Articles | Volume 17, issue 6
https://doi.org/10.5194/essd-17-2605-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-17-2605-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
16 Jun 2025
Data description paper |
| 16 Jun 2025
| 16 Jun 2025
Mapping global distributions, environmental controls, and uncertainties of apparent topsoil and subsoil organic carbon turnover times
Lei Zhang
School of Geography and Ocean Science, Nanjing University, Nanjing, China
Climate and Ecosystem Science Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
School of Geography and Ocean Science, Nanjing University, Nanjing, China
Thomas W. Crowther
Institute of Integrative Biology, ETH Zurich (Swiss Federal Institute of Technology), Zurich, Switzerland
Constantin M. Zohner
Institute of Integrative Biology, ETH Zurich (Swiss Federal Institute of Technology), Zurich, Switzerland
Sebastian Doetterl
Soil Resources Group, Department of Environmental Systems Science, ETH, Zurich, Switzerland
Gerard B. M. Heuvelink
Soil Geography and Landscape Group, Wageningen University, Wageningen, the Netherlands
ISRIC – World Soil Information, Wageningen, the Netherlands
Alexandre M. J.-C. Wadoux
LISAH, Univ. Montpellier, AgroParisTech, INRAE, IRD, L'Institut Agro, Montpellier, France
A.-Xing Zhu
Department of Geography, University of Wisconsin-Madison, Madison, WI, USA
Yue Pu
School of Geography and Ocean Science, Nanjing University, Nanjing, China
Feixue Shen
School of Geography and Ocean Science, Nanjing University, Nanjing, China
Haozhi Ma
Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmensdorf, Switzerland
Yibiao Zou
Institute of Integrative Biology, ETH Zurich (Swiss Federal Institute of Technology), Zurich, Switzerland
Chenghu Zhou
CORRESPONDING AUTHOR
School of Geography and Ocean Science, Nanjing University, Nanjing, China
State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
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Jing-li Lu, Thomas W. Crowther, Manuel Delgado-Baquerizo, Wenjie Liu, Yamin Jiang, Hongyang Sun, and Zhiqiang Wang
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-229, https://doi.org/10.5194/essd-2025-229, 2025
Preprint under review for ESSD
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We used a global dataset to examine patterns and drivers of fungal necromass C (FNC), bacterial necromass C (BNC), and their ratio across agricultural and natural ecosystems. FNC contributed about twice as much as BNC to SOC in both systems, with higher contributions overall in agricultural soils. Soil C/N and clay content mainly drove FNC and BNC contributions, while elevation primarily influenced the FNC/BNC ratio.
Annina Maier, Maria E. Macfarlane, Marco Griepentrog, and Sebastian Doetterl
EGUsphere, https://doi.org/10.5194/egusphere-2025-2006, https://doi.org/10.5194/egusphere-2025-2006, 2025
This preprint is open for discussion and under review for Biogeosciences (BG).
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A systematic analysis of the interaction between pedo- and biosphere in shaping alpine soil organic carbon (SOC) stocks remains missing. Our regional-scale study of alpine SOC stocks across five parent materials shows that plant biomass is not a strong control of SOC stocks. Rather, the greatest SOC stocks are linked to more weathered soil profiles with higher Fe and Al pedogenic oxide content, showing the importance of parent material weatherability and geochemistry for SOC stabilization.
Xin Yang, Sijin Li, Junfei Ma, Yang Chen, Xingyu Zhou, Fayuan Li, Liyang Xiong, Chenghu Zhou, Guoan Tang, and Michael Meadows
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-401, https://doi.org/10.5194/essd-2024-401, 2024
Revised manuscript under review for ESSD
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Surveys of global landforms are important for understanding the internal and external dynamic information during the planet's evolution. This study proposes a novel framework for global landform classification and releases a novel dataset called Global Basic Landform Units (GBLU) with 1 arc-second resolution. this dataset can provide abundant and detailed geomorphological information for the field of earth sciences, facilitating further advancements in related research.
Anatol Helfenstein, Vera L. Mulder, Mirjam J. D. Hack-ten Broeke, Maarten van Doorn, Kees Teuling, Dennis J. J. Walvoort, and Gerard B. M. Heuvelink
Earth Syst. Sci. Data, 16, 2941–2970, https://doi.org/10.5194/essd-16-2941-2024, https://doi.org/10.5194/essd-16-2941-2024, 2024
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Earth system models and decision support systems greatly benefit from high-resolution soil information with quantified accuracy. Here we introduce BIS-4D, a statistical modeling platform that predicts nine essential soil properties and their uncertainties at 25 m resolution in surface 2 m across the Netherlands. Using machine learning informed by up to 856 000 soil observations coupled with 366 spatially explicit environmental variables, prediction accuracy was the highest for clay, sand and pH.
Johan Six, Sebastian Doetterl, Moritz Laub, Claude R. Müller, and Marijn Van de Broek
SOIL, 10, 275–279, https://doi.org/10.5194/soil-10-275-2024, https://doi.org/10.5194/soil-10-275-2024, 2024
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Soil C saturation has been tested in several recent studies and led to a debate about its existence. We argue that, to test C saturation, one should pay attention to six fundamental principles: the right measures, the right units, the right dispersive energy and application, the right soil type, the right clay type, and the right saturation level. Once we take care of those six rights across studies, we find support for a maximum of C stabilized by minerals and thus soil C saturation.
Gina Garland, John Koestel, Alice Johannes, Olivier Heller, Sebastian Doetterl, Dani Or, and Thomas Keller
SOIL, 10, 23–31, https://doi.org/10.5194/soil-10-23-2024, https://doi.org/10.5194/soil-10-23-2024, 2024
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The concept of soil aggregates is hotly debated, leading to confusion about their function or relevancy to soil processes. We propose that the use of conceptual figures showing detached and isolated aggregates can be misleading and has contributed to this skepticism. Here, we conceptually illustrate how aggregates can form and dissipate within the context of undisturbed soils, highlighting the fact that aggregates do not necessarily need to have distinct physical boundaries.
Shane W. Stoner, Marion Schrumpf, Alison Hoyt, Carlos A. Sierra, Sebastian Doetterl, Valier Galy, and Susan Trumbore
Biogeosciences, 20, 3151–3163, https://doi.org/10.5194/bg-20-3151-2023, https://doi.org/10.5194/bg-20-3151-2023, 2023
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Soils store more carbon (C) than any other terrestrial C reservoir, but the processes that control how much C stays in soil, and for how long, are very complex. Here, we used a recent method that involves heating soil in the lab to measure the range of C ages in soil. We found that most C in soil is decades to centuries old, while some stays for much shorter times (days to months), and some is thousands of years old. Such detail helps us to estimate how soil C may react to changing climate.
Mercedes Román Dobarco, Alexandre M. J-C. Wadoux, Brendan Malone, Budiman Minasny, Alex B. McBratney, and Ross Searle
Biogeosciences, 20, 1559–1586, https://doi.org/10.5194/bg-20-1559-2023, https://doi.org/10.5194/bg-20-1559-2023, 2023
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Soil organic carbon (SOC) is of a heterogeneous nature and varies in chemistry, stabilisation mechanisms, and persistence in soil. In this study we mapped the stocks of SOC fractions with different characteristics and turnover rates (presumably PyOC >= MAOC > POC) across Australia, combining spectroscopy and digital soil mapping. The SOC stocks (0–30 cm) were estimated as 13 Pg MAOC, 2 Pg POC, and 5 Pg PyOC.
Yongzhe Chen, Xiaoming Feng, Bojie Fu, Haozhi Ma, Constantin M. Zohner, Thomas W. Crowther, Yuanyuan Huang, Xutong Wu, and Fangli Wei
Earth Syst. Sci. Data, 15, 897–910, https://doi.org/10.5194/essd-15-897-2023, https://doi.org/10.5194/essd-15-897-2023, 2023
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This study presented a long-term (2002–2021) above- and belowground biomass dataset for woody vegetation in China at 1 km resolution. It was produced by combining various types of remote sensing observations with adequate plot measurements. Over 2002–2021, China’s woody biomass increased at a high rate, especially in the central and southern parts. This dataset can be applied to evaluate forest carbon sinks across China and the efficiency of ecological restoration programs in China.
Jun Qin, Weihao Pan, Min He, Ning Lu, Ling Yao, Hou Jiang, and Chenghu Zhou
Earth Syst. Sci. Data, 15, 331–344, https://doi.org/10.5194/essd-15-331-2023, https://doi.org/10.5194/essd-15-331-2023, 2023
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To enrich a glacial surface air temperature (SAT) product of a long time series, an ensemble learning model is constructed to estimate monthly SATs from satellite land surface temperatures at a spatial resolution of 1 km, and long-term glacial SATs from 1961 to 2020 are reconstructed using a Bayesian linear regression. This product reveals the overall warming trend and the spatial heterogeneity of warming on TP glaciers and helps to monitor glacier warming, analyze glacier evolution, etc.
Alexandre M. J.-C. Wadoux, Nicolas P. A. Saby, and Manuel P. Martin
SOIL, 9, 21–38, https://doi.org/10.5194/soil-9-21-2023, https://doi.org/10.5194/soil-9-21-2023, 2023
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We introduce Shapley values for machine learning model interpretation and reveal the local and global controlling factors of soil organic carbon (SOC) stocks. The method enables spatial analysis of the important variables. Vegetation and topography determine much of the SOC stock variation in mainland France. We conclude that SOC stock variation is complex and should be interpreted at multiple levels.
Kailiang Yu, Johan van den Hoogen, Zhiqiang Wang, Colin Averill, Devin Routh, Gabriel Reuben Smith, Rebecca E. Drenovsky, Kate M. Scow, Fei Mo, Mark P. Waldrop, Yuanhe Yang, Weize Tang, Franciska T. De Vries, Richard D. Bardgett, Peter Manning, Felipe Bastida, Sara G. Baer, Elizabeth M. Bach, Carlos García, Qingkui Wang, Linna Ma, Baodong Chen, Xianjing He, Sven Teurlincx, Amber Heijboer, James A. Bradley, and Thomas W. Crowther
Earth Syst. Sci. Data, 14, 4339–4350, https://doi.org/10.5194/essd-14-4339-2022, https://doi.org/10.5194/essd-14-4339-2022, 2022
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We used a global-scale dataset for the surface topsoil (>3000 distinct observations of abundance of soil fungi versus bacteria) to generate the first quantitative map of soil fungal proportion across terrestrial ecosystems. We reveal striking latitudinal trends. Fungi dominated in regions with low mean annual temperature (MAT) and net primary productivity (NPP) and bacteria dominated in regions with high MAT and NPP.
