Articles | Volume 10, issue 2
https://doi.org/10.5194/soil-10-619-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/soil-10-619-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Original research article
| 
10 Sep 2024
Original research article |
| 10 Sep 2024
| 10 Sep 2024
An ensemble estimate of Australian soil organic carbon using machine learning and process-based modelling
Lingfei Wang
CORRESPONDING AUTHOR
ARC Centre of Excellence for Climate Extremes, Sydney, NSW 2052, Australia
Climate Change Research Centre, University of New South Wales, Sydney, NSW 2052, Australia
Gab Abramowitz
ARC Centre of Excellence for Climate Extremes, Sydney, NSW 2052, Australia
Climate Change Research Centre, University of New South Wales, Sydney, NSW 2052, Australia
Ying-Ping Wang
CSIRO Environment, Clayton South, Melbourne, VIC 3169, Australia
Andy Pitman
ARC Centre of Excellence for Climate Extremes, Sydney, NSW 2052, Australia
Climate Change Research Centre, University of New South Wales, Sydney, NSW 2052, Australia
Raphael A. Viscarra Rossel
Soil and Landscape Science, School of Molecular and Life Sciences, Faculty of Science and Engineering, Curtin University, Perth, WA 6845, Australia
Related authors
Lingfei Wang, Gab Abramowitz, Ying-Ping Wang, Andy Pitman, Philippe Ciais, and Daniel S. Goll
EGUsphere, https://doi.org/10.5194/egusphere-2025-2545, https://doi.org/10.5194/egusphere-2025-2545, 2025
This preprint is open for discussion and under review for Biogeosciences (BG).
Short summary
Short summary
Accurate estimates of global soil organic carbon (SOC) content and its spatial pattern are critical for future climate change mitigation. However, the most advanced mechanistic SOC models struggle to do this task. Here we apply multiple explainable machine learning methods to identify missing variables and misrepresented relationships between environmental factors and SOC in these models, offering new insights to guide model development for more reliable SOC predictions.
Georgina Falster, Gab Abramowitz, Sanaa Hobeichi, Cath Hughes, Pauline Treble, Nerilie J. Abram, Michael I. Bird, Alexandre Cauquoin, Bronwyn Dixon, Russell Drysdale, Chenhui Jin, Niels Munksgaard, Bernadette Proemse, Jonathan J. Tyler, Martin Werner, and Carol Tadros
EGUsphere, https://doi.org/10.5194/egusphere-2025-2458, https://doi.org/10.5194/egusphere-2025-2458, 2025
This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
Short summary
Short summary
We used a random forest approach to produce estimates of monthly precipitation stable isotope variability from 1962–2023, at high resolution across the entire Australian continent. Comprehensive skill and sensitivity testing shows that our random forest models skilfully predict precipitation isotope values in places and times that observations are not available. We make all outputs publicly available, facilitating use in fields from ecology and hydrology to archaeology and forensic science.
Lingfei Wang, Gab Abramowitz, Ying-Ping Wang, Andy Pitman, Philippe Ciais, and Daniel S. Goll
EGUsphere, https://doi.org/10.5194/egusphere-2025-2545, https://doi.org/10.5194/egusphere-2025-2545, 2025
This preprint is open for discussion and under review for Biogeosciences (BG).
Short summary
Short summary
Accurate estimates of global soil organic carbon (SOC) content and its spatial pattern are critical for future climate change mitigation. However, the most advanced mechanistic SOC models struggle to do this task. Here we apply multiple explainable machine learning methods to identify missing variables and misrepresented relationships between environmental factors and SOC in these models, offering new insights to guide model development for more reliable SOC predictions.
Matthew O. Grant, Anna M. Ukkola, Elisabeth Vogel, Sanaa Hobeichi, Andy J. Pitman, Alex Raymond Borowiak, and Keirnan Fowler
EGUsphere, https://doi.org/10.5194/egusphere-2024-4024, https://doi.org/10.5194/egusphere-2024-4024, 2025
Short summary
Short summary
Australia is regularly subjected to severe and widespread drought. By using multiple drought indicators, we show that while there have been widespread decreases in droughts since the beginning of the 20th century. However, many regions have seen an increase in droughts in more recent decades. Despite these changes, our analysis shows that they remain within the range of observed variability and are not unprecedented in the context of past droughts.
