| 12 Mar 2026
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.
This manuscript presents a highly relevant and novel approach to mapping soil thickness on the Qinghai-Tibet Plateau (QTP). By departing from the purely empirical and machine-learning paradigms that currently dominate national-scale soil mapping, the authors implement a physically consistent mass balance model. This work addresses a critical bottleneck in Earth system modeling: the structural bias of observational data in remote, high-altitude regions. Overall, this is a worthy effort that offers a robust, physics-driven alternative to pervasive data-driven methods, offering a promising path forward for data-scarce environments. I believe this paper makes a strong contribution to the field. However, several methodological assumptions require deeper discussion before publication.
1) The core of the mathematical solution fundamentally relies on the assumption of a geomorphic steady state. The QTP, however, is a highly transient landscape. While the authors acknowledge this limitation in the discussion, relying on adjustable weighting coefficients to mathematically force a balance may inadvertently mask true mass imbalances across the region. I hope that the authors expand the discussion to thoroughly explore how this mathematical workaround affects the final outputs and the interpretation of the underlying geomorphic mechanisms.
2) The model uses the Effective Energy and Mass Transfer framework to estimate potential weathering rates, which relies strictly on Mean Annual Temperature (MAT) and Mean Annual Precipitation (MAP). Consequently, the model largely overlooks the specific mechanics of freeze-thaw cycles and active layer dynamics. While I recognize the immense difficulty of implementing these complex cryogenic components into the current model structure, the authors must provide a more thorough and critical discussion of this limitation.
3) The model is driven by several external inputs, including a global machine-learning Depth-to-Bedrock product and high-resolution outputs from the Revised Universal Soil Loss Equation and the Revised Wind Erosion Equation. Because these are fundamentally empirical products, they often also fail to capture the complex sediment transport dynamics specific to alpine meadows, freeze-thaw eroded slopes, and discontinuous permafrost zones. The uncertainties inherent in these driving datasets will inevitably propagate into the final solum product. The authors should discuss whether there are methods to quantify, or ideally further reduce, the uncertainties introduced by these foundational inputs.
4) In Section 3.3, the authors perform a Pearson correlation analysis and report MAT and elevation as the primary drivers of the regional thickness gradient. However, because the model's core soil production function is mathematically driven by MAT and MAP in the first place, the final results are practically guaranteed to correlate strongly with MAT and elevation. I recommend reframing this section. Rather than presenting these correlations as independent scientific discoveries about the region, they should simply be framed as an internal validation confirming that the model behaves as parameterized.
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