Moritz Mainka, Laura Summerauer, Daniel Wasner, Gina Garland, Marco Griepentrog, Asmeret Asefaw Berhe, and Sebastian Doetterl
Biogeosciences, 19, 1675–1689, https://doi.org/10.5194/bg-19-1675-2022, https://doi.org/10.5194/bg-19-1675-2022, 2022
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The largest share of terrestrial carbon is stored in soils, making them highly relevant as regards global change. Yet, the mechanisms governing soil carbon stabilization are not well understood. The present study contributes to a better understanding of these processes. We show that qualitative changes in soil organic matter (SOM) co-vary with alterations of the soil matrix following soil weathering. Hence, the type of SOM that is stabilized in soils might change as soils develop.
Pengzhi Zhao, Daniel Joseph Fallu, Sara Cucchiaro, Paolo Tarolli, Clive Waddington, David Cockcroft, Lisa Snape, Andreas Lang, Sebastian Doetterl, Antony G. Brown, and Kristof Van Oost
Biogeosciences, 18, 6301–6312, https://doi.org/10.5194/bg-18-6301-2021, https://doi.org/10.5194/bg-18-6301-2021, 2021
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We investigate the factors controlling the soil organic carbon (SOC) stability and temperature sensitivity of abandoned prehistoric agricultural terrace soils. Results suggest that the burial of former topsoil due to terracing provided an SOC stabilization mechanism. Both the soil C : N ratio and SOC mineral protection regulate soil SOC temperature sensitivity. However, which mechanism predominantly controls SOC temperature sensitivity depends on the age of the buried terrace soils.
Hou Jiang, Ling Yao, Ning Lu, Jun Qin, Tang Liu, Yujun Liu, and Chenghu Zhou
Earth Syst. Sci. Data, 13, 5389–5401, https://doi.org/10.5194/essd-13-5389-2021, https://doi.org/10.5194/essd-13-5389-2021, 2021
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A multi-resolution (0.8, 0.3, and 0.1 m) photovoltaic (PV) dataset is established using satellite and aerial images. The dataset contains 3716 samples of PVs installed on various land and rooftop types. The dataset can support multi-scale PV segmentation (e.g., concentrated PVs, distributed ground PVs, and fine-grained rooftop PVs) and cross applications between different resolutions (e.g., from satellite to aerial samples and vice versa), as well as other research related to PVs.
Chongyang Wang, Li Wang, Danni Wang, Dan Li, Chenghu Zhou, Hao Jiang, Qiong Zheng, Shuisen Chen, Kai Jia, Yangxiaoyue Liu, Ji Yang, Xia Zhou, and Yong Li
Geosci. Model Dev., 14, 6833–6846, https://doi.org/10.5194/gmd-14-6833-2021, https://doi.org/10.5194/gmd-14-6833-2021, 2021
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The turbidity maximum zone (TMZ) is a special phenomenon in estuaries worldwide. However, the extraction methods and criteria used to describe the TMZ vary significantly both spatially and temporally. This study proposes an new index, the turbidity maximum zone index, based on the corresponding relationship of total suspended solid concentration and Chl a concentration, which could better extract TMZs in different estuaries and on different dates.
Laura Summerauer, Philipp Baumann, Leonardo Ramirez-Lopez, Matti Barthel, Marijn Bauters, Benjamin Bukombe, Mario Reichenbach, Pascal Boeckx, Elizabeth Kearsley, Kristof Van Oost, Bernard Vanlauwe, Dieudonné Chiragaga, Aimé Bisimwa Heri-Kazi, Pieter Moonen, Andrew Sila, Keith Shepherd, Basile Bazirake Mujinya, Eric Van Ranst, Geert Baert, Sebastian Doetterl, and Johan Six
SOIL, 7, 693–715, https://doi.org/10.5194/soil-7-693-2021, https://doi.org/10.5194/soil-7-693-2021, 2021
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We present a soil mid-infrared library with over 1800 samples from central Africa in order to facilitate soil analyses of this highly understudied yet critical area. Together with an existing continental library, we demonstrate a regional analysis and geographical extrapolation to predict total carbon and nitrogen. Our results show accurate predictions and highlight the value that the data contribute to existing libraries. Our library is openly available for public use and for expansion.
Benjamin Bukombe, Peter Fiener, Alison M. Hoyt, Laurent K. Kidinda, and Sebastian Doetterl
SOIL, 7, 639–659, https://doi.org/10.5194/soil-7-639-2021, https://doi.org/10.5194/soil-7-639-2021, 2021
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Through a laboratory incubation experiment, we investigated the spatial patterns of specific maximum heterotrophic respiration in tropical African mountain forest soils developed from contrasting parent material along slope gradients. We found distinct differences in soil respiration between soil depths and geochemical regions related to soil fertility and the chemistry of the soil solution. The topographic origin of our samples was not a major determinant of the observed rates of respiration.
Sebastian Doetterl, Rodrigue K. Asifiwe, Geert Baert, Fernando Bamba, Marijn Bauters, Pascal Boeckx, Benjamin Bukombe, Georg Cadisch, Matthew Cooper, Landry N. Cizungu, Alison Hoyt, Clovis Kabaseke, Karsten Kalbitz, Laurent Kidinda, Annina Maier, Moritz Mainka, Julia Mayrock, Daniel Muhindo, Basile B. Mujinya, Serge M. Mukotanyi, Leon Nabahungu, Mario Reichenbach, Boris Rewald, Johan Six, Anna Stegmann, Laura Summerauer, Robin Unseld, Bernard Vanlauwe, Kristof Van Oost, Kris Verheyen, Cordula Vogel, Florian Wilken, and Peter Fiener
Earth Syst. Sci. Data, 13, 4133–4153, https://doi.org/10.5194/essd-13-4133-2021, https://doi.org/10.5194/essd-13-4133-2021, 2021
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The African Tropics are hotspots of modern-day land use change and are of great relevance for the global carbon cycle. Here, we present data collected as part of the DFG-funded project TropSOC along topographic, land use, and geochemical gradients in the eastern Congo Basin and the Albertine Rift. Our database contains spatial and temporal data on soil, vegetation, environmental properties, and land management collected from 136 pristine tropical forest and cropland plots between 2017 and 2020.
Mario Reichenbach, Peter Fiener, Gina Garland, Marco Griepentrog, Johan Six, and Sebastian Doetterl
SOIL, 7, 453–475, https://doi.org/10.5194/soil-7-453-2021, https://doi.org/10.5194/soil-7-453-2021, 2021
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In deeply weathered tropical rainforest soils of Africa, we found that patterns of soil organic carbon stocks differ between soils developed from geochemically contrasting parent material due to differences in the abundance of organo-mineral complexes, the presence/absence of chemical stabilization mechanisms of carbon with minerals and the presence of fossil organic carbon from sedimentary rocks. Physical stabilization mechanisms by aggregation provide additional protection of soil carbon.
Joseph Tamale, Roman Hüppi, Marco Griepentrog, Laban Frank Turyagyenda, Matti Barthel, Sebastian Doetterl, Peter Fiener, and Oliver van Straaten
SOIL, 7, 433–451, https://doi.org/10.5194/soil-7-433-2021, https://doi.org/10.5194/soil-7-433-2021, 2021
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Soil greenhouse gas (GHG) fluxes were measured monthly from nitrogen (N), phosphorous (P), N and P, and control plots of the first nutrient manipulation experiment located in an African pristine tropical forest using static chambers. The results suggest (1) contrasting soil GHG responses to nutrient addition, hence highlighting the complexity of the tropical forests, and (2) that the feedback of tropical forests to the global soil GHG budget could be altered by changes in N and P availability.
Florian Wilken, Peter Fiener, Michael Ketterer, Katrin Meusburger, Daniel Iragi Muhindo, Kristof van Oost, and Sebastian Doetterl
SOIL, 7, 399–414, https://doi.org/10.5194/soil-7-399-2021, https://doi.org/10.5194/soil-7-399-2021, 2021
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This study demonstrates the usability of fallout radionuclides 239Pu and 240Pu as a tool to assess soil degradation processes in tropical Africa, which is particularly valuable in regions with limited infrastructure and challenging monitoring conditions for landscape-scale soil degradation monitoring. The study shows no indication of soil redistribution in forest sites but substantial soil redistribution in cropland (sedimentation >40 cm in 55 years) with high variability.
Sophie F. von Fromm, Alison M. Hoyt, Markus Lange, Gifty E. Acquah, Ermias Aynekulu, Asmeret Asefaw Berhe, Stephan M. Haefele, Steve P. McGrath, Keith D. Shepherd, Andrew M. Sila, Johan Six, Erick K. Towett, Susan E. Trumbore, Tor-G. Vågen, Elvis Weullow, Leigh A. Winowiecki, and Sebastian Doetterl
SOIL, 7, 305–332, https://doi.org/10.5194/soil-7-305-2021, https://doi.org/10.5194/soil-7-305-2021, 2021
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We investigated various soil and climate properties that influence soil organic carbon (SOC) concentrations in sub-Saharan Africa. Our findings indicate that climate and geochemistry are equally important for explaining SOC variations. The key SOC-controlling factors are broadly similar to those for temperate regions, despite differences in soil development history between the two regions.
Laura Poggio, Luis M. de Sousa, Niels H. Batjes, Gerard B. M. Heuvelink, Bas Kempen, Eloi Ribeiro, and David Rossiter
SOIL, 7, 217–240, https://doi.org/10.5194/soil-7-217-2021, https://doi.org/10.5194/soil-7-217-2021, 2021
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This paper focuses on the production of global maps of soil properties with quantified spatial uncertainty, as implemented in the SoilGrids version 2.0 product using DSM practices and adapting them for global digital soil mapping with legacy data. The quantitative evaluation showed metrics in line with previous studies. The qualitative evaluation showed that coarse-scale patterns are well reproduced. The spatial uncertainty at global scale highlighted the need for more soil observations.
Jairo Arturo Torres-Matallana, Ulrich Leopold, and Gerard B. M. Heuvelink
Hydrol. Earth Syst. Sci., 25, 193–216, https://doi.org/10.5194/hess-25-193-2021, https://doi.org/10.5194/hess-25-193-2021, 2021
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This study aimed to select and characterise the main sources of input uncertainty in urban sewer systems, while accounting for temporal correlations of uncertain model inputs, by propagating input uncertainty through the model. We discuss the water quality impact of the model outputs to the environment, specifically in combined sewer systems, in relation to the uncertainty analysis, which constitutes valuable information for the environmental authorities and decision-makers.
Simon Baumgartner, Matti Barthel, Travis William Drake, Marijn Bauters, Isaac Ahanamungu Makelele, John Kalume Mugula, Laura Summerauer, Nora Gallarotti, Landry Cizungu Ntaboba, Kristof Van Oost, Pascal Boeckx, Sebastian Doetterl, Roland Anton Werner, and Johan Six
Biogeosciences, 17, 6207–6218, https://doi.org/10.5194/bg-17-6207-2020, https://doi.org/10.5194/bg-17-6207-2020, 2020
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Soil respiration is an important carbon flux and key process determining the net ecosystem production of terrestrial ecosystems. The Congo Basin lacks studies quantifying carbon fluxes. We measured soil CO2 fluxes from different forest types in the Congo Basin and were able to show that, even though soil CO2 fluxes are similarly high in lowland and montane forests, the drivers were different: soil moisture in montane forests and C availability in the lowland forests.