Yi Xi, Philippe Ciais, Dan Zhu, Chunjing Qiu, Yuan Zhang, Shushi Peng, Gustaf Hugelius, Simon P. K. Bowring, Daniel S. Goll, and Ying-Ping Wang
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-206, https://doi.org/10.5194/gmd-2024-206, 2025
Revised manuscript accepted for GMD
Short summary
Short summary
Including high-latitude deep carbon is critical for projecting future soil carbon emissions, yet it’s absent in most land surface models. Here we propose a new carbon accumulation protocol by integrating deep carbon from Yedoma deposits and representing the observed history of peat carbon formation in ORCHIDEE-MICT. Our results show an additional 157 PgC in present-day Yedoma deposits and a 1–5 m shallower peat depth, 43 % less passive soil carbon in peatlands against the convention protocol.
Yang Hu, Adam Cross, Zefang Shen, Johan Bouma, and Raphael A. Viscarra Rossel
EGUsphere, https://doi.org/10.5194/egusphere-2024-3939, https://doi.org/10.5194/egusphere-2024-3939, 2025
Short summary
Short summary
We reviewed the literature on soil health definition, indicators and assessment frameworks, highlighting sensing technologies' significant potential to improve current time-consuming and costly assessment methods. We proposed a soil health assessment framework from an ecological perspective free from human bias, that leverages proximal sensing, remote sensing, machine learning, and sensor data fusion to enable objective, rapid, cost-effective, scalable, and integrative assessments.
Gab Abramowitz, Anna Ukkola, Sanaa Hobeichi, Jon Cranko Page, Mathew Lipson, Martin G. De Kauwe, Samuel Green, Claire Brenner, Jonathan Frame, Grey Nearing, Martyn Clark, Martin Best, Peter Anthoni, Gabriele Arduini, Souhail Boussetta, Silvia Caldararu, Kyeungwoo Cho, Matthias Cuntz, David Fairbairn, Craig R. Ferguson, Hyungjun Kim, Yeonjoo Kim, Jürgen Knauer, David Lawrence, Xiangzhong Luo, Sergey Malyshev, Tomoko Nitta, Jerome Ogee, Keith Oleson, Catherine Ottlé, Phillipe Peylin, Patricia de Rosnay, Heather Rumbold, Bob Su, Nicolas Vuichard, Anthony P. Walker, Xiaoni Wang-Faivre, Yunfei Wang, and Yijian Zeng
Biogeosciences, 21, 5517–5538, https://doi.org/10.5194/bg-21-5517-2024, https://doi.org/10.5194/bg-21-5517-2024, 2024
Short summary
Short summary
This paper evaluates land models – computer-based models that simulate ecosystem dynamics; land carbon, water, and energy cycles; and the role of land in the climate system. It uses machine learning and AI approaches to show that, despite the complexity of land models, they do not perform nearly as well as they could given the amount of information they are provided with about the prediction problem.
Anjana Devanand, Jason Evans, Andy Pitman, Sujan Pal, David Gochis, and Kevin Sampson
EGUsphere, https://doi.org/10.5194/egusphere-2024-3148, https://doi.org/10.5194/egusphere-2024-3148, 2024
Short summary
Short summary
Including lateral flow increases evapotranspiration near major river channels in high-resolution land surface simulations in southeast Australia, consistent with observations. The 1-km resolution model shows a widespread pattern of dry ridges that does not exist at coarser resolutions. Our results have implications for improved simulations of droughts and future water availability.
Thorsten Behrens, Karsten Schmidt, Felix Stumpf, Simon Tutsch, Marie Hertzog, Urs Grob, Armin Keller, and Raphael Viscarra Rossel
EGUsphere, https://doi.org/10.5194/egusphere-2024-2810, https://doi.org/10.5194/egusphere-2024-2810, 2024
Preprint archived
Short summary
Short summary
We integrate various methods to create soil property maps for soil surveyors, which they can utilize as a reference before beginning their fieldwork. A new sampling design based on a geographical stratification is proposed focussing on local feature space variability. It allows for a systematic analysis of predictive accuracy for varying densities. The spectral and spatial models yielded high accuracies. Our study highlights the value of integrating pedometric technologies in soil surveys.
