Mapping global distributions, environmental controls, and uncertainties of apparent topsoil and subsoil organic carbon turnover times
<p>The turnover time (<span class="inline-formula"><i>τ</i></span>) of global soil organic carbon is central to the functioning of terrestrial ecosystems. Yet our spatially explicit understanding of the depth-dependent variations and environmental controls of...
Saved in:
| Main Authors: | , , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2025-06-01
|
| Series: | Earth System Science Data |
| Online Access: | https://essd.copernicus.org/articles/17/2605/2025/essd-17-2605-2025.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849694331990966272 |
|---|---|
| author | L. Zhang L. Zhang L. Yang T. W. Crowther C. M. Zohner S. Doetterl G. B. M. Heuvelink G. B. M. Heuvelink A. M. J.-C. Wadoux A.-X. Zhu Y. Pu F. Shen H. Ma Y. Zou C. Zhou C. Zhou |
| author_facet | L. Zhang L. Zhang L. Yang T. W. Crowther C. M. Zohner S. Doetterl G. B. M. Heuvelink G. B. M. Heuvelink A. M. J.-C. Wadoux A.-X. Zhu Y. Pu F. Shen H. Ma Y. Zou C. Zhou C. Zhou |
| author_sort | L. Zhang |
| collection | DOAJ |
| description | <p>The turnover time (<span class="inline-formula"><i>τ</i></span>) of global soil organic carbon is central to the functioning of terrestrial ecosystems. Yet our spatially explicit understanding of the depth-dependent variations and environmental controls of <span class="inline-formula"><i>τ</i></span> at a global scale remains incomplete. In this study, we combine multiple state-of-the-art observation-based datasets, including over 90 000 geo-referenced soil profiles, the latest root observations distributed globally, and large numbers of satellite-derived environmental variables, to generate global maps of apparent <span class="inline-formula"><i>τ</i></span> in topsoil (0–0.3 m) and subsoil (0.3–1 m) layers, with a spatial resolution of 30 arcsec (<span class="inline-formula">∼1</span> km at the Equator). We show that subsoil <span class="inline-formula"><i>τ</i></span> (<span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M6" display="inline" overflow="scroll" dspmath="mathml"><mrow><msubsup><mn mathvariant="normal">385</mn><mn mathvariant="normal">20</mn><mn mathvariant="normal">3485</mn></msubsup></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="38pt" height="17pt" class="svg-formula" dspmath="mathimg" md5hash="a132f65dc09e708dd9108643455fa8af"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="essd-17-2605-2025-ie00001.svg" width="38pt" height="17pt" src="essd-17-2605-2025-ie00001.png"/></svg:svg></span></span> years (mean, with a variation range from the 2.5th to 97.5th percentile)) is over 8 times longer than topsoil <span class="inline-formula"><i>τ</i></span> (<span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M8" display="inline" overflow="scroll" dspmath="mathml"><mrow><msubsup><mn mathvariant="normal">15</mn><mn mathvariant="normal">11</mn><mn mathvariant="normal">137</mn></msubsup></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="28pt" height="17pt" class="svg-formula" dspmath="mathimg" md5hash="5ded7d1340a257b483e8b9ea41f869bf"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="essd-17-2605-2025-ie00002.svg" width="28pt" height="17pt" src="essd-17-2605-2025-ie00002.png"/></svg:svg></span></span> years). The cross-validation shows that the fitted machine learning models effectively captured the variabilities in <span class="inline-formula"><i>τ</i></span>, with <span class="inline-formula"><i>R</i><sup>2</sup></span> values of 0.87 and 0.70 for topsoil and subsoil <span class="inline-formula"><i>τ</i></span> mapping, respectively. The prediction uncertainties of the <span class="inline-formula"><i>τ</i></span> maps were quantified for better user applications. The environmental controls on topsoil and subsoil <span class="inline-formula"><i>τ</i></span> were investigated at global, biome, and local scales. Our analyses illustrate the ways in which temperature, water availability, physio-chemical properties, and depth jointly exert impacts on <span class="inline-formula"><i>τ</i></span>. The data-driven approaches allow us to identify their interactions, thereby enriching our comprehension of mechanisms driving nonlinear <span class="inline-formula"><i>τ</i></span>–environment relationships at global to local scales. The distributions of dominating factors of <span class="inline-formula"><i>τ</i></span> at local scales were mapped for purposes of identifying context-dependent controls on <span class="inline-formula"><i>τ</i></span> across different regions. We further reveal that the current Earth system models may underestimate <span class="inline-formula"><i>τ</i></span> by comparing model-derived maps with our observation-derived <span class="inline-formula"><i>τ</i></span> maps. The resulting maps, with new insights, as demonstrated in this study, will facilitate future modelling efforts relating to carbon cycle–climate feedbacks and support effective carbon management. The dataset is archived and freely available at <a href="https://doi.org/10.5281/zenodo.14560239">https://doi.org/10.5281/zenodo.14560239</a> (Zhang, 2025a).</p> |