Laurent K. Kidinda, Folasade K. Olagoke, Cordula Vogel, Karsten Kalbitz, and Sebastian Doetterl
SOIL Discuss., https://doi.org/10.5194/soil-2020-80, https://doi.org/10.5194/soil-2020-80, 2020
Preprint withdrawn
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In deeply weathered tropical rainforest soils of Africa, we found that patterns of microbial processes differ between soils developed from geochemically contrasting parent materials due to differences in resource availability. Across investigated geochemical regions and soil depths, soil microbes were P-limited rather than N-limited. Topsoil microbes were more C-limited than their subsoil counterparts but inversely P-limited.
Marco Pfeiffer, José Padarian, Rodrigo Osorio, Nelson Bustamante, Guillermo Federico Olmedo, Mario Guevara, Felipe Aburto, Francisco Albornoz, Monica Antilén, Elías Araya, Eduardo Arellano, Maialen Barret, Juan Barrera, Pascal Boeckx, Margarita Briceño, Sally Bunning, Lea Cabrol, Manuel Casanova, Pablo Cornejo, Fabio Corradini, Gustavo Curaqueo, Sebastian Doetterl, Paola Duran, Mauricio Escudey, Angelina Espinoza, Samuel Francke, Juan Pablo Fuentes, Marcel Fuentes, Gonzalo Gajardo, Rafael García, Audrey Gallaud, Mauricio Galleguillos, Andrés Gomez, Marcela Hidalgo, Jorge Ivelic-Sáez, Lwando Mashalaba, Francisco Matus, Francisco Meza, Maria de la Luz Mora, Jorge Mora, Cristina Muñoz, Pablo Norambuena, Carolina Olivera, Carlos Ovalle, Marcelo Panichini, Aníbal Pauchard, Jorge F. Pérez-Quezada, Sergio Radic, José Ramirez, Nicolás Riveras, Germán Ruiz, Osvaldo Salazar, Iván Salgado, Oscar Seguel, Maria Sepúlveda, Carlos Sierra, Yasna Tapia, Francisco Tapia, Balfredo Toledo, José Miguel Torrico, Susana Valle, Ronald Vargas, Michael Wolff, and Erick Zagal
Earth Syst. Sci. Data, 12, 457–468, https://doi.org/10.5194/essd-12-457-2020, https://doi.org/10.5194/essd-12-457-2020, 2020
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The CHLSOC database is the biggest soil organic carbon (SOC) database that has been compiled for Chile yet, comprising 13 612 data points. This database is the product of the compilation of numerous sources including unpublished and difficult-to-access data, allowing us to fill numerous spatial gaps where no SOC estimates were publicly available before. The values of SOC compiled in CHLSOC have a wide range, reflecting the variety of ecosystems that exists in Chile.
Corey R. Lawrence, Jeffrey Beem-Miller, Alison M. Hoyt, Grey Monroe, Carlos A. Sierra, Shane Stoner, Katherine Heckman, Joseph C. Blankinship, Susan E. Crow, Gavin McNicol, Susan Trumbore, Paul A. Levine, Olga Vindušková, Katherine Todd-Brown, Craig Rasmussen, Caitlin E. Hicks Pries, Christina Schädel, Karis McFarlane, Sebastian Doetterl, Christine Hatté, Yujie He, Claire Treat, Jennifer W. Harden, Margaret S. Torn, Cristian Estop-Aragonés, Asmeret Asefaw Berhe, Marco Keiluweit, Ágatha Della Rosa Kuhnen, Erika Marin-Spiotta, Alain F. Plante, Aaron Thompson, Zheng Shi, Joshua P. Schimel, Lydia J. S. Vaughn, Sophie F. von Fromm, and Rota Wagai
Earth Syst. Sci. Data, 12, 61–76, https://doi.org/10.5194/essd-12-61-2020, https://doi.org/10.5194/essd-12-61-2020, 2020
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The International Soil Radiocarbon Database (ISRaD) is an an open-source archive of soil data focused on datasets including radiocarbon measurements. ISRaD includes data from bulk or
whole soils, distinct soil carbon pools isolated in the laboratory by a variety of soil fractionation methods, samples of soil gas or water collected interstitially from within an intact soil profile, CO2 gas isolated from laboratory soil incubations, and fluxes collected in situ from a soil surface.
Jiawei Yi, Yunyan Du, Fuyuan Liang, Tao Pei, Ting Ma, and Chenghu Zhou
Nat. Hazards Earth Syst. Sci., 19, 2169–2182, https://doi.org/10.5194/nhess-19-2169-2019, https://doi.org/10.5194/nhess-19-2169-2019, 2019
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This paper utilized the advantages of smartphone location data to study human responses to rainstorm disasters. Intense rainstorms disrupt city residents' behaviors as reflected in anomalies of location-based service requests. Anomaly identification from fine-scale smartphone location data facilitates the monitoring of social responses to rainstorms. Residents' collective geotagged behaviors in different cities show different sensitivities to rainstorms.
Ziyue Chen, Danlu Chen, Wei Wen, Yan Zhuang, Mei-Po Kwan, Bin Chen, Bo Zhao, Lin Yang, Bingbo Gao, Ruiyuan Li, and Bing Xu
Atmos. Chem. Phys., 19, 6879–6891, https://doi.org/10.5194/acp-19-6879-2019, https://doi.org/10.5194/acp-19-6879-2019, 2019
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This research is the first attempt to comprehensively evaluate the recent
2+26strategy for air quality improvement in Beijing. We additionally considered two corresponding pollution episodes with different emission-reduction strategies to comprehensively understand the effects of
2+26strategy. The findings suggested that red alerts be employed in heavy pollution episodes to intentionally reduce vehicle exhaust, which has become the dominant source for high PM2.5 concentrations in Beijing.
Alexandre M. J.-C. Wadoux, José Padarian, and Budiman Minasny
SOIL, 5, 107–119, https://doi.org/10.5194/soil-5-107-2019, https://doi.org/10.5194/soil-5-107-2019, 2019
Changchun Huang, Quanliang Jiang, Ling Yao, Hao Yang, Chen Lin, Tao Huang, A-Xing Zhu, and Yimin Zhang
Biogeosciences, 15, 1827–1841, https://doi.org/10.5194/bg-15-1827-2018, https://doi.org/10.5194/bg-15-1827-2018, 2018
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The latitudinal dependency of POC / PON in ocean and inland water is significant, regulated by trophic state and climate, etc. factors. POC / PON significantly increased from coastal water (6.89 ± 2.38) to open ocean (7.59 ± 4.22) with the increasing rate of 0.0024 / km. The re-examination of the global relationship between, and variations in, POC and PON could be important for the global and regional coupling between the carbon and nitrogen cycles in the ocean and freshwater.
Manoranjan Muthusamy, Alma Schellart, Simon Tait, and Gerard B. M. Heuvelink
Hydrol. Earth Syst. Sci., 21, 1077–1091, https://doi.org/10.5194/hess-21-1077-2017, https://doi.org/10.5194/hess-21-1077-2017, 2017
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In this study we develop a method to estimate the spatially averaged rainfall intensity together with associated level of uncertainty using geostatistical upscaling. Rainfall data collected from a cluster of eight paired rain gauges in a small urban catchment are used in this study. Results show that the prediction uncertainty comes mainly from two sources: spatial variability of rainfall and measurement error. Results from this study can be used for uncertainty analyses of hydrologic modelling.
W. Marijn van der Meij, Arnaud J. A. M. Temme, Christian M. F. J. J. de Kleijn, Tony Reimann, Gerard B. M. Heuvelink, Zbigniew Zwoliński, Grzegorz Rachlewicz, Krzysztof Rymer, and Michael Sommer
SOIL, 2, 221–240, https://doi.org/10.5194/soil-2-221-2016, https://doi.org/10.5194/soil-2-221-2016, 2016
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This study combined fieldwork, geochronology and modelling to get a better understanding of Arctic soil development on a landscape scale. Main processes are aeolian deposition, physical and chemical weathering and silt translocation. Discrepancies between model results and field observations showed that soil and landscape development is not as straightforward as we hypothesized. Interactions between landscape processes and soil processes have resulted in a complex soil pattern in the landscape.
S. Doetterl, J.-T. Cornelis, J. Six, S. Bodé, S. Opfergelt, P. Boeckx, and K. Van Oost
Biogeosciences, 12, 1357–1371, https://doi.org/10.5194/bg-12-1357-2015, https://doi.org/10.5194/bg-12-1357-2015, 2015
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We link the mineralogy of soils affected by erosion and deposition to the distribution of soil carbon fractions, their turnover and microbial activity. We show that the weathering status of soils and their history are controlling the stabilization of carbon with minerals. After burial, aggregated C is preserved more efficiently while non-aggregated C can be released and younger C re-sequestered more easily. Weathering changes the effectiveness of stabilization mechanism limiting this C sink.
J. Yi, Y. Du, Z. He, and C. Zhou
Ocean Sci., 10, 39–48, https://doi.org/10.5194/os-10-39-2014, https://doi.org/10.5194/os-10-39-2014, 2014
D. R. Cameron, M. Van Oijen, C. Werner, K. Butterbach-Bahl, R. Grote, E. Haas, G. B. M. Heuvelink, R. Kiese, J. Kros, M. Kuhnert, A. Leip, G. J. Reinds, H. I. Reuter, M. J. Schelhaas, W. De Vries, and J. Yeluripati
Biogeosciences, 10, 1751–1773, https://doi.org/10.5194/bg-10-1751-2013, https://doi.org/10.5194/bg-10-1751-2013, 2013
J. Yi, Y. Du, X. Wang, Z. He, and C. Zhou
Ocean Sci., 9, 171–182, https://doi.org/10.5194/os-9-171-2013, https://doi.org/10.5194/os-9-171-2013, 2013
Related subject area
Domain: ESSD – Land | Subject: Pedology
Distribution and sources of fallout 137Cs and 239+240Pu in equatorial and Southern Hemisphere reference soils
A China dataset of soil properties for land surface modelling (version 2, CSDLv2)
An integrated dataset of ground hydrothermal regimes and soil nutrients monitored in some previously burned areas in hemiboreal forests in Northeast China during 2016–2022
Providing quality-assessed and standardised soil data to support global mapping and modelling (WoSIS snapshot 2023)
Global patterns of soil organic carbon dynamics in the 20–100 cm soil profile for different ecosystems: A global meta-analysis
BIS-4D: mapping soil properties and their uncertainties at 25 m resolution in the Netherlands
European topsoil bulk density and organic carbon stock database (0–20 cm) using machine-learning-based pedotransfer functions
Improving the Latin America and Caribbean Soil Information System (SISLAC) database enhances its usability and scalability
The patterns of soil nitrogen stocks and C : N stoichiometry under impervious surfaces in China
Mapping of peatlands in the forested landscape of Sweden using lidar-based terrain indices
Harmonized Soil Database of Ecuador (HESD): data from 2009 to 2015
ChinaCropSM1 km: a fine 1 km daily soil moisture dataset for dryland wheat and maize across China during 1993–2018
Colombian soil texture: building a spatial ensemble model
SGD-SM 2.0: an improved seamless global daily soil moisture long-term dataset from 2002 to 2022
A high spatial resolution soil carbon and nitrogen dataset for the northern permafrost region based on circumpolar land cover upscaling
A repository of measured soil freezing characteristic curves: 1921 to 2021
A compiled soil respiration dataset at different time scales for forest ecosystems across China from 2000 to 2018
Gerald Dicen, Floriane Guillevic, Surya Gupta, Pierre-Alexis Chaboche, Katrin Meusburger, Pierre Sabatier, Olivier Evrard, and Christine Alewell
Earth Syst. Sci. Data, 17, 1529–1549, https://doi.org/10.5194/essd-17-1529-2025, https://doi.org/10.5194/essd-17-1529-2025, 2025
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Fallout radionuclides (FRNs) such as 137Cs and 239+240Pu are considered to be critical tools in various environmental research. Here, we compiled reference soil data on these FRNs from the literature to build a comprehensive database. Using this database, we determined the distribution and sources of 137Cs and 239+240Pu. We also demonstrated how the database can be used to identify the environmental factors that influence their distribution using a machine learning algorithm.
