Daily Snow Depth Fusion Dataset for Central Asia

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, ERA5, JRA-55, 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 1990–2023 (covering winter, spring, and autumn). Evaluation results demonstrate that the dataset achieves an RMSE of 4.1 cm, MAE of 2.3 cm, and R of 0.96 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.