| format | Article |
| id | doaj-art-4aea90122b964e6eb5f1bde8fa16f6bd |
| institution | DOAJ |
| issn | 1866-3508 1866-3516 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Earth System Science Data |
| spelling | doaj-art-4aea90122b964e6eb5f1bde8fa16f6bd2025-08-20T03:20:06ZengCopernicus PublicationsEarth System Science Data1866-35081866-35162025-06-01172605262310.5194/essd-17-2605-2025Mapping global distributions, environmental controls, and uncertainties of apparent topsoil and subsoil organic carbon turnover timesL. Zhang0L. Zhang1L. Yang2T. W. Crowther3C. M. Zohner4S. Doetterl5G. B. M. Heuvelink6G. B. M. Heuvelink7A. M. J.-C. Wadoux8A.-X. Zhu9Y. Pu10F. Shen11H. Ma12Y. Zou13C. Zhou14C. Zhou15School of Geography and Ocean Science, Nanjing University, Nanjing, ChinaClimate and Ecosystem Science Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USASchool of Geography and Ocean Science, Nanjing University, Nanjing, ChinaInstitute of Integrative Biology, ETH Zurich (Swiss Federal Institute of Technology), Zurich, SwitzerlandInstitute of Integrative Biology, ETH Zurich (Swiss Federal Institute of Technology), Zurich, SwitzerlandSoil Resources Group, Department of Environmental Systems Science, ETH, Zurich, SwitzerlandSoil Geography and Landscape Group, Wageningen University, Wageningen, the NetherlandsISRIC – World Soil Information, Wageningen, the NetherlandsLISAH, Univ. Montpellier, AgroParisTech, INRAE, IRD, L'Institut Agro, Montpellier, FranceDepartment of Geography, University of Wisconsin-Madison, Madison, WI, USASchool of Geography and Ocean Science, Nanjing University, Nanjing, ChinaSchool of Geography and Ocean Science, Nanjing University, Nanjing, ChinaSwiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmensdorf, SwitzerlandInstitute of Integrative Biology, ETH Zurich (Swiss Federal Institute of Technology), Zurich, SwitzerlandSchool of Geography and Ocean Science, Nanjing University, Nanjing, ChinaState Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China<p>The turnover time (<span class="inline-formula"><i>τ</i></span>) of global soil organic carbon is central to the functioning of terrestrial ecosystems. Yet our spatially explicit understanding of the depth-dependent variations and environmental controls of <span class="inline-formula"><i>τ</i></span> at a global scale remains incomplete. In this study, we combine multiple state-of-the-art observation-based datasets, including over 90 000 geo-referenced soil profiles, the latest root observations distributed globally, and large numbers of satellite-derived environmental variables, to generate global maps of apparent <span class="inline-formula"><i>τ</i></span> in topsoil (0–0.3 m) and subsoil (0.3–1 m) layers, with a spatial resolution of 30 arcsec (<span class="inline-formula">∼1</span> km at the Equator). We show that subsoil <span class="inline-formula"><i>τ</i></span> (<span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M6" display="inline" overflow="scroll" dspmath="mathml"><mrow><msubsup><mn mathvariant="normal">385</mn><mn mathvariant="normal">20</mn><mn mathvariant="normal">3485</mn></msubsup></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="38pt" height="17pt" class="svg-formula" dspmath="mathimg" md5hash="a132f65dc09e708dd9108643455fa8af"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="essd-17-2605-2025-ie00001.svg" width="38pt" height="17pt" src="essd-17-2605-2025-ie00001.png"/></svg:svg></span></span> years (mean, with a variation range from the 2.5th to 97.5th percentile)) is over 8 times longer than topsoil <span class="inline-formula"><i>τ</i></span> (<span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M8" display="inline" overflow="scroll" dspmath="mathml"><mrow><msubsup><mn mathvariant="normal">15</mn><mn mathvariant="normal">11</mn><mn mathvariant="normal">137</mn></msubsup></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="28pt" height="17pt" class="svg-formula" dspmath="mathimg" md5hash="5ded7d1340a257b483e8b9ea41f869bf"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="essd-17-2605-2025-ie00002.svg" width="28pt" height="17pt" src="essd-17-2605-2025-ie00002.png"/></svg:svg></span></span> years). The cross-validation shows that the fitted machine learning models effectively captured the variabilities in <span class="inline-formula"><i>τ</i></span>, with <span class="inline-formula"><i>R</i><sup>2</sup></span> values of 0.87 and 0.70 for topsoil and subsoil <span class="inline-formula"><i>τ</i></span> mapping, respectively. The prediction uncertainties of the <span class="inline-formula"><i>τ</i></span> maps were quantified