Gaosong Shi, Wenye Sun, Wei Shangguan, Zhongwang Wei, Hua Yuan, Lu Li, Xiaolin Sun, Ye Zhang, Hongbin Liang, Danxi Li, Feini Huang, Qingliang Li, and Yongjiu Dai
Earth Syst. Sci. Data, 17, 517–543, https://doi.org/10.5194/essd-17-517-2025, https://doi.org/10.5194/essd-17-517-2025, 2025
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In this study, we developed the second version of China's high-resolution soil information grid using legacy soil samples and advanced machine learning. This version predicts over 20 soil properties at six depths, providing accurate soil variation maps across China. It outperforms previous versions and global products, offering valuable data for hydrological and ecological analyses and Earth system modelling, enhancing our understanding of soil roles in environmental processes.
Xiaoying Li, Huijun Jin, Qi Feng, Qingbai Wu, Hongwei Wang, Ruixia He, Dongliang Luo, Xiaoli Chang, Raul-David Şerban, and Tao Zhan
Earth Syst. Sci. Data, 16, 5009–5026, https://doi.org/10.5194/essd-16-5009-2024, https://doi.org/10.5194/essd-16-5009-2024, 2024
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In Northeast China, the permafrost is more sensitive to climate warming and fire disturbances than the boreal and Arctic permafrost. Since 2016, a continuous ground hydrothermal regime and soil nutrient content observation system has been gradually established in Northeast China. The integrated dataset includes soil moisture content, soil organic carbon, total nitrogen, total phosphorus, total potassium, ground temperatures at depths of 0–20 m, and active layer thickness from 2016 to 2022.
Niels H. Batjes, Luis Calisto, and Luis M. de Sousa
Earth Syst. Sci. Data, 16, 4735–4765, https://doi.org/10.5194/essd-16-4735-2024, https://doi.org/10.5194/essd-16-4735-2024, 2024
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Soils are an important provider of ecosystem services. This dataset provides quality-assessed and standardised soil data to support digital soil mapping and environmental applications at a broad scale. The underpinning soil profiles were shared by a wide range of data providers. Special attention was paid to the standardisation of soil property definitions, analytical method descriptions and property values. We present three measures to assess "fitness for intended use" of the standardised data.
Haiyan Wang, Yulong Yin, Tingyao Cai, Xingshuai Tian, Zhong Chen, Kai He, Zihan Wang, Haiqing Gong, Qi Miao, Yingcheng Wang, Yiyan Chu, Qingsong Zhang, Minghao Zhuang, and Zhengling Cui
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-100, https://doi.org/10.5194/essd-2024-100, 2024
Revised manuscript accepted for ESSD
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Accurately quantifying the distribution of soil profile SOC stocks is crucial for C sequestration and mitigation. The detailed spatial subsoil SOC data are the scientific basis for environmental protection as well as the development of Earth system models. Based on multiple environmental variables and soil profile data, this study use machine-learning approaches to evaluate evaluated the SOC stocks and their spatial distribution at a depth interval of 0–1 m of various ecosystems.
Anatol Helfenstein, Vera L. Mulder, Mirjam J. D. Hack-ten Broeke, Maarten van Doorn, Kees Teuling, Dennis J. J. Walvoort, and Gerard B. M. Heuvelink
Earth Syst. Sci. Data, 16, 2941–2970, https://doi.org/10.5194/essd-16-2941-2024, https://doi.org/10.5194/essd-16-2941-2024, 2024
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Earth system models and decision support systems greatly benefit from high-resolution soil information with quantified accuracy. Here we introduce BIS-4D, a statistical modeling platform that predicts nine essential soil properties and their uncertainties at 25 m resolution in surface 2 m across the Netherlands. Using machine learning informed by up to 856 000 soil observations coupled with 366 spatially explicit environmental variables, prediction accuracy was the highest for clay, sand and pH.
Songchao Chen, Zhongxing Chen, Xianglin Zhang, Zhongkui Luo, Calogero Schillaci, Dominique Arrouays, Anne Christine Richer-de-Forges, and Zhou Shi
Earth Syst. Sci. Data, 16, 2367–2383, https://doi.org/10.5194/essd-16-2367-2024, https://doi.org/10.5194/essd-16-2367-2024, 2024
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A new dataset for topsoil bulk density (BD) and soil organic carbon (SOC) stock (0–20 cm) across Europe using machine learning was generated. The proposed approach performed better in BD prediction and slightly better in SOC stock prediction than earlier-published PTFs. The outcomes present a meaningful advancement in enhancing the accuracy of BD, and the resultant topsoil BD and SOC stock datasets across Europe enable more precise soil hydrological and biological modeling.
Sergio Díaz-Guadarrama, Viviana M. Varón-Ramírez, Iván Lizarazo, Mario Guevara, Marcos Angelini, Gustavo A. Araujo-Carrillo, Jainer Argeñal, Daphne Armas, Rafael A. Balta, Adriana Bolivar, Nelson Bustamante, Ricardo O. Dart, Martin Dell Acqua, Arnulfo Encina, Hernán Figueredo, Fernando Fontes, Joan S. Gutiérrez-Díaz, Wilmer Jiménez, Raúl S. Lavado, Jesús F. Mansilla-Baca, Maria de Lourdes Mendonça-Santos, Lucas M. Moretti, Iván D. Muñoz, Carolina Olivera, Guillermo Olmedo, Christian Omuto, Sol Ortiz, Carla Pascale, Marco Pfeiffer, Iván A. Ramos, Danny Ríos, Rafael Rivera, Lady M. Rodriguez, Darío M. Rodríguez, Albán Rosales, Kenset Rosales, Guillermo Schulz, Víctor Sevilla, Leonardo M. Tenti, Ronald Vargas, Gustavo M. Vasques, Yusuf Yigini, and Yolanda Rubiano
Earth Syst. Sci. Data, 16, 1229–1246, https://doi.org/10.5194/essd-16-1229-2024, https://doi.org/10.5194/essd-16-1229-2024, 2024
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In this work, the Latin America and Caribbean Soil Information System (SISLAC) database (https://54.229.242.119/sislac/es) was revised to generate an improved version of the data. Rules for data enhancement were defined. In addition, other datasets available in the region were included. Subsequently, through a principal component analysis (PCA), the main soil characteristics for the region were analyzed. We hope this dataset can help mitigate problems such as food security and global warming.
Qian Ding, Hua Shao, Chi Zhang, and Xia Fang
Earth Syst. Sci. Data, 15, 4599–4612, https://doi.org/10.5194/essd-15-4599-2023, https://doi.org/10.5194/essd-15-4599-2023, 2023
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A soil survey in 41 Chinese cities showed the soil nitrogen (N) in impervious surface areas (ISA; NISA) was 0.59±0.35 kg m−2, lower than in pervious soils. Eastern China had the highest NISA but the lowest natural soil N in China. Soil N decreased linearly with depth in ISA but nonlinearly in natural ecosystems. Temperature was negatively correlated with C : NISA but positively correlated with natural soil C : N. The unique NISA patterns imply intensive disturbance in N cycle by soil sealing.
Lukas Rimondini, Thomas Gumbricht, Anders Ahlström, and Gustaf Hugelius
Earth Syst. Sci. Data, 15, 3473–3482, https://doi.org/10.5194/essd-15-3473-2023, https://doi.org/10.5194/essd-15-3473-2023, 2023
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Peatlands have historically sequestrated large amounts of carbon and contributed to atmospheric cooling. However, human activities and climate change may instead turn them into considerable carbon emitters. In this study, we produced high-quality maps showing the extent of peatlands in the forests of Sweden, one of the most peatland-dense countries in the world. The maps are publicly available and may be used to support work promoting sustainable peatland management and combat their degradation.
Daphne Armas, Mario Guevara, Fernando Bezares, Rodrigo Vargas, Pilar Durante, Víctor Osorio, Wilmer Jiménez, and Cecilio Oyonarte
Earth Syst. Sci. Data, 15, 431–445, https://doi.org/10.5194/essd-15-431-2023, https://doi.org/10.5194/essd-15-431-2023, 2023
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The global need for updated soil datasets has increased. Our main objective was to synthesize and harmonize soil profile information collected by two different projects in Ecuador between 2009 and 2015.The main result was the development of the Harmonized Soil Database of Ecuador (HESD) that includes information from 13 542 soil profiles with over 51 713 measured soil horizons, including 92 different edaphic variables, and follows international standards for archiving and sharing soil data.
Fei Cheng, Zhao Zhang, Huimin Zhuang, Jichong Han, Yuchuan Luo, Juan Cao, Liangliang Zhang, Jing Zhang, Jialu Xu, and Fulu Tao
Earth Syst. Sci. Data, 15, 395–409, https://doi.org/10.5194/essd-15-395-2023, https://doi.org/10.5194/essd-15-395-2023, 2023
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We generated a 1 km daily soil moisture dataset for dryland wheat and maize across China (ChinaCropSM1 km) over 1993–2018 through random forest regression, based on in situ observations. Our improved products have a remarkably better quality compared with the public global products in terms of both spatial and time dimensions by integrating an irrigation module (crop type, phenology, soil depth). The dataset may be useful for agriculture drought monitoring and crop yield forecasting studies.
Viviana Marcela Varón-Ramírez, Gustavo Alfonso Araujo-Carrillo, and Mario Antonio Guevara Santamaría
Earth Syst. Sci. Data, 14, 4719–4741, https://doi.org/10.5194/essd-14-4719-2022, https://doi.org/10.5194/essd-14-4719-2022, 2022
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These are the first national soil texture maps obtained via digital soil mapping. We built clay, sand, and silt maps using spatial assembling with the best possible predictions at different depths. Also, we identified the better model for each pixel. This work was done to address the lack of soil texture maps in Colombia, and it can provide soil information for water-related applications, ecosystem services, and agricultural and crop modeling.