Mengjie Han, Qing Zhao, Xili Wang, Ying-Ping Wang, Philippe Ciais, Haicheng Zhang, Daniel S. Goll, Lei Zhu, Zhe Zhao, Zhixuan Guo, Chen Wang, Wei Zhuang, Fengchang Wu, and Wei Li
Geosci. Model Dev., 17, 4871–4890, https://doi.org/10.5194/gmd-17-4871-2024, https://doi.org/10.5194/gmd-17-4871-2024, 2024
Short summary
Short summary
The impact of biochar (BC) on soil organic carbon (SOC) dynamics is not represented in most land carbon models used for assessing land-based climate change mitigation. Our study develops a BC model that incorporates our current understanding of BC effects on SOC based on a soil carbon model (MIMICS). The BC model can reproduce the SOC changes after adding BC, providing a useful tool to couple dynamic land models to evaluate the effectiveness of BC application for CO2 removal from the atmosphere.
Lewis Walden, Farid Sepanta, and Raphael Viscarra Rossel
EGUsphere, https://doi.org/10.5194/egusphere-2023-2464, https://doi.org/10.5194/egusphere-2023-2464, 2023
Preprint archived
Short summary
Short summary
We characterised the chemical and mineral composition of soil organic carbon fractions with mid-infrared spectroscopy. We identified unique and shared features of the spectra of carbon fractions, and the interactions between their organic and mineral components. These interactions are key to the persistence of C in soils, and we propose that mid-infrared spectroscopy could help to infer stability of soil C.
Xianjin He, Laurent Augusto, Daniel S. Goll, Bruno Ringeval, Ying-Ping Wang, Julian Helfenstein, Yuanyuan Huang, and Enqing Hou
Biogeosciences, 20, 4147–4163, https://doi.org/10.5194/bg-20-4147-2023, https://doi.org/10.5194/bg-20-4147-2023, 2023
Short summary
Short summary
We identified total soil P concentration as the most important predictor of all soil P pool concentrations, except for primary mineral P concentration, which is primarily controlled by soil pH and only secondarily by total soil P concentration. We predicted soil P pools’ distributions in natural systems, which can inform assessments of the role of natural P availability for ecosystem productivity, climate change mitigation, and the functioning of the Earth system.
Lina Teckentrup, Martin G. De Kauwe, Gab Abramowitz, Andrew J. Pitman, Anna M. Ukkola, Sanaa Hobeichi, Bastien François, and Benjamin Smith
Earth Syst. Dynam., 14, 549–576, https://doi.org/10.5194/esd-14-549-2023, https://doi.org/10.5194/esd-14-549-2023, 2023
Short summary
Short summary
Studies analyzing the impact of the future climate on ecosystems employ climate projections simulated by global circulation models. These climate projections display biases that translate into significant uncertainty in projections of the future carbon cycle. Here, we test different methods to constrain the uncertainty in simulations of the carbon cycle over Australia. We find that all methods reduce the bias in the steady-state carbon variables but that temporal properties do not improve.
Zefang Shen, Haylee D'Agui, Lewis Walden, Mingxi Zhang, Tsoek Man Yiu, Kingsley Dixon, Paul Nevill, Adam Cross, Mohana Matangulu, Yang Hu, and Raphael A. Viscarra Rossel
SOIL, 8, 467–486, https://doi.org/10.5194/soil-8-467-2022, https://doi.org/10.5194/soil-8-467-2022, 2022
Short summary
Short summary
We compared miniaturised visible and near-infrared spectrometers to a portable visible–near-infrared instrument, which is more expensive. Statistical and machine learning algorithms were used to model 29 key soil health indicators. Accuracy of the miniaturised spectrometers was comparable to the portable system. Soil spectroscopy with these tiny sensors is cost-effective and could diagnose soil health, help monitor soil rehabilitation, and deliver positive environmental and economic outcomes.
