the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Mapping global distributions, environmental controls, and uncertainties of apparent top- and subsoil organic carbon turnover times
Abstract. The turnover time (τ) of global soil organic carbon is central to the functioning of terrestrial ecosystems. Yet our spatially-explicit understanding of depth-dependent variations and environmental controls of τ at a global scale remain incomplete. In this study, we combine multiple state-of-the-art observation-based datasets, including over ninety thousand geo-referenced soil profiles, the latest root observations distributed globally, and large amounts of satellite-derived environmental variables, to generate global maps of apparent τ in topsoil (0–0.3 m) and subsoil (0.3–1 m) layers with a spatial resolution of 30 arcsec (~1 km at the Equator). We show that subsoil τ (385203485 years [mean with a variation range from 2.5th to 97.5th percentile]) is over eight times longer than topsoil τ (1511137 years). The cross-validation shows that the fitted machine learning models effectively captured the variabilities in τ, with R2 values of 0.87 and 0.70 for topsoil and subsoil τ mapping, respectively. The prediction uncertainties of the τ maps were quantified for better user applications. The environmental controls on top- and subsoil τ were investigated at global, biome, and local scales. Our analyses illustrate that how temperature, water availability, physio-chemical properties and depth exert jointly impacts on τ. The data-driven approaches allow us to identify their interactions, thereby enriching our comprehension of mechanisms driving nonlinear τ–environment relationships from global to local scales. The distributions of dominating factors of τ at local scales were mapped for identifying context-dependent controls on τ across different regions. We further reveal that the current Earth system models may underestimate τ by comparing model-derived maps with our observation-derived τ maps. The resulting maps with new insights demonstrated in this study facilitate the future modelling efforts of carbon cycle–climate feedbacks and supporting effective carbon management. The dataset is archived and freely available at https://doi.org/10.5281/zenodo.14560239 (Zhang, 2025).
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RC1: 'Comment on essd-2024-618', Anonymous Referee #1, 03 Feb 2025
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This study creates global maps of soil organic carbon turnover times (τ) at both topsoil and subsoil depths with observation-based datasets and machine learning methods. It addresses an important knowledge gap regarding the global maps related to the depth-dependent variations and environmental controls of soil carbon turnover, which is essential for understanding terrestrial carbon storage and dynamics, especially in the context of climate change. The methods used are well-suited to the scale and complexity of the question being addressed. The use of over ninety thousand geo-referenced soil profiles, satellite-derived environmental variables, and machine learning models, provides a robust framework for mapping and analyzing τ across a wide range of environmental conditions and different biomes. The authors’ approach to quantify the uncertainty of their maps also adds considerable value, making their results more applicable to future carbon cycle modelling and land management efforts. In conclusion, I recommend the acceptance of this manuscript after revisions following several suggestions I listed below.
(1) Organic carbon turnover times can be calculated from influencing factors, such as carbon allocation belowground, SOC stocks. Why not first generate the spatial distribution data of those factors based on sampling data with machine learning method, then calculate organic carbon turnover times with the physical equations.
(2) From the physical equations 1 to 8, the importance of different factors could be derived. Do the importance results of RF coincide with those equations?
(3) While the methods section is thorough, some readers may benefit from further clarification on the choice of specific methods, particularly regarding the machine learning model calibration. A more explicit discussion of how the model’s hyper-parameters were tuned would help readers better appreciate the methodology’s rigor.
(4) The cross-validation procedure is well-executed, but it would be helpful to provide more specific details on how the biome-specific samples were handled during the 10-fold validation process. Ensuring that the sample division properly accounts for the geographical and ecological variation across biomes could further strengthen the model’s credibility.
(5) The paper requires careful attention to language for clarity and readability. I would recommend reviewing some descriptions for possible simplifications in sentence structure and corrections in grammar. Some examples are listed below.
- 18: “… is central to …” -> “… plays a crucial role in …”.
- 19: “remain” -> “remains”.
- 92: it is better to change the word “minimize” to “reduce”.
- 103: “layers observations” -> “layer observations”.
- 465: “insights of this study -> “insights from this study”.
Citation: https://doi.org/10.5194/essd-2024-618-RC1
Data sets
Global maps of top- and subsoil organic carbon turnover times Lei Zhang, Lin Yang, Thomas W. Crowther, Constantin M. Zohner, Sebastian Doetterl, Gerard B. M. Heuvelink, Alexandre M. J.-C. Wadoux, A.-Xing Zhu, Yue Pu, Feixue Shen, Haozhi Ma, Yibiao Zou, and Chenghu Zhou https://doi.org/10.5281/zenodo.14560239
Model code and software
Code for the analyses of global top- and subsoil organic carbon turnover times Lei Zhang https://github.com/leizhang-geo/global_soil_carbon_turnover_time.git
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