Qiang Zhang, Qiangqiang Yuan, Taoyong Jin, Meiping Song, and Fujun Sun
Earth Syst. Sci. Data, 14, 4473–4488, https://doi.org/10.5194/essd-14-4473-2022, https://doi.org/10.5194/essd-14-4473-2022, 2022
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Compared to previous seamless global daily soil moisture (SGD-SM 1.0) products, SGD-SM 2.0 enlarges the temporal scope from 2002 to 2022. By fusing auxiliary precipitation information with the long short-term memory convolutional neural network (LSTM-CNN) model, SGD-SM 2.0 can consider sudden extreme weather conditions for 1 d in global daily soil moisture products and is significant for full-coverage global daily hydrologic monitoring, rather than averaging monthly–quarterly–yearly results.
Juri Palmtag, Jaroslav Obu, Peter Kuhry, Andreas Richter, Matthias B. Siewert, Niels Weiss, Sebastian Westermann, and Gustaf Hugelius
Earth Syst. Sci. Data, 14, 4095–4110, https://doi.org/10.5194/essd-14-4095-2022, https://doi.org/10.5194/essd-14-4095-2022, 2022
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The northern permafrost region covers 22 % of the Northern Hemisphere and holds almost twice as much carbon as the atmosphere. This paper presents data from 651 soil pedons encompassing more than 6500 samples from 16 different study areas across the northern permafrost region. We use this dataset together with ESA's global land cover dataset to estimate soil organic carbon and total nitrogen storage up to 300 cm soil depth, with estimated values of 813 Pg for carbon and 55 Pg for nitrogen.
Élise G. Devoie, Stephan Gruber, and Jeffrey M. McKenzie
Earth Syst. Sci. Data, 14, 3365–3377, https://doi.org/10.5194/essd-14-3365-2022, https://doi.org/10.5194/essd-14-3365-2022, 2022
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Soil freezing characteristic curves (SFCCs) relate the temperature of a soil to its ice content. SFCCs are needed in all physically based numerical models representing freezing and thawing soils, and they affect the movement of water in the subsurface, biogeochemical processes, soil mechanics, and ecology. Over a century of SFCC data exist, showing high variability in SFCCs based on soil texture, water content, and other factors. This repository summarizes all available SFCC data and metadata.
Hongru Sun, Zhenzhu Xu, and Bingrui Jia
Earth Syst. Sci. Data, 14, 2951–2961, https://doi.org/10.5194/essd-14-2951-2022, https://doi.org/10.5194/essd-14-2951-2022, 2022
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We compiled a new soil respiration (Rs) database of China's forests from 568 studies published up to 2018. The hourly, monthly, and annual samples were 8317, 5003, and 634, respectively. Most of the Rs data are shown in figures but were seldom exploited. For the first time, these data were digitized, accounting for 82 % of samples. Rs measured with common methods was selected (Li-6400, Li-8100, Li-8150, gas chromatography) and showed small differences of ~10 %. Bamboo had the highest Rs.
Cited articles
Altmann, A., Toloşi, L., Sander, O., and Lengauer, T.: Permutation importance: a corrected feature importance measure, Bioinformatics, 26, 1340–1347, https://doi.org/10.1093/bioinformatics/btq134, 2010.
Arrouays, D., McBratney, A., Minasny, B., Hempel, J., Heuvelink, G., MacMillan, R., Hartemink, A., Lagacherie, P., and McKenzie, N.: The GlobalSoilMap project specifications, in: GlobalSoilMap, edited by: Arrouays, D., McKenzie, N., Hempel, J., de Forges, A., and McBratney, A., CRC Press, 9–12, ISBN 978-1-138-00119-0, 2014.
Bahram, M., Hildebrand, F., Forslund, S. K., Anderson, J. L., Soudzilovskaia, N. A., Bodegom, P. M., Bengtsson-Palme, J., Anslan, S., Coelho, L. P., Harend, H., Huerta-Cepas, J., Medema, M. H., Maltz, M. R., Mundra, S., Olsson, P. A., Pent, M., Põlme, S., Sunagawa, S., Ryberg, M., Tedersoo, L., and Bork, P.: Structure and function of the global topsoil microbiome, Nature, 560, 233–237, https://doi.org/10.1038/s41586-018-0386-6, 2018.
Balesdent, J., Basile-Doelsch, I., Chadoeuf, J., Cornu, S., Derrien, D., Fekiacova, Z., and Hatté, C.: Atmosphere–soil carbon transfer as a function of soil depth, Nature, 559, 599–602, https://doi.org/10.1038/s41586-018-0328-3, 2018.
Bardgett, R. D., Mommer, L., and Vries, F. T. D.: Going underground: root traits as drivers of ecosystem processes, Trends Ecol. Evol., 29, 692–699, https://doi.org/10.1016/j.tree.2014.10.006, 2014.
Batjes, N. H.: Total carbon and nitrogen in the soils of the world, Eur. J. Soil Sci., 47, 151–163, https://doi.org/10.1111/j.1365-2389.1996.tb01386.x, 1996.
Batjes, N. H., Ribeiro, E., and van Oostrum, A.: Standardised soil profile data to support global mapping and modelling (WoSIS snapshot 2019), Earth Syst. Sci. Data, 12, 299–320, https://doi.org/10.5194/essd-12-299-2020, 2020.
Bishop, T. F. A., McBratney, A. B., and Laslett, G. M.: Modelling soil attribute depth functions with equal-area quadratic smoothing splines, Geoderma, 91, 27–45, https://doi.org/10.1016/S0016-7061(99)00003-8, 1999.
Braakhekke, M. C., Beer, C., Hoosbeek, M. R., Reichstein, M., Kruijt, B., Schrumpf, M., and Kabat, P.: SOMPROF: A vertically explicit soil organic matter model, Ecol. Model., 222, 1712–1730, https://doi.org/10.1016/j.ecolmodel.2011.02.015, 2011.
Bradford, M. A.: A leaky sink, Nat. Clim. Change, 7, 475–476, https://doi.org/10.1038/nclimate3332, 2017.
Brady, N. C. and Weil, R. R.: The nature and properties of soils, in: 12th Edn., Prentice Hall, Upper Saddle River, NJ, 881 pp., ISBN 978-0-13-852444-9, 1999.
Breiman, L.: Random Forests, Mach. Learn., 45, 5–32, https://doi.org/10.1023/a:1010933404324, 2001.
Carvalhais, N., Forkel, M., Khomik, M., Bellarby, J., Jung, M., Migliavacca, M., Mu, M., Saatchi, S., Santoro, M., Thurner, M., Weber, U., Ahrens, B., Beer, C., Cescatti, A., Randerson, J. T., and Reichstein, M.: Global covariation of carbon turnover times with climate in terrestrial ecosystems, Nature, 514, 213–217, https://doi.org/10.1038/nature13731, 2014.
Chapin, F. S., Matson, P. A., and Vitousek, P. M.: Principles of Terrestrial Ecosystem Ecology, Springer New York, New York, NY, https://doi.org/10.1007/978-1-4419-9504-9, 2011.
Chen, J., Luo, Y., and Sinsabaugh, R. L.: Subsoil carbon loss, Nat. Geosci., 16, 284–285, https://doi.org/10.1038/s41561-023-01164-9, 2023.
Chen, L., Fang, K., Wei, B., Qin, S., Feng, X., Hu, T., Ji, C., and Yang, Y.: Soil carbon persistence governed by plant input and mineral protection at regional and global scales, Ecol. Lett., 24, 1018–1028, https://doi.org/10.1111/ele.13723, 2021.
Chen, S., Huang, Y., Zou, J., and Shi, Y.: Mean residence time of global topsoil organic carbon depends on temperature, precipitation and soil nitrogen, Global Planet. Change, 100, 99–108, https://doi.org/10.1016/j.gloplacha.2012.10.006, 2013.
Cleveland, C. C. and Townsend, A. R.: Nutrient additions to a tropical rain forest drive substantial soil carbon dioxide losses to the atmosphere, P. Natl. Acad. Sci. USA, 103, 10316–10321, https://doi.org/10.1073/pnas.0600989103, 2006.
Cotrufo, M. F., Ranalli, M. G., Haddix, M. L., Six, J., and Lugato, E.: Soil carbon storage informed by particulate and mineral-associated organic matter, Nat. Geosci., 12, 989–994, https://doi.org/10.1038/s41561-019-0484-6, 2019.
Crowther, T. W., Todd-Brown, K. E. O., Rowe, C. W., Wieder, W. R., Carey, J. C., Machmuller, M. B., Snoek, B. L., Fang, S., Zhou, G., Allison, S. D., Blair, J. M., Bridgham, S. D., Burton, A. J., Carrillo, Y., Reich, P. B., Clark, J. S., Classen, A. T., Dijkstra, F. A., Elberling, B., Emmett, B. A., Estiarte, M., Frey, S. D., Guo, J., Harte, J., Jiang, L., Johnson, B. R., Kröel-Dulay, G., Larsen, K. S., Laudon, H., Lavallee, J. M., Luo, Y., Lupascu, M., Ma, L. N., Marhan, S., Michelsen, A., Mohan, J., Niu, S., Pendall, E., Peñuelas, J., Pfeifer-Meister, L., Poll, C., Reinsch, S., Reynolds, L. L., Schmidt, I. K., Sistla, S., Sokol, N. W., Templer, P. H., Treseder, K. K., Welker, J. M., and Bradford, M. A.: Quantifying global soil carbon losses in response to warming, Nature, 540, 104–108, https://doi.org/10.1038/nature20150, 2016.
Crowther, T. W., Riggs, C., Lind, E. M., Borer, E. T., Seabloom, E. W., Hobbie, S. E., Wubs, J., Adler, P. B., Firn, J., Gherardi, L., Hagenah, N., Hofmockel, K. S., Knops, J. M. H., McCulley, R. L., MacDougall, A. S., Peri, P. L., Prober, S. M., Stevens, C. J., and Routh, D.: Sensitivity of global soil carbon stocks to combined nutrient enrichment, Ecol. Lett., 22, 936–945, https://doi.org/10.1111/ele.13258, 2019a.
Crowther, T. W., van den Hoogen, J., Wan, J., Mayes, M. A., Keiser, A. D., Mo, L., Averill, C., and Maynard, D. S.: The global soil community and its influence on biogeochemistry, Science, 365, eaav0550, https://doi.org/10.1126/science.aav0550, 2019b.
Davidson, E. A. and Janssens, I. A.: Temperature sensitivity of soil carbon decomposition and feedbacks to climate change, Nature, 440, 165–173, https://doi.org/10.1038/nature04514, 2006.