Jon Cranko Page, Martin G. De Kauwe, Gab Abramowitz, Jamie Cleverly, Nina Hinko-Najera, Mark J. Hovenden, Yao Liu, Andy J. Pitman, and Kiona Ogle
Biogeosciences, 19, 1913–1932, https://doi.org/10.5194/bg-19-1913-2022, https://doi.org/10.5194/bg-19-1913-2022, 2022
Short summary
Short summary
Although vegetation responds to climate at a wide range of timescales, models of the land carbon sink often ignore responses that do not occur instantly. In this study, we explore the timescales at which Australian ecosystems respond to climate. We identified that carbon and water fluxes can be modelled more accurately if we include environmental drivers from up to a year in the past. The importance of antecedent conditions is related to ecosystem aridity but is also influenced by other factors.
Yuanyuan Yang, Zefang Shen, Andrew Bissett, and Raphael A. Viscarra Rossel
SOIL, 8, 223–235, https://doi.org/10.5194/soil-8-223-2022, https://doi.org/10.5194/soil-8-223-2022, 2022
Short summary
Short summary
We present a new method to estimate the relative abundance of the dominant phyla and diversity of fungi in Australian soil. It uses state-of-the-art machine learning with publicly available data on soil and environmental proxies for edaphic, climatic, biotic and topographic factors, and visible–near infrared wavelengths. The estimates could serve to supplement the more expensive molecular approaches towards a better understanding of soil fungal abundance and diversity in agronomy and ecology.
Anna M. Ukkola, Gab Abramowitz, and Martin G. De Kauwe
Earth Syst. Sci. Data, 14, 449–461, https://doi.org/10.5194/essd-14-449-2022, https://doi.org/10.5194/essd-14-449-2022, 2022
Short summary
Short summary
Flux towers provide measurements of water, energy, and carbon fluxes. Flux tower data are invaluable in improving and evaluating land models but are not suited to modelling applications as published. Here we present flux tower data tailored for land modelling, encompassing 170 sites globally. Our dataset resolves several key limitations hindering the use of flux tower data in land modelling, including incomplete forcing variable, data format, and low data quality.
Sami W. Rifai, Martin G. De Kauwe, Anna M. Ukkola, Lucas A. Cernusak, Patrick Meir, Belinda E. Medlyn, and Andy J. Pitman
Biogeosciences, 19, 491–515, https://doi.org/10.5194/bg-19-491-2022, https://doi.org/10.5194/bg-19-491-2022, 2022
Short summary
Short summary
Australia's woody ecosystems have experienced widespread greening despite a warming climate and repeated record-breaking droughts and heat waves. Increasing atmospheric CO2 increases plant water use efficiency, yet quantifying the CO2 effect is complicated due to co-occurring effects of global change. Here we harmonized a 38-year satellite record to separate the effects of climate change, land use change, and disturbance to quantify the CO2 fertilization effect on the greening phenomenon.
Xianjin He, Laurent Augusto, Daniel S. Goll, Bruno Ringeval, Yingping Wang, Julian Helfenstein, Yuanyuan Huang, Kailiang Yu, Zhiqiang Wang, Yongchuan Yang, and Enqing Hou
Earth Syst. Sci. Data, 13, 5831–5846, https://doi.org/10.5194/essd-13-5831-2021, https://doi.org/10.5194/essd-13-5831-2021, 2021
Short summary
Short summary
Our database of globally distributed natural soil total P (STP) concentration showed concentration ranged from 1.4 to 9630.0 (mean 570.0) mg kg−1. Global predictions of STP concentration increased with latitude. Global STP stocks (excluding Antarctica) were estimated to be 26.8 and 62.2 Pg in the topsoil and subsoil, respectively. Our global map of STP concentration can be used to constrain Earth system models representing the P cycle and to inform quantification of global soil P availability.
