the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 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.
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Status: final response (author comments only)
- RC1: 'Comment on essd-2025-809', Anonymous Referee #1, 10 Apr 2026
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RC2: 'Comment on essd-2025-809', Anonymous Referee #2, 13 Apr 2026
This manuscript presents a new 1 km resolution solum thickness dataset for the Qinghai-Tibet Plateau (QTP) developed using a revised geomorphic mass balance model. The topic is highly relevant and timely. The methodological innovations represent a thoughtful and non-routine adaptation of classical mass balance models to the QTP’s unique conditions. This work has strong potential value for the soil science, cryospheric, and Earth system modeling communities.
Overall, the scientific core has clear merit and novelty. However, several non-trivial weaknesses in validation strategy and uncertainty quantification prevent acceptance in the current form. With careful attention to the major and minor points below, a revised version has a good chance of acceptance in ESSD.
Major Comments
1 The performance assessment relies on 4-fold cross-validation using the same 552 soil profiles that overlap significantly with the data sources used to construct the benchmark maps (Shangguan and Liu). Therefore, the claims of outperforming existing products by 10–17% in RMSE and MRE are weakened by the lack of truly independent test data. Please provide a clearer acknowledgment of this shared-data limitation. Additionally, spatial pattern analysis (e.g., correlation of modeled thickness with topographic curvature, topographic position index, or slope) might be helpful in enhancing assessment.
2 The steady-state assumption is also a critical simplification on a tectonically active and permafrost-degrading plateau. While limitations are discussed, there is no quantitative propagation of uncertainty into the final dataset. I suggest the authors can add some sensitivity analysis. For example, vary key parameters ±20–50% or using Monte Carlo sampling for weighting coefficients and present spatial uncertainty estimates, then discuss how uncertainty varies across clusters or geomorphic zones.
Minor Comments
Abstract
- Lines 24–25: Specify the metric(s) in the performance claim (e.g., “by approximately 10–17% in RMSE and MRE”).
Study Area and Data
- Line 105: Briefly mention known limitations of the Shangguan et al. (2017) DTB dataset in the QTP interior when it is first introduced.
Methods
- Lines 165–170: Justification for excluding human activities is too brief. Note that effects are assumed negligible at 1 km resolution.
- Table 1: Move “Parent Material of Soil Formation” to a new “Geological” category.
Results
- Table 3: Add “(–)” for the unitless NDVI column.
- Table 4: Replace “-” for Cluster 8 RMSE with “N/A (n=4 insufficient for cross-validation)”. Add a footnote explaining n.
- Fig. 3: Consider adding a histogram inset or basin-specific summary statistics.
- Lines 330–340: explain why curvature shows near-zero correlation at plateau scale yet drives local differentiation in the model.
Discussion and Comparison
- Table 5: Explicitly note the shared validation data limitation. explain the higher MAE.
- Lines 470–485: Strengthen discussion of potential RUSLE/RWEQ biases in permafrost areas and suggest future use of cryogenic-specific erosion models.
- Lines 500–510: Rephrase the sentence about weighting coefficients mitigating steady-state bias to “partially mitigated”.
Citation: https://doi.org/10.5194/essd-2025-809-RC2
Data sets
Dataset associated with the paper “A physically consistent soil thickness map of the Qinghai-Tibet Plateau derived from coupled erosion mechanisms" L. Chen et al. https://doi.org/10.6084/m9.figshare.30925358
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- 1
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.
Specific comments