Dinerstein, E., Olson, D., Joshi, A., Vynne, C., Burgess, N. D., Wikramanayake, E., Hahn, N., Palminteri, S., Hedao, P., Noss, R., Hansen, M., Locke, H., Ellis, E. C., Jones, B., Barber, C. V., Hayes, R., Kormos, C., Martin, V., Crist, E., Sechrest, W., Price, L., Baillie, J. E. M., Weeden, D., Suckling, K., Davis, C., Sizer, N., Moore, R., Thau, D., Birch, T., Potapov, P., Turubanova, S., Tyukavina, A., de Souza, N., Pintea, L., Brito, J. C., Llewellyn, O. A., Miller, A. G., Patzelt, A., Ghazanfar, S. A., Timberlake, J., Klöser, H., Shennan-Farpón, Y., Kindt, R., Lillesø, J.-P. B., van Breugel, P., Graudal, L., Voge, M., Al-Shammari, K. F., and Saleem, M.: An Ecoregion-Based Approach to Protecting Half the Terrestrial Realm, BioScience, 67, 534–545, https://doi.org/10.1093/biosci/bix014, 2017.
Doetterl, S., Stevens, A., Six, J., Merckx, R., Van Oost, K., Casanova Pinto, M., Casanova-Katny, A., Muñoz, C., Boudin, M., Zagal Venegas, E., and Boeckx, P.: Soil carbon storage controlled by interactions between geochemistry and climate, Nat. Geosci., 8, 780–783, https://doi.org/10.1038/ngeo2516, 2015.
Doetterl, S., Berhe, A. A., Nadeu, E., Wang, Z., Sommer, M., and Fiener, P.: Erosion, deposition and soil carbon: A review of process-level controls, experimental tools and models to address C cycling in dynamic landscapes, Earth-Sci. Rev., 154, 102–122, https://doi.org/10.1016/j.earscirev.2015.12.005, 2016.
Don, A., Böhme, I. H., Dohrmann, A. B., Poeplau, C., and Tebbe, C. C.: Microbial community composition affects soil organic carbon turnover in mineral soils, Biol. Fert. Soils, 53, 445–456, https://doi.org/10.1007/s00374-017-1198-9, 2017.
Fan, N., Koirala, S., Reichstein, M., Thurner, M., Avitabile, V., Santoro, M., Ahrens, B., Weber, U., and Carvalhais, N.: Apparent ecosystem carbon turnover time: uncertainties and robust features, Earth Syst. Sci. Data, 12, 2517–2536, https://doi.org/10.5194/essd-12-2517-2020, 2020.
Fan, N., Reichstein, M., Koirala, S., Ahrens, B., Mahecha, M. D., and Carvalhais, N.: Global apparent temperature sensitivity of terrestrial carbon turnover modulated by hydrometeorological factors, Nat. Geosci., 15, 989–994, https://doi.org/10.1038/s41561-022-01074-2, 2022.
FAO–UNESCO: Soil map of the world, FAO, Rome, Italy, https://www.fao.org/soils-portal/data-hub/soil-maps-and-databases (last access: 1 January 2025), 1990.
Field, C. B., Barros, V. R., and Intergovernmental Panel on Climate Change (Eds.): Climate change 2014: impacts, adaptation, and vulnerability: Working Group II contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change, Cambridge University Press, New York, NY, p. 1, ISBN 978-1-107-64165-5, 2014.
Friend, A. D., Lucht, W., Rademacher, T. T., Keribin, R., Betts, R., Cadule, P., Ciais, P., Clark, D. B., Dankers, R., Falloon, P. D., Ito, A., Kahana, R., Kleidon, A., Lomas, M. R., Nishina, K., Ostberg, S., Pavlick, R., Peylin, P., Schaphoff, S., Vuichard, N., Warszawski, L., Wiltshire, A., and Woodward, F. I.: Carbon residence time dominates uncertainty in terrestrial vegetation responses to future climate and atmospheric CO2, P. Natl. Acad. Sci. USA, 111, 3280–3285, https://doi.org/10.1073/pnas.1222477110, 2014.
Frostegård, Å., Vick, S. H. W., Lim, N. Y. N., Bakken, L. R., and Shapleigh, J. P.: Linking meta-omics to the kinetics of denitrification intermediates reveals pH-dependent causes of N2O emissions and nitrite accumulation in soil, ISME J., 16, 26–37, https://doi.org/10.1038/s41396-021-01045-2, 2022.
Gale, M. R. and Grigal, D. F.: Vertical root distributions of northern tree species in relation to successional status, Can. J. Forest Res., 17, 829–834, https://doi.org/10.1139/x87-131, 1987.
Giardina, C. P. and Ryan, M. G.: Evidence that decomposition rates of organic carbon in mineral soil do not vary with temperature, Nature, 404, 858–861, https://doi.org/10.1038/35009076, 2000.
Gillabel, J., Cebrian-Lopez, B., Six, J., and Merckx, R.: Experimental evidence for the attenuating effect of SOM protection on temperature sensitivity of SOM decomposition, Glob. Change Biol., 16, 2789–2798, https://doi.org/10.1111/j.1365-2486.2009.02132.x, 2010.
Goovaerts, P.: Geostatistical modelling of uncertainty in soil science, Geoderma, 103, 3–26, https://doi.org/10.1016/S0016-7061(01)00067-2, 2001.
Gower, S. T., Kucharik, C. J., and Norman, J. M.: Direct and Indirect Estimation of Leaf Area Index, fAPAR, and Net Primary Production of Terrestrial Ecosystems, Remote Sens. Environ., 70, 29–51, https://doi.org/10.1016/S0034-4257(99)00056-5, 1999.
Guerrero-Ramírez, N. R., Mommer, L., Freschet, G. T., Iversen, C. M., McCormack, M. L., Kattge, J., Poorter, H., van der Plas, F., Bergmann, J., Kuyper, T. W., York, L. M., Bruelheide, H., Laughlin, D. C., Meier, I. C., Roumet, C., Semchenko, M., Sweeney, C. J., van Ruijven, J., Valverde-Barrantes, O. J., Aubin, I., Catford, J. A., Manning, P., Martin, A., Milla, R., Minden, V., Pausas, J. G., Smith, S. W., Soudzilovskaia, N. A., Ammer, C., Butterfield, B., Craine, J., Cornelissen, J. H. C., de Vries, F. T., Isaac, M. E., Kramer, K., König, C., Lamb, E. G., Onipchenko, V. G., Peñuelas, J., Reich, P. B., Rillig, M. C., Sack, L., Shipley, B., Tedersoo, L., Valladares, F., van Bodegom, P., Weigelt, P., Wright, J. P., and Weigelt, A.: Global root traits (GRooT) database, Global Ecol. Biogeogr., 30, 25–37, https://doi.org/10.1111/geb.13179, 2021.
Guo, M., Yang, L., Zhang, L., Shen, F., Meadows, M. E., and Zhou, C.: Hydrology, vegetation, and soil properties as key drivers of soil organic carbon in coastal wetlands: A high-resolution study, Environmental Science and Ecotechnology, 23, 100482, https://doi.org/10.1016/j.ese.2024.100482, 2025.
Han, D., Hu, Z., Wang, X., Wang, T., Chen, A., Weng, Q., Liang, M., Zeng, X., Cao, R., Di, K., Luo, D., Zhang, G., Yang, Y., He, H., Fan, J., and Yu, G.: Shift in controlling factors of carbon stocks across biomes on the Qinghai-Tibetan Plateau, Environ. Res. Lett., 17, 074016, https://doi.org/10.1088/1748-9326/ac78f5, 2022.
He, Y., Trumbore, S. E., Torn, M. S., Harden, J. W., Vaughn, L. J. S., Allison, S. D., and Randerson, J. T.: Radiocarbon constraints imply reduced carbon uptake by soils during the 21st century, Science, 353, 1419–1424, https://doi.org/10.1126/science.aad4273, 2016.
Hengl, T., Nussbaum, M., Wright, M. N., Heuvelink, G. B. M., and Gräler, B.: Random forest as a generic framework for predictive modeling of spatial and spatio-temporal variables, PeerJ, 6, e5518, https://doi.org/10.7717/peerj.5518, 2018.
Heuvelink, G. B. M.: Error Propagation in Environmental Modelling with GIS, CRC Press, London, 150 pp., https://doi.org/10.4324/9780203016114, 1998.
Hicks Pries, C., Ryals, R., Zhu, B., Min, K., Cooper, A., Goldsmith, S., Pett-Ridge, J., Torn, M., and Asefaw Berhe, A.: The Deep Soil Organic Carbon Response to Global Change, Annu. Rev. Ecol. Evol. S., 54, 375–401, https://doi.org/10.1146/annurev-ecolsys-102320-085332, 2023.
Hicks Pries, C. E., Castanha, C., Porras, R. C., and Torn, M. S.: The whole-soil carbon flux in response to warming, Science, 355, 1420–1423, https://doi.org/10.1126/science.aal1319, 2017.
Holland, E. A., Post, W. M., Matthews, E. G., Sulzman, J. M., Staufer, R., and Krankina, O. N.: A Global Database of Litterfall Mass and Litter Pool Carbon and Nutrients, 2.150694 MB, https://doi.org/10.3334/ORNLDAAC/1244, 2015.
Hugelius, G., Tarnocai, C., Broll, G., Canadell, J. G., Kuhry, P., and Swanson, D. K.: The Northern Circumpolar Soil Carbon Database: spatially distributed datasets of soil coverage and soil carbon storage in the northern permafrost regions, Earth Syst. Sci. Data, 5, 3–13, https://doi.org/10.5194/essd-5-3-2013, 2013.
Jackson, R. B., Canadell, J., Ehleringer, J. R., Mooney, H. A., Sala, O. E., and Schulze, E. D.: A global analysis of root distributions for terrestrial biomes, Oecologia, 108, 389–411, https://doi.org/10.1007/BF00333714, 1996.
Jia, B., Zhou, G., and Xu, Z.: Forest litterfall and its composition: a new data set of observational data from China, Ecology, 97, 1365–1365, https://doi.org/10.1890/15-1604.1, 2016.
Jia, J., Cao, Z., Liu, C., Zhang, Z., Lin, L., Wang, Y., Haghipour, N., Wacker, L., Bao, H., Dittmar, T., Simpson, M. J., Yang, H., Crowther, T. W., Eglinton, T. I., He, J.-S., and Feng, X.: Climate warming alters subsoil but not topsoil carbon dynamics in alpine grassland, Glob. Change Biol., 25, 4383–4393, https://doi.org/10.1111/gcb.14823, 2019.
Jorgenson, M. T., Romanovsky, V., Harden, J., Shur, Y., O'Donnell, J., Schuur, E. A. G., Kanevskiy, M., and Marchenko, S.: Resilience and vulnerability of permafrost to climate change, Can. J. Forest Res., 40, 1219–1236, https://doi.org/10.1139/X10-060, 2010.