Lina Teckentrup, Martin G. De Kauwe, Andrew J. Pitman, Daniel S. Goll, Vanessa Haverd, Atul K. Jain, Emilie Joetzjer, Etsushi Kato, Sebastian Lienert, Danica Lombardozzi, Patrick C. McGuire, Joe R. Melton, Julia E. M. S. Nabel, Julia Pongratz, Stephen Sitch, Anthony P. Walker, and Sönke Zaehle
Biogeosciences, 18, 5639–5668, https://doi.org/10.5194/bg-18-5639-2021, https://doi.org/10.5194/bg-18-5639-2021, 2021
Short summary
Short summary
The Australian continent is included in global assessments of the carbon cycle such as the global carbon budget, yet the performance of dynamic global vegetation models (DGVMs) over Australia has rarely been evaluated. We assessed simulations by an ensemble of dynamic global vegetation models over Australia and highlighted a number of key areas that lead to model divergence on both short (inter-annual) and long (decadal) timescales.
Juhwan Lee, Raphael A. Viscarra Rossel, Mingxi Zhang, Zhongkui Luo, and Ying-Ping Wang
Biogeosciences, 18, 5185–5202, https://doi.org/10.5194/bg-18-5185-2021, https://doi.org/10.5194/bg-18-5185-2021, 2021
Short summary
Short summary
We performed Roth C simulations across Australia and assessed the response of soil carbon to changing inputs and future climate change using a consistent modelling framework. Site-specific initialisation of the C pools with measurements of the C fractions is essential for accurate simulations of soil organic C stocks and composition at a large scale. With further warming, Australian soils will become more vulnerable to C loss: natural environments > native grazing > cropping > modified grazing.
Mengyuan Mu, Martin G. De Kauwe, Anna M. Ukkola, Andy J. Pitman, Weidong Guo, Sanaa Hobeichi, and Peter R. Briggs
Earth Syst. Dynam., 12, 919–938, https://doi.org/10.5194/esd-12-919-2021, https://doi.org/10.5194/esd-12-919-2021, 2021
Short summary
Short summary
Groundwater can buffer the impacts of drought and heatwaves on ecosystems, which is often neglected in model studies. Using a land surface model with groundwater, we explained how groundwater sustains transpiration and eases heat pressure on plants in heatwaves during multi-year droughts. Our results showed the groundwater’s influences diminish as drought extends and are regulated by plant physiology. We suggest neglecting groundwater in models may overstate projected future heatwave intensity.
Philipp Baumann, Anatol Helfenstein, Andreas Gubler, Armin Keller, Reto Giulio Meuli, Daniel Wächter, Juhwan Lee, Raphael Viscarra Rossel, and Johan Six
SOIL, 7, 525–546, https://doi.org/10.5194/soil-7-525-2021, https://doi.org/10.5194/soil-7-525-2021, 2021
Short summary
Short summary
We developed the Swiss mid-infrared spectral library and a statistical model collection across 4374 soil samples with reference measurements of 16 properties. Our library incorporates soil from 1094 grid locations and 71 long-term monitoring sites. This work confirms once again that nationwide spectral libraries with diverse soils can reliably feed information to a fast chemical diagnosis. Our data-driven reduction of the library has the potential to accurately monitor carbon at the plot scale.
Sanaa Hobeichi, Gab Abramowitz, and Jason P. Evans
Hydrol. Earth Syst. Sci., 25, 3855–3874, https://doi.org/10.5194/hess-25-3855-2021, https://doi.org/10.5194/hess-25-3855-2021, 2021
Short summary
Short summary
Evapotranspiration (ET) links the water, energy and carbon cycle on land. Reliable ET estimates are key to understand droughts and flooding. We develop a new ET dataset, DOLCE V3, by merging multiple global ET datasets, and we show that it matches ET observations better and hence is more reliable than its parent datasets. Next, we use DOLCE V3 to examine recent changes in ET and find that ET has increased over most of the land, decreased in some regions, and has not changed in some other regions
Anatol Helfenstein, Philipp Baumann, Raphael Viscarra Rossel, Andreas Gubler, Stefan Oechslin, and Johan Six
SOIL, 7, 193–215, https://doi.org/10.5194/soil-7-193-2021, https://doi.org/10.5194/soil-7-193-2021, 2021
Short summary
Short summary
In this study, we show that a soil spectral library (SSL) can be used to predict soil carbon at new and very different locations. The importance of this finding is that it requires less time-consuming lab work than calibrating a new model for every local application, while still remaining similar to or more accurate than local models. Furthermore, we show that this method even works for predicting (drained) peat soils, using a SSL with mostly mineral soils containing much less soil carbon.
