A physically consistent soil thickness map of the Qinghai-Tibet Plateau derived from coupled erosion mechanisms

Abstract. Accurate, spatially explicit data on soil thickness is a critical missing link for Earth system modeling on the Qinghai-Tibet Plateau (QTP), where complex terrain and a lack of subsurface observations limit the performance of traditional empirical mapping approaches. This study presents a new high-resolution (1 km) soil thickness dataset for the QTP, specifically defined as the solum thickness extending from the surface to the C horizon interface. To overcome the inherent biases of sparse observation networks in high-altitude regions, we developed a revised mass balance model that ensures physical consistency across the plateau’s diverse landscapes. Unlike existing products derived from statistical extrapolation or machine learning, our dataset is grounded in the mechanistic coupling of climate-driven weathering with a multi-processes erosion framework that partitions sediment transport into wind, hydraulic, and gravitational components. The resulting dataset reveals that solum thickness on the QTP ranges from 0.39 m to 2.04 m, with a spatially averaged mean of 0.89 m, exhibiting a pronounced decreasing gradient from the warm, humid southeast to the cold, arid northwest. Validation against 552 soil profile observations demonstrates that this physically-based approach achieves a Root Mean Square Error of 0.34 m and a Mean Relative Error of 0.78, outperforming existing national-scale soil maps (e.g., the Shangguan and Liu maps) by approximately 10–17 %. Crucially, the model effectively reproduces realistic geomorphological patterns, such as the sharp differentiation between depositional basins and erosional ridges, which purely data-driven models often fail to capture in undersampled high-altitude regions. This dataset provides essential model parameter for hydrological, ecological, and cryosphere models, facilitating more reliable assessments of the “Third Pole” under a changing climate. The dataset is available at figshare via https://doi.org/10.6084/m9.figshare.30925358.