for better user applications. The environmental controls on topsoil and subsoil <span class="inline-formula"><i>τ</i></span> were investigated at global, biome, and local scales. Our analyses illustrate the ways in which temperature, water availability, physio-chemical properties, and depth jointly exert impacts on <span class="inline-formula"><i>τ</i></span>. The data-driven approaches allow us to identify their interactions, thereby enriching our comprehension of mechanisms driving nonlinear <span class="inline-formula"><i>τ</i></span>–environment relationships at global to local scales. The distributions of dominating factors of <span class="inline-formula"><i>τ</i></span> at local scales were mapped for purposes of identifying context-dependent controls on <span class="inline-formula"><i>τ</i></span> across different regions. We further reveal that the current Earth system models may underestimate <span class="inline-formula"><i>τ</i></span> by comparing model-derived maps with our observation-derived <span class="inline-formula"><i>τ</i></span> maps. The resulting maps, with new insights, as demonstrated in this study, will facilitate future modelling efforts relating to carbon cycle–climate feedbacks and support effective carbon management. The dataset is archived and freely available at <a href="https://doi.org/10.5281/zenodo.14560239">https://doi.org/10.5281/zenodo.14560239</a> (Zhang, 2025a).</p>https://essd.copernicus.org/articles/17/2605/2025/essd-17-2605-2025.pdf |
| spellingShingle | L. Zhang L. Zhang L. Yang T. W. Crowther C. M. Zohner S. Doetterl G. B. M. Heuvelink G. B. M. Heuvelink A. M. J.-C. Wadoux A.-X. Zhu Y. Pu F. Shen H. Ma Y. Zou C. Zhou C. Zhou Mapping global distributions, environmental controls, and uncertainties of apparent topsoil and subsoil organic carbon turnover times Earth System Science Data |
| title | Mapping global distributions, environmental controls, and uncertainties of apparent topsoil and subsoil organic carbon turnover times |
| title_full | Mapping global distributions, environmental controls, and uncertainties of apparent topsoil and subsoil organic carbon turnover times |
| title_fullStr | Mapping global distributions, environmental controls, and uncertainties of apparent topsoil and subsoil organic carbon turnover times |
| title_full_unstemmed | Mapping global distributions, environmental controls, and uncertainties of apparent topsoil and subsoil organic carbon turnover times |
| title_short | Mapping global distributions, environmental controls, and uncertainties of apparent topsoil and subsoil organic carbon turnover times |
| title_sort | mapping global distributions environmental controls and uncertainties of apparent topsoil and subsoil organic carbon turnover times |
| url | https://essd.copernicus.org/articles/17/2605/2025/essd-17-2605-2025.pdf |
| work_keys_str_mv | AT lzhang mappingglobaldistributionsenvironmentalcontrolsanduncertaintiesofapparenttopsoilandsubsoilorganiccarbonturnovertimes AT lzhang mappingglobaldistributionsenvironmentalcontrolsanduncertaintiesofapparenttopsoilandsubsoilorganiccarbonturnovertimes AT lyang mappingglobaldistributionsenvironmentalcontrolsanduncertaintiesofapparenttopsoilandsubsoilorganiccarbonturnovertimes AT twcrowther mappingglobaldistributionsenvironmentalcontrolsanduncertaintiesofapparenttopsoilandsubsoilorganiccarbonturnovertimes AT cmzohner mappingglobaldistributionsenvironmentalcontrolsanduncertaintiesofapparenttopsoilandsubsoilorganiccarbonturnovertimes AT sdoetterl mappingglobaldistributionsenvironmentalcontrolsanduncertaintiesofapparenttopsoilandsubsoilorganiccarbonturnovertimes AT gbmheuvelink mappingglobaldistributionsenvironmentalcontrolsanduncertaintiesofapparenttopsoilandsubsoilorganiccarbonturnovertimes AT gbmheuvelink mappingglobaldistributionsenvironmentalcontrolsanduncertaintiesofapparenttopsoilandsubsoilorganiccarbonturnovertimes AT amjcwadoux mappingglobaldistributionsenvironmentalcontrolsanduncertaintiesofapparenttopsoilandsubsoilorganiccarbonturnovertimes AT axzhu mappingglobaldistributionsenvironmentalcontrolsanduncertaintiesofapparenttopsoilandsubsoilorganiccarbonturnovertimes AT ypu mappingglobaldistributionsenvironmentalcontrolsanduncertaintiesofapparenttopsoilandsubsoilorganiccarbonturnovertimes AT fshen mappingglobaldistributionsenvironmentalcontrolsanduncertaintiesofapparenttopsoilandsubsoilorganiccarbonturnovertimes AT hma mappingglobaldistributionsenvironmentalcontrolsanduncertaintiesofapparenttopsoilandsubsoilorganiccarbonturnovertimes AT yzou mappingglobaldistributionsenvironmentalcontrolsanduncertaintiesofapparenttopsoilandsubsoilorganiccarbonturnovertimes AT czhou mappingglobaldistributionsenvironmentalcontrolsanduncertaintiesofapparenttopsoilandsubsoilorganiccarbonturnovertimes AT czhou mappingglobaldistributionsenvironmentalcontrolsanduncertaintiesofapparenttopsoilandsubsoilorganiccarbonturnovertimes |