Lina Teckentrup, Martin G. De Kauwe, Andrew J. Pitman, and Benjamin Smith
Biogeosciences, 18, 2181–2203, https://doi.org/10.5194/bg-18-2181-2021, https://doi.org/10.5194/bg-18-2181-2021, 2021
Short summary
Short summary
The El Niño–Southern Oscillation (ENSO) describes changes in the sea surface temperature patterns of the Pacific Ocean. This influences the global weather, impacting vegetation on land. There are two types of El Niño: central Pacific (CP) and eastern Pacific (EP). In this study, we explored the long-term impacts on the carbon balance on land linked to the two El Niño types. Using a dynamic vegetation model, we simulated what would happen if only either CP or EP El Niño events had occurred.
Zhongkui Luo, Raphael A. Viscarra-Rossel, and Tian Qian
Biogeosciences, 18, 2063–2073, https://doi.org/10.5194/bg-18-2063-2021, https://doi.org/10.5194/bg-18-2063-2021, 2021
Short summary
Short summary
Using the data from 141 584 whole-soil profiles across the globe, we disentangled the relative importance of biotic, climatic and edaphic variables in controlling global SOC stocks. The results suggested that soil properties and climate contributed similarly to the explained global variance of SOC in four sequential soil layers down to 2 m. However, the most important individual controls are consistently soil-related, challenging current climate-driven framework of SOC dynamics.
Mengyuan Mu, Martin G. De Kauwe, Anna M. Ukkola, Andy J. Pitman, Teresa E. Gimeno, Belinda E. Medlyn, Dani Or, Jinyan Yang, and David S. Ellsworth
Hydrol. Earth Syst. Sci., 25, 447–471, https://doi.org/10.5194/hess-25-447-2021, https://doi.org/10.5194/hess-25-447-2021, 2021
Short summary
Short summary
Land surface model (LSM) is a critical tool to study land responses to droughts and heatwaves, but lacking comprehensive observations limited past model evaluations. Here we use a novel dataset at a water-limited site, evaluate a typical LSM with a range of competing model hypotheses widely used in LSMs and identify marked uncertainty due to the differing process assumptions. We show the extensive observations constrain model processes and allow better simulated land responses to these extremes.
Erqian Cui, Chenyu Bian, Yiqi Luo, Shuli Niu, Yingping Wang, and Jianyang Xia
Biogeosciences, 17, 6237–6246, https://doi.org/10.5194/bg-17-6237-2020, https://doi.org/10.5194/bg-17-6237-2020, 2020
Short summary
Short summary
Mean annual net ecosystem productivity (NEP) is related to the magnitude of the carbon sink of a specific ecosystem, while its inter-annual variation (IAVNEP) characterizes the stability of such a carbon sink. Thus, a better understanding of the co-varying NEP and IAVNEP is critical for locating the major and stable carbon sinks on land. Based on daily NEP observations from eddy-covariance sites, we found local indicators for the spatially varying NEP and IAVNEP, respectively.
Cited articles
Abramoff, R. Z., Guenet, B., Zhang, H., Georgiou, K., Xu, X., Viscarra Rossel, R. A., Yuan, W., and Ciais, P.: Improved global-scale predictions of soil carbon stocks with Millennial Version 2, Soil Biol. Biochem., 164, 108466, https://doi.org/10.1016/j.soilbio.2021.108466, 2022.
Abs, E. and Ferrière, R.: Modeling microbial dynamics and heterotrophic soil respiration, in: Biogeochemical cycles, American Geophysical Union, 103–129, https://doi.org/10.1002/9781119413332.ch5, 2020.
Adhikari, K., Mishra, U., Owens, P., Libohova, Z., Wills, S., Riley, W., Hoffman, F., and Smith, D.: Importance and strength of environmental controllers of soil organic carbon changes with scale, Geoderma, 375, 114472, https://doi.org/10.1016/j.geoderma.2020.114472, 2020.
Australian Government: National Vegetation Information System (NVIS), https://www.dcceew.gov.au/environment/land/native-vegetation/national-vegetation-information-system, last access: 1 April 2024.
