A long-term daily gridded snow depth dataset for the Northern Hemisphere from 1980 to 2019 based on machine learning

Hu, Yong; Che, Tao; Dai, Liyun; Zhu, Yu; Lin, Xiaopei; Deng, Jie; Li, Xin

doi:10.1080/20964471.2023.2177435

Cited by 3 publications

(5 citation statements)

References 53 publications

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“…Even though there are differences to be seen between the studies spatially, in strength and significance, as well depending on the employed products, some general tendencies can be observed. For instance, the decline in SWE/SD is stronger in North America than in Eurasia [13,121,124,125,132]. In addition, many studies observe a stronger decrease in spring than in winter [13,100,124,240].…”

Section: Results Of Long-term Studiesmentioning

confidence: 99%

“…Such a significant decrease was also observed in Finland and Sweden around the Baltic Sea. However, there are also regions, such as Siberia between the Lena and Kolyma Rivers or the American Rocky Mountains, where a significant increase in SWE/SD has been observed [13,121,124]. Looking at China, where more studies have been conducted, no coherent pattern is apparent.…”

Section: Results Of Long-term Studiesmentioning

confidence: 99%

“…Regardless of the individual intentions and methods of the studies, most AI studies can be grouped together as they all use AI to uncover relationships between the Tb signal and other variables (such as, but not exclusively, hillslope, temperature, or precipitation) to improve SWE data [63,106,107,115,118,135,146,147,149,170,174,175,187,190,197,201,205,[229][230][231][232][233]. The remainder of the AI studies attempted to fuse existing datasets to fill data gaps and generate more robust data [121,125,133]. Considering where in our topic classification the AI studies can be located, most of them were classified as snow monitoring (n = 16), but AI is also used, for instance, for the development of new products (n = 4) [121,125,133,197] or for the analysis of a snow storm (n = 1) [205].…”

Section: • Artificial Intelligence (Ai) Machine Learning (Ml) and Dee...mentioning

confidence: 99%

“…The remainder of the AI studies attempted to fuse existing datasets to fill data gaps and generate more robust data [121,125,133]. Considering where in our topic classification the AI studies can be located, most of them were classified as snow monitoring (n = 16), but AI is also used, for instance, for the development of new products (n = 4) [121,125,133,197] or for the analysis of a snow storm (n = 1) [205]. The individual tasks for which AI is used are diverse.…”

Section: • Artificial Intelligence (Ai) Machine Learning (Ml) and Dee...mentioning

confidence: 99%

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Snow Water Equivalent Monitoring—A Review of Large-Scale Remote Sensing Applications

Schilling,

Dietz,

Kuenzer

2024

Remote Sensing

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Snow plays a crucial role in the global water cycle, providing water to over 20% of the world’s population and serving as a vital component for flora, fauna, and climate regulation. Changes in snow patterns due to global warming have far-reaching impacts on water management, agriculture, and other economic sectors such as winter tourism. Additionally, they have implications for environmental stability, prompting migration and cultural shifts in snow-dependent communities. Accurate information on snow and its variables is, thus, essential for both scientific understanding and societal planning. This review explores the potential of remote sensing in monitoring snow water equivalent (SWE) on a large scale, analyzing 164 selected publications from 2000 to 2023. Categorized by methodology and content, the analysis reveals a growing interest in the topic, with a concentration of research in North America and China. Methodologically, there is a shift from passive microwave (PMW) inversion algorithms to artificial intelligence (AI), particularly the Random Forest (RF) and neural network (NN) approaches. A majority of studies integrate PMW data with auxiliary information, focusing thematically on remote sensing and snow research, with limited incorporation into broader environmental contexts. Long-term studies (>30 years) suggest a general decrease in SWE in the Northern Hemisphere, though regional and seasonal variations exist. Finally, the review suggests potential future SWE research directions such as addressing PMW data issues, downsampling for detailed analyses, conducting interdisciplinary studies, and incorporating forecasting to enable more widespread applications.

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Section: Results Of Long-term Studiesmentioning

confidence: 99%

Section: Results Of Long-term Studiesmentioning

confidence: 99%

Section: • Artificial Intelligence (Ai) Machine Learning (Ml) and Dee...mentioning

confidence: 99%

Section: • Artificial Intelligence (Ai) Machine Learning (Ml) and Dee...mentioning

confidence: 99%

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Snow Water Equivalent Monitoring—A Review of Large-Scale Remote Sensing Applications

Schilling,

Dietz,

Kuenzer

2024

Remote Sensing

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“…Additionally, MODIS snow products have been widely adopted in the study of snow cover changes in mountainous areas on regional or global scales [15][16][17]. The combination of remote sensing images and model simulations with in-situ observations is considered the best validation for regional investigations of snow cover [18,19]. However, it cannot be denied that clouds are the main influencing factor affecting the recognition of snow information by MODIS snow products [11,20].…”

Section: Introductionmentioning

confidence: 99%

The Variability of Snow Cover and Its Contribution to Water Resources in the Chinese Altai Mountains from 2000 to 2022

Yu,

Wang,

Liu

et al. 2023

Remote Sensing

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As one of the major water supply systems for inland rivers, especially in arid and semi-arid regions, snow cover strongly affects hydrological cycles. In this study, remote sensing datasets combined with in-situ observation data from a route survey of snow cover were used to investigate the changes in snow cover parameters on the Chinese Altai Mountains from 2000 to 2022, and the responses of snow cover to climate and hydrology were also discussed. The annual snow cover frequency (SCF), snow cover area, snow depth (SD), and snow density were 45.03%, 2.27 × 104 km2, 23.4 cm, and ~0.21 g·cm−3, respectively. The snow water equivalent ranged from 0.58 km3 to 1.49 km3, with an average of 1.12 km3. Higher and lower SCF were mainly distributed at high elevations and on both sides of the Irtysh river. The maximum and minimum snow cover parameters occurred in the Burqin River Basin and the Lhaster River Basin. In years with high SCF, abnormal westerly airflow was favorable for water vapor transport to the Chinese Altai Mountains, resulting in strong snowfall, and vice versa in years with low SCF. There were significant seasonal differences in the impact of temperature and precipitation on regional SCF changes. The snowmelt runoff ratios were 11.2%, 25.30%, 8.04%, 30.22%, and 11.56% in the Irtysh, Kayit, Haba, Kelan, and Burqin River Basins. Snow meltwater has made a significant contribution to the hydrology of the Chinese Altai Mountains.

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Spatiotemporal changes and management measure to enhance ecosystem services in the Mongolian Plateau

Wei,

Wu,

Wang

et al. 2024

Environmental Development

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A long-term daily gridded snow depth dataset for the Northern Hemisphere from 1980 to 2019 based on machine learning

Cited by 3 publications

References 53 publications

Snow Water Equivalent Monitoring—A Review of Large-Scale Remote Sensing Applications

Snow Water Equivalent Monitoring—A Review of Large-Scale Remote Sensing Applications

The Variability of Snow Cover and Its Contribution to Water Resources in the Chinese Altai Mountains from 2000 to 2022

Spatiotemporal changes and management measure to enhance ecosystem services in the Mongolian Plateau

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