Koven, C. D., Riley, W. J., Subin, Z. M., Tang, J. Y., Torn, M. S., Collins, W. D., Bonan, G. B., Lawrence, D. M., and Swenson, S. C.: The effect of vertically resolved soil biogeochemistry and alternate soil C and N models on C dynamics of CLM4, Biogeosciences, 10, 7109–7131, https://doi.org/10.5194/bg-10-7109-2013, 2013.
Krull, E. S., Baldock, J. A., and Skjemstad, J. O.: Importance of mechanisms and processes of the stabilisation of soil organic matter for modelling carbon turnover, Funct. Plant Biol., 30, 207–222, https://doi.org/10.1071/fp02085, 2003.
Li, D., Niu, S., and Luo, Y.: Global patterns of the dynamics of soil carbon and nitrogen stocks following afforestation: a meta-analysis, New Phytol., 195, 172–181, https://doi.org/10.1111/j.1469-8137.2012.04150.x, 2012.
Liljedahl, A. K., Boike, J., Daanen, R. P., Fedorov, A. N., Frost, G. V., Grosse, G., Hinzman, L. D., Iijma, Y., Jorgenson, J. C., Matveyeva, N., Necsoiu, M., Raynolds, M. K., Romanovsky, V. E., Schulla, J., Tape, K. D., Walker, D. A., Wilson, C. J., Yabuki, H., and Zona, D.: Pan-Arctic ice-wedge degradation in warming permafrost and its influence on tundra hydrology, Nat. Geosci., 9, 312–318, https://doi.org/10.1038/ngeo2674, 2016.
Liu, F., Wu, H., Zhao, Y., Li, D., Yang, J.-L., Song, X., Shi, Z., Zhu, A.-X., and Zhang, G.-L.: Mapping high resolution National Soil Information Grids of China, Sci. Bull., 67, 328–340, https://doi.org/10.1016/j.scib.2021.10.013, 2022.
Luo, Z., Feng, W., Luo, Y., Baldock, J., and Wang, E.: Soil organic carbon dynamics jointly controlled by climate, carbon inputs, soil properties and soil carbon fractions, Glob. Change Biol., 23, 4430–4439, https://doi.org/10.1111/gcb.13767, 2017.
Luo, Z., Wang, G., and Wang, E.: Global subsoil organic carbon turnover times dominantly controlled by soil properties rather than climate, Nat. Commun., 10, 3688, https://doi.org/10.1038/s41467-019-11597-9, 2019.
Lützow, M. v., Kögel-Knabner, I., Ekschmitt, K., Matzner, E., Guggenberger, G., Marschner, B., and Flessa, H.: Stabilization of organic matter in temperate soils: mechanisms and their relevance under different soil conditions – a review, Eur. J. Soil Sci., 57, 426–445, https://doi.org/10.1111/j.1365-2389.2006.00809.x, 2006.
Ma, H., Mo, L., Crowther, T. W., Maynard, D. S., van den Hoogen, J., Stocker, B. D., Terrer, C., and Zohner, C. M.: The global distribution and environmental drivers of aboveground versus belowground plant biomass, Nat. Ecol. Evol., 5, 1110–1122, https://doi.org/10.1038/s41559-021-01485-1, 2021.
Malone, B. P., McBratney, A. B., Minasny, B., and Laslett, G. M.: Mapping continuous depth functions of soil carbon storage and available water capacity, Geoderma, 154, 138–152, https://doi.org/10.1016/j.geoderma.2009.10.007, 2009.
Malone, B. P., McBratney, A. B., and Minasny, B.: Empirical estimates of uncertainty for mapping continuous depth functions of soil attributes, Geoderma, 160, 614–626, https://doi.org/10.1016/j.geoderma.2010.11.013, 2011.
Mathieu, J. A., Hatté, C., Balesdent, J., and Parent, É.: Deep soil carbon dynamics are driven more by soil type than by climate: a worldwide meta-analysis of radiocarbon profiles, Glob. Change Biol., 21, 4278–4292, https://doi.org/10.1111/gcb.13012, 2015.
Meinshausen, N.: Quantile Regression Forests, J. Mach. Learn. Res., 7, 983–999, 2006.
Mishra, U., Hugelius, G., Shelef, E., Yang, Y., Strauss, J., Lupachev, A., Harden, J. W., Jastrow, J. D., Ping, C.-L., Riley, W. J., Schuur, E. A. G., Matamala, R., Siewert, M., Nave, L. E., Koven, C. D., Fuchs, M., Palmtag, J., Kuhry, P., Treat, C. C., Zubrzycki, S., Hoffman, F. M., Elberling, B., Camill, P., Veremeeva, A., and Orr, A.: Spatial heterogeneity and environmental predictors of permafrost region soil organic carbon stocks, Sci. Adv., 7, eaaz5236, https://doi.org/10.1126/sciadv.aaz5236, 2021.
Oleson, K., Lawrence, D., Bonan, G., Drewniak, B., Huang, M., Koven, C., Levis, S., Li, F., Riley, W., Subin, Z., Swenson, S., Thornton, P., Bozbiyik, A., Fisher, R., Heald, C., Kluzek, E., Lamarque, J.-F., Lawrence, P., Leung, L., Lipscomb, W., Muszala, S., Ricciuto, D., Sacks, W., Sun, Y., Tang, J., and Yang, Z.-L.: Technical description of version 4.5 of the Community Land Model (CLM), NCAR, https://doi.org/10.5065/D6RR1W7M, 2013.
Poggio, L., de Sousa, L. M., Batjes, N. H., Heuvelink, G. B. M., Kempen, B., Ribeiro, E., and Rossiter, D.: SoilGrids 2.0: producing soil information for the globe with quantified spatial uncertainty, SOIL, 7, 217–240, https://doi.org/10.5194/soil-7-217-2021, 2021.
Porras, R. C., Hicks Pries, C. E., McFarlane, K. J., Hanson, P. J., and Torn, M. S.: Association with pedogenic iron and aluminum: effects on soil organic carbon storage and stability in four temperate forest soils, Biogeochemistry, 133, 333–345, https://doi.org/10.1007/s10533-017-0337-6, 2017.
Qin, S., Chen, L., Fang, K., Zhang, Q., Wang, J., Liu, F., Yu, J., and Yang, Y.: Temperature sensitivity of SOM decomposition governed by aggregate protection and microbial communities, Sci. Adv., 5, eaau1218, https://doi.org/10.1126/sciadv.aau1218, 2019.
Reichstein, M., Baldocchi, D., Running, S., Tenhunen, J., Valentini, R., Rambal, S., Ourcival, J., Granier, A., Bouriaud, O., Bernhofer, C., and Gruenwald, T.: Validation Effort of MODIS LAI/GPP/NPP Products at FLUXNET Sites, in: AGU Fall Meeting, https://ui.adsabs.harvard.edu/abs/2002AGUFM.B61B0728R (last access: 1 January 2025), 2002.
Rousk, J., Bååth, E., Brookes, P. C., Lauber, C. L., Lozupone, C., Caporaso, J. G., Knight, R., and Fierer, N.: Soil bacterial and fungal communities across a pH gradient in an arable soil, ISME J., 4, 1340–1351, https://doi.org/10.1038/ismej.2010.58, 2010.
Rumpel, C. and Kögel-Knabner, I.: Deep soil organic matter – a key but poorly understood component of terrestrial C cycle, Plant Soil, 338, 143–158, https://doi.org/10.1007/s11104-010-0391-5, 2011.
Running, S. and Zhao, M.: MOD17A3HGF MODIS/Terra Net Primary Production Gap-Filled Yearly L4 Global 500 m SIN Grid V006, https://doi.org/10.5067/MODIS/MOD17A3HGF.006, 2019.
Schenk, H. J. and Jackson, R. B.: The Global Biogeography of Roots, Ecol. Monogr., 72, 311–328, https://doi.org/10.1890/0012-9615(2002)072[0311:TGBOR]2.0.CO;2, 2002.
Schenk, H. J. and Jackson, R. B.: Mapping the global distribution of deep roots in relation to climate and soil characteristics, Geoderma, 126, 129–140, https://doi.org/10.1016/j.geoderma.2004.11.018, 2005.
Schimel, D. S.: Terrestrial ecosystems and the carbon cycle, Glob. Change Biol., 1, 77–91, https://doi.org/10.1111/j.1365-2486.1995.tb00008.x, 1995.
Schimel, D. S., Braswell, B. H., Holland, E. A., McKeown, R., Ojima, D. S., Painter, T. H., Parton, W. J., and Townsend, A. R.: Climatic, edaphic, and biotic controls over storage and turnover of carbon in soils, Global Biogeochem. Cy., 8, 279–293, https://doi.org/10.1029/94GB00993, 1994.
Schmidinger, J. and Heuvelink, G. B. M.: Validation of uncertainty predictions in digital soil mapping, Geoderma, 437, 116585, https://doi.org/10.1016/j.geoderma.2023.116585, 2023.
Schmidt, M. W. I., Torn, M. S., Abiven, S., Dittmar, T., Guggenberger, G., Janssens, I. A., Kleber, M., Kögel-Knabner, I., Lehmann, J., Manning, D. A. C., Nannipieri, P., Rasse, D. P., Weiner, S., and Trumbore, S. E.: Persistence of soil organic matter as an ecosystem property, Nature, 478, 49–56, https://doi.org/10.1038/nature10386, 2011.
Schuur, E. A. G., Bockheim, J., Canadell, J. G., Euskirchen, E., Field, C. B., Goryachkin, S. V., Hagemann, S., Kuhry, P., Lafleur, P. M., Lee, H., Mazhitova, G., Nelson, F. E., Rinke, A., Romanovsky, V. E., Shiklomanov, N., Tarnocai, C., Venevsky, S., Vogel, J. G., and Zimov, S. A.: Vulnerability of Permafrost Carbon to Climate Change: Implications for the Global Carbon Cycle, BioScience, 58, 701–714, https://doi.org/10.1641/B580807, 2008.
Shi, Y., Tang, X., Yu, P., Xu, L., Chen, G., Cao, L., Song, C., Cai, C., and Li, J.: Subsoil organic carbon turnover is dominantly controlled by soil properties in grasslands across China, CATENA, 207, 105654, https://doi.org/10.1016/j.catena.2021.105654, 2021.
Shi, Z., Allison, S. D., He, Y., Levine, P. A., Hoyt, A. M., Beem-Miller, J., Zhu, Q., Wieder, W. R., Trumbore, S., and Randerson, J. T.: The age distribution of global soil carbon inferred from radiocarbon measurements, Nat. Geosci., 13, 555–559, https://doi.org/10.1038/s41561-020-0596-z, 2020.
Sierra, C. A., Müller, M., Metzler, H., Manzoni, S., and Trumbore, S. E.: The muddle of ages, turnover, transit, and residence times in the carbon cycle, Glob. Change Biol., 23, 1763–1773, https://doi.org/10.1111/gcb.13556, 2017.