Bai, Y. and Cotrufo, M. F.: Grassland soil carbon sequestration: Current understanding, challenges, and solutions, Science, 377, 603–608, https://doi.org/10.1126/science.abo2380, 2022.
Bennett, L. T., Hinko-Najera, N., Aponte, C., Nitschke, C. R., Fairman, T. A., Fedrigo, M., and Kasel, S.: Refining benchmarks for soil organic carbon in Australia's temperate forests, Geoderma, 368, 114246, https://doi.org/10.1016/j.geoderma.2020.114246, 2020.
Bissett, A., Fitzgerald, A., Meintjes, T., Mele, P. M., Reith, F., Dennis, P. G., Breed, M. F., Brown, B., Brown, M. V., Brugger, J., Byrne, M., Caddy-Retalic, S., Carmody, B., Coates, D. J., Correa, C., Ferrari, B. C., Gupta, V. V. S. R., Hamonts, K., Haslem, A., Hugenholtz, P., Karan, M., Koval, J., Lowe, A. J., Macdonald, S., McGrath, L., Martin, D., Morgan, M., North, K. I., Paungfoo-Lonhienne, C., Pendall, E., Phillips, L., Pirzl, R., Powell, J.R., Ragan, M. A., Schmidt, S., Seymour, N., Snape, I., Stephen, J. R., Stevens, M., Tinning, M., Williams, K., Yeoh, Y. K., Zammit, C. M., and Young, A.: Introducing BASE: the biomes of Australian soil environments soil microbial diversity database, GigaScicence, 5, s13742, https://doi.org/10.1186/s13742-016-0126-5, 2016.
Bioplatforms Australia: Soil Biodiversity, https://bioplatforms.com/projects/soil-biodiversity/, last access: 1 April 2024.
Bossio, D., Cook-Patton, S., Ellis, P., Fargione, J., Sanderman, J., Smith, P., Wood, S., Zomer, R., Von Unger, M., and Emmer, I.: The role of soil carbon in natural climate solutions, Nat. Sustain., 3, 391–398, https://doi.org/10.1038/s41893-020-0491-z, 2020.
Breiman, L.: Random forests, Mach. Learn., 45, 5–32, https://doi.org/10.1023/A:1010933404324, 2001.
Bronick, C. J. and Lal, R: Soil structure and management: a review, Geoderma, 124, 3–22, https://doi.org/10.1016/j.geoderma.2004.03.005, 2005.
Cranko Page, J., Abramowitz, G., De Kauwe, M. G., and Pitman, A. J.: Are plant functional types fit for purpose?, Geophys. Res. Lett., 51, e2023GL104962, https://doi.org/10.1029/2023GL104962, 2024.
Chandel, A. K., Jiang, L., and Luo, Y.: Microbial Models for Simulating Soil Carbon Dynamics: A Review, J. Geophys. Res.-Biogeo., e2023JG007436, https://doi.org/10.1029/2023JG007436, 2023.
De Deyn, G. B., Cornelissen, J. H., and Bardgett, R. D.: Plant functional traits and soil carbon sequestration in contrasting biomes, Ecol. Lett., 11, 516–531, https://doi.org/10.1111/j.1461-0248.2008.01164.x, 2008.
Debeer, D. and Strobl, C.: Conditional permutation importance revisited, BMC Bioinformatics, 21, 1–30, https://doi.org/10.1186/s12859-020-03622-2, 2020.
Doetterl, S., Stevens, A., Six, J., Merckx, R., Van Oost, K., Casanova Pinto, M., Casanova-Katny, A., Muñoz, C., Boudin, M., and Zagal Venegas, E.: Soil carbon storage controlled by interactions between geochemistry and climate, Nat. Geosci., 8, 780–783, https://doi.org/10.1038/ngeo2516, 2015.
Duan, Q., Gupta, V. K., and Sorooshian, S.: Shuffled complex evolution approach for effective and efficient global minimization, J. Optimiz. Theory. App., 76, 501–521, https://doi.org/10.1007/BF00939380, 1993.
