Daily Snow Depth Fusion Products for Arid Regions of Central Asia

Published: 24 March 2026| Version 2 | DOI: 10.17632/ngp35c3x9n.2
Contributors:
Liancheng Zhang, Guli Jiapaer, Tao Yu, Xiapeng Jiang, Hongwu Liang, Pingping Feng, Tongwei Ju, Jingxin Zhang, Philippe De Maeyer, Tim VandeVoorde

Description

This dataset employs the XGBoost (XGB) machine learning model, adopting a seasonal modeling strategy (winter, spring, and autumn) to integrate the advantages of multiple daily snow depth (SD) products, including ERA5-Land, MERRA-2, and GLDAS, based on in-situ SD observations. By coupling multi-dimensional covariates such as topography, meteorological factors, temporal variables, land use, and snow-related parameters, a high-precision daily SD fusion model was developed for Central Asia (CA). The model was then applied to generate a 0.1° daily SD fusion product for CA spanning 1980–2023 (covering winter, spring, and autumn). Evaluation results demonstrate that the dataset achieves an RMSE of 6.5 cm, MAE of 3.9 cm, and R of 0.86 across the CA region, significantly improving accuracy compared to other existing SD products. This dataset provides reliable data support for climate change studies, water resource management, and disaster early warning systems in Central Asia.

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Categories

Water Resource, Arid Region, Snow

Funders

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

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