Sierra, C. A., Ahrens, B., Bolinder, M. A., Braakhekke, M. C., von Fromm, S., Kätterer, T., Luo, Z., Parvin, N., and Wang, G.: Carbon sequestration in the subsoil and the time required to stabilize carbon for climate change mitigation, Glob. Change Biol., 30, e17153, https://doi.org/10.1111/gcb.17153, 2024.
Six, J. and Jastrow, J.: Organic Matter Turnover, in: Encyclopedia of Soil Science, Marcel Dekker, New York, 936–942, ISBN 9780429110757, 2002.
Slessarev, E. W., Lin, Y., Bingham, N. L., Johnson, J. E., Dai, Y., Schimel, J. P., and Chadwick, O. A.: Water balance creates a threshold in soil pH at the global scale, Nature, 540, 567–569, https://doi.org/10.1038/nature20139, 2016.
Slot, M. and Winter, K.: The Effects of Rising Temperature on the Ecophysiology of Tropical Forest Trees, in: Tropical Tree Physiology: Adaptations and Responses in a Changing Environment, edited by: Goldstein, G. and Santiago, L. S., Springer International Publishing, Cham, 385–412, https://doi.org/10.1007/978-3-319-27422-5_18, 2016.
Smith, P., Poch, R. M., Lobb, D. A., Bhattacharyya, R., Alloush, G., Eudoxie, G. D., Anjos, L. H. C., Castellano, M., Ndzana, G. M., Chenu, C., Naidu, R., Vijayanathan, J., Muscolo, A. M., Studdert, G. A., Eugenio, N. R., Calzolari, M. C., Amuri, N., and Hallett, P.: Status of the World's Soils, Annu. Rev. Env. Resour., 49, 73–104, https://doi.org/10.1146/annurev-environ-030323-075629, 2024.
Soong, J. L., Castanha, C., Hicks Pries, C. E., Ofiti, N., Porras, R. C., Riley, W. J., Schmidt, M. W. I., and Torn, M. S.: Five years of whole-soil warming led to loss of subsoil carbon stocks and increased CO2 efflux, Sci. Adv., 7, eabd1343, https://doi.org/10.1126/sciadv.abd1343, 2021.
Spawn, S. A., Sullivan, C. C., Lark, T. J., and Gibbs, H. K.: Harmonized global maps of above and belowground biomass carbon density in the year 2010, Sci. Data, 7, 112, https://doi.org/10.1038/s41597-020-0444-4, 2020.
Todd-Brown, K. E. O., Randerson, J. T., Post, W. M., Hoffman, F. M., Tarnocai, C., Schuur, E. A. G., and Allison, S. D.: Causes of variation in soil carbon simulations from CMIP5 Earth system models and comparison with observations, Biogeosciences, 10, 1717–1736, https://doi.org/10.5194/bg-10-1717-2013, 2013.
Torn, M. S., Trumbore, S. E., Chadwick, O. A., Vitousek, P. M., and Hendricks, D. M.: Mineral control of soil organic carbon storage and turnover, Nature, 389, 170–173, https://doi.org/10.1038/38260, 1997.
Torn, M. S., Swanston, C. W., Castanha, C., and Trumbore, S. E.: Storage and Turnover of Organic Matter in Soil, in: Biophysico-Chemical Processes Involving Natural Nonliving Organic Matter in Environmental Systems, John Wiley & Sons, Ltd, 219–272, https://doi.org/10.1002/9780470494950.ch6, 2009.
Tumber-Dávila, S. J., Schenk, H. J., Du, E., and Jackson, R. B.: Plant sizes and shapes above and belowground and their interactions with climate, New Phytol., 235, 1032–1056, https://doi.org/10.1111/nph.18031, 2022.
van den Hoogen, J., Geisen, S., Routh, D., Ferris, H., Traunspurger, W., Wardle, D. A., de Goede, R. G. M., Adams, B. J., Ahmad, W., Andriuzzi, W. S., Bardgett, R. D., Bonkowski, M., Campos-Herrera, R., Cares, J. E., Caruso, T., de Brito Caixeta, L., Chen, X., Costa, S. R., Creamer, R., Mauro da Cunha Castro, J., Dam, M., Djigal, D., Escuer, M., Griffiths, B. S., Gutiérrez, C., Hohberg, K., Kalinkina, D., Kardol, P., Kergunteuil, A., Korthals, G., Krashevska, V., Kudrin, A. A., Li, Q., Liang, W., Magilton, M., Marais, M., Martín, J. A. R., Matveeva, E., Mayad, E. H., Mulder, C., Mullin, P., Neilson, R., Nguyen, T. A. D., Nielsen, U. N., Okada, H., Rius, J. E. P., Pan, K., Peneva, V., Pellissier, L., Carlos Pereira da Silva, J., Pitteloud, C., Powers, T. O., Powers, K., Quist, C. W., Rasmann, S., Moreno, S. S., Scheu, S., Setälä, H., Sushchuk, A., Tiunov, A. V., Trap, J., van der Putten, W., Vestergård, M., Villenave, C., Waeyenberge, L., Wall, D. H., Wilschut, R., Wright, D. G., Yang, J., and Crowther, T. W.: Soil nematode abundance and functional group composition at a global scale, Nature, 572, 194–198, https://doi.org/10.1038/s41586-019-1418-6, 2019.
van Groenigen, K. J., Osenberg, C. W., Terrer, C., Carrillo, Y., Dijkstra, F. A., Heath, J., Nie, M., Pendall, E., Phillips, R. P., and Hungate, B. A.: Faster turnover of new soil carbon inputs under increased atmospheric CO2, Glob. Change Biol., 23, 4420–4429, https://doi.org/10.1111/gcb.13752, 2017.
Varney, R. M., Chadburn, S. E., Burke, E. J., and Cox, P. M.: Evaluation of soil carbon simulation in CMIP6 Earth system models, Biogeosciences, 19, 4671–4704, https://doi.org/10.5194/bg-19-4671-2022, 2022.
Viscarra Rossel, R. A., Lee, J., Behrens, T., Luo, Z., Baldock, J., and Richards, A.: Continental-scale soil carbon composition and vulnerability modulated by regional environmental controls, Nat. Geosci., 12, 547–552, https://doi.org/10.1038/s41561-019-0373-z, 2019.
Wadoux, A. M. J. C.: Using deep learning for multivariate mapping of soil with quantified uncertainty, Geoderma, 351, 59–70, https://doi.org/10.1016/j.geoderma.2019.05.012, 2019.
Wang, J., Sun, J., Xia, J., He, N., Li, M., and Niu, S.: Soil and vegetation carbon turnover times from tropical to boreal forests, Funct. Ecol., 32, 71–82, https://doi.org/10.1111/1365-2435.12914, 2018.
Wright, M. N. and Ziegler, A.: ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R, J. Stat. Softw., 77, 1–17, https://doi.org/10.18637/jss.v077.i01, 2017.
Wu, D., Liu, D., Wang, T., Ding, J., He, Y., Ciais, P., Zhang, G., and Piao, S.: Carbon turnover times shape topsoil carbon difference between Tibetan Plateau and Arctic tundra, Sci. Bull., 66, 1698–1704, https://doi.org/10.1016/j.scib.2021.04.019, 2021.
Xiang, D., Wang, G., Tian, J., and Li, W.: Global patterns and edaphic-climatic controls of soil carbon decomposition kinetics predicted from incubation experiments, Nat. Commun., 14, 2171, https://doi.org/10.1038/s41467-023-37900-3, 2023.
Xiao, L., Wang, G., Wang, M., Zhang, S., Sierra, C. A., Guo, X., Chang, J., Shi, Z., and Luo, Z.: Younger carbon dominates global soil carbon efflux, Glob. Change Biol., 28, 5587–5599, https://doi.org/10.1111/gcb.16311, 2022.
Xiao, L., Wang, G., Chang, J., Chen, Y., Guo, X., Mao, X., Wang, M., Zhang, S., Shi, Z., Luo, Y., Cheng, L., Yu, K., Mo, F., and Luo, Z.: Global depth distribution of belowground net primary productivity and its drivers, Global Ecol. Biogeogr., 32, 1435–1451, https://doi.org/10.1111/geb.13705, 2023.
Yang, S., Jansen, B., Absalah, S., Kalbitz, K., Chunga Castro, F. O., and Cammeraat, E. L. H.: Soil organic carbon content and mineralization controlled by the composition, origin and molecular diversity of organic matter: A study in tropical alpine grasslands, Soil Till. Res., 215, 105203, https://doi.org/10.1016/j.still.2021.105203, 2022.
Zeng, X.: Global Vegetation Root Distribution for Land Modeling, J. Hydrometeorol., 2, 525–530, https://doi.org/10.1175/1525-7541(2001)002<0525:GVRDFL>2.0.CO;2, 2001.
Zhang, G., Wan, Q., Zhang, F., Wu, K., Cai, C., Zhang, M., Li, D., Zhao, Y., and Yang, J.: Criteria for establishment of soil family and soil series in Chinese Soil Taxonomy, Acta Pedologica Sinica, 50, 826–834, https://doi.org/10.11766/trxb201303180124, 2013.
Zhang, L.: Global maps of top- and subsoil organic carbon turnover times, Zenodo [data set], https://doi.org/10.5281/zenodo.14560239, 2025a.
Zhang, L.: Code for paper “Mapping global distributions, environmental controls, and uncertainties of apparent topsoil and subsoil organic carbon turnover times”, Zenodo [code], https://doi.org/10.5281/zenodo.15636310, 2025b. (code available at: https://github.com/leizhang-geo/global_soil_carbon_turnover_time.git, last access: 11 June 2025)
Zhang, L., Yang, L., Ma, T., Shen, F., Cai, Y., and Zhou, C.: A self-training semi-supervised machine learning method for predictive mapping of soil classes with limited sample data, Geoderma, 384, 114809, https://doi.org/10.1016/j.geoderma.2020.114809, 2021.
Zhou, Z., Sun, O. J., Huang, J., Li, L., Liu, P., and Han, X.: Soil carbon and nitrogen stores and storage potential as affected by land-use in an agro-pastoral ecotone of northern China, Biogeochemistry, 82, 127–138, https://doi.org/10.1007/s10533-006-9058-y, 2007.
Zosso, C. U., Ofiti, N. O. E., Torn, M. S., Wiesenberg, G. L. B., and Schmidt, M. W. I.: Rapid loss of complex polymers and pyrogenic carbon in subsoils under whole-soil warming, Nat. Geosci., 16, 344–348, https://doi.org/10.1038/s41561-023-01142-1, 2023.
Short summary
Current understandings of depth-dependent variations and controls of soil organic carbon turnover time (τ) at global, biome, and local scales remain incomplete. We used the state-of-the-art soil and root profile databases and satellite observations to generate new spatially explicit global maps of topsoil and subsoil τ, with quantified uncertainties for better user applications. The new insights from the resulting maps will facilitate efforts to model the carbon cycle and will support effective carbon management.
Current understandings of depth-dependent variations and controls of soil organic carbon...
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