Zhu (2025) Snow depth

Identification

• Journal: Mendeley Data
• Year: 2025
• Date: 2025-11-17
• Authors: Zhu, Liu
• DOI: 10.17632/c9nj9mjdcw.1

Research Groups

Not explicitly stated, but contributed by Liu Zhu.

Short Summary

A long-term daily snow depth dataset for the Northern Hemisphere was prepared by combining multi-source data and machine learning, which was then used to analyze the spatiotemporal characteristics of average and maximum snow depth on the Qinghai-Tibet Plateau from 1980 to 2019.

Objective

• To prepare a long-term daily snow depth dataset for the Northern Hemisphere by integrating multi-source snow depth products, environmental factors, and ground observations using machine learning methods.
• To analyze the spatiotemporal characteristics of average and maximum snow depth on the Qinghai-Tibet Plateau from 1980 to 2019 using the newly prepared dataset.

Study Configuration

• Spatial Scale: Northern Hemisphere (for dataset preparation), Qinghai-Tibet Plateau (for analysis).
• Temporal Scale: 1980 to 2019 (40 years), daily resolution.

Methodology and Data

• Models used: Machine learning methods.
• Data sources: Multi-source snow depth product data, environmental factor variables, ground-observed snow depth data.

Main Results

• A comprehensive long-term daily snow depth dataset for the Northern Hemisphere was successfully generated.
• The spatiotemporal characteristics of average snow depth and maximum snow depth on the Qinghai-Tibet Plateau were analyzed for the period 1980-2019 using the developed dataset.

Contributions

• Creation of a novel, long-term (40-year), daily snow depth dataset for the Northern Hemisphere by integrating diverse data sources (multi-source products, environmental factors, ground observations) using machine learning.
• Application of this new dataset to provide insights into the spatiotemporal dynamics of snow depth on the Qinghai-Tibet Plateau.

Funding

Not specified in the provided text.

Citation

@article{Zhu2025Snow,
  author = {Zhu, Liu},
  title = {Snow depth},
  journal = {Mendeley Data},
  year = {2025},
  doi = {10.17632/c9nj9mjdcw.1},
  url = {https://doi.org/10.17632/c9nj9mjdcw.1}
}