial parameters like snow cover, slope, and vegetation that influence avalanche risks. However, challenges such as limitations in spatial and temporal resolution and real-time monitoring were identified. Emerging technologies, including microsatellites and hyperspectral imaging, offer potential solutions to these issues. The practical implications of these findings underscore the importance of integrating remote sensing data with ground-based observations for more robust avalanche forecasting. Enhanced real-time monitoring and data fusion techniques will improve disaster management, allowing for quicker response times and more effective policymaking to mitigate risks in avalanche-prone regions.

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Interannual Variability of Snowiness and Avalanche Activity in the Ile Alatau Ridge, Northern Tien Shan

Cited by 4 publications

References 27 publications

Research on the Evolution of Snow Crystal Necks and the Effect on Hardness during Snowpack Metamorphism

Research on the Evolution of Snow Crystal Necks and the Effect on Hardness during Snowpack Metamorphism

On the problem of estimating the snowiness of winters by different methods by the example of the Yuzhno-Sakhalinsk city

Remote Sensing Techniques for Assessing Snow Avalanche Formation Factors and Building Hazard Monitoring Systems

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