ZHANG et al. (2025) Daily Snow Depth Fusion Products for Arid Regions of Central Asia

Identification

• Journal: Mendeley Data
• Year: 2025
• Date: 2025-12-25
• Authors: Liancheng ZHANG, Guli Jiapaer, Xiapeng Jiang, Hongwu Liang, Pingping Feng, Tongwei Ju, Jingxin Zhang, Philippe De Maeyer, Tim VandeVoorde
• DOI: 10.17632/ngp35c3x9n.1

Research Groups

Liancheng Zhang, Guli Jiapaer, Xiapeng Jiang, Hongwu Liang, Pingping Feng, Tongwei Ju, Jingxin Zhang, Philippe De Maeyer, Tim VandeVoorde

Short Summary

This study developed a high-precision daily snow depth fusion product for Central Asia (1990–2023) by integrating multiple existing snow depth products and in-situ observations using an XGBoost model, achieving significantly improved accuracy.

Objective

• To develop a high-precision daily snow depth fusion product for Central Asia by integrating existing snow depth products and in-situ observations using a machine learning model.

Study Configuration

• Spatial Scale: Central Asia (CA), 0.1° spatial resolution.
• Temporal Scale: Daily, spanning 1990–2023 (covering winter, spring, and autumn seasons).

Methodology and Data

• Models used: XGBoost (XGB) machine learning model.
• Data sources: ERA5-Land, MERRA-2, GLDAS (existing daily snow depth products); In-situ snow depth observations; Multi-dimensional covariates (topography, meteorological factors, temporal variables, land use, snow-related parameters).

Main Results

• The generated daily snow depth fusion product for Central Asia achieved an RMSE of 4.7 cm, MAE of 2.8 cm, and R of 0.95.
• The dataset significantly improves accuracy compared to other existing snow depth products for the region.

Contributions

• Provides a novel, high-precision daily snow depth fusion product for Central Asia, significantly enhancing the accuracy of snow depth data for the region.
• Offers reliable data support for climate change studies, water resource management, and disaster early warning systems in Central Asia.

Funding

• Natural Science Foundation of Xinjiang Uygur Autonomous Region (Grant ID: 2025D01A104)

Citation

@article{ZHANG2025Daily,
  author = {ZHANG, Liancheng and Jiapaer, Guli and Jiang, Xiapeng and Liang, Hongwu and Feng, Pingping and Ju, Tongwei and Zhang, Jingxin and Maeyer, Philippe De and VandeVoorde, Tim},
  title = {Daily Snow Depth Fusion Products for Arid Regions of Central Asia},
  journal = {Mendeley Data},
  year = {2025},
  doi = {10.17632/ngp35c3x9n.1},
  url = {https://doi.org/10.17632/ngp35c3x9n.1}
}