Famiglietti, C. A., Worden, M., Quetin, G. R., Smallman, T. L., Dayal, U., Bloom, A. A., Williams, M., and Konings, A. G.: Global net biome CO2 exchange predicted comparably well using parameter–environment relationships and plant functional types, Glob. Change Biol., 29, 2256–2273, https://doi.org/10.1111/gcb.16574, 2023.
Faucon, M.-P., Houben, D., and Lambers, H.: Plant functional traits: soil and ecosystem services, Trends Plant Sci., 22, 385–394, https://doi.org/10.1016/j.tplants.2017.01.005, 2017.
Georgiou, K., Malhotra, A., Wieder, W. R., Ennis, J. H., Hartman, M. D., Sulman, B. N., Berhe, A. A., Grandy, A. S., Kyker-Snowman, E., and Lajtha, K.: Divergent controls of soil organic carbon between observations and process-based models, Biogeochemistry, 156, 5–17, https://doi.org/10.1007/s10533-021-00819-2, 2021.
Georgiou, K., Jackson, R. B., Vindušková, O., Abramoff, R. Z., Ahlström, A., Feng, W., Harden, J. W., Pellegrini, A. F., Polley, H. W., and Soong, J. L.: Global stocks and capacity of mineral-associated soil organic carbon, Nat. Commun., 13, 3797, https://doi.org/10.1038/s41467-022-31540-9, 2022.
Grace, P. R., Post, W. M., and Hennessy, K.: The potential impact of climate change on Australia's soil organic carbon resources, Carbon Balance Manag., 1, 1–10, https://doi.org/10.1186/1750-0680-1-14, 2006.
Grundy, M., Viscarra Rossel, R. A., Searle, R., Wilson, P., Chen, C., and Gregory, L.: Soil and landscape grid of Australia, Soil Res., 53, 835–844, https://doi.org/10.1071/SR15191, 2015.
Guo, Z., Adhikari, K., Chellasamy, M., Greve, M. B., Owens, P. R., and Greve, M. H.: Selection of terrain attributes and its scale dependency on soil organic carbon prediction, Geoderma, 340, 303–312, https://doi.org/10.1016/j.geoderma.2019.01.023, 2019.
Heung, B., Ho, H. C., Zhang, J., Knudby, A., Bulmer, C. E., and Schmidt, M. G.: An overview and comparison of machine-learning techniques for classification purposes in digital soil mapping, Geoderma, 265, 62–77, https://doi.org/10.1016/j.geoderma.2015.11.014, 2016.
Hobley, E., Wilson, B., Wilkie, A., Gray, J., and Koen, T.: Drivers of soil organic carbon storage and vertical distribution in Eastern Australia, Plant Soil, 390, 111–127, https://doi.org/10.1007/s11104-015-2380-1, 2015.
Hobley, E. U., Baldock, J., and Wilson, B.: Environmental and human influences on organic carbon fractions down the soil profile, Agr. Ecosyst. Environ., 223, 152–166, https://doi.org/10.1016/j.agee.2016.03.004, 2016.
Jackson, R. B., Lajtha, K., Crow, S. E., Hugelius, G., Kramer, M. G., and Piñeiro, G.: The ecology of soil carbon: pools, vulnerabilities, and biotic and abiotic controls, Annu. Rev. Ecol. Evol. Syst., 48, 419–445, https://doi.org/10.1146/annurev-ecolsys-112414-054234, 2017.
Jeffrey, S. J., Carter, J. O., Moodie, K. B., and Beswick, A. R.: Using spatial interpolation to construct a comprehensive archive of Australian climate data, Environ. Model. Softw., 16, 309–330, https://doi.org/10.1016/S1364-8152(01)00008-1, 2001.
Jenny, H.: Factors of soil formation: a system of quantitative pedology, Agron. J., 33, 857–858, https://doi.org/10.2134/agronj1941.00021962003300090016x, 1941.
Jobbágy, E. G. and Jackson, R. B.: The Vertical Distribution of Soil Organic Carbon and Its Relation to Climate and Vegetation, Ecol. Appl., 10, 423–436, https://doi.org/10.1890/1051-0761(2000)010[0423:TVDOSO]2.0.CO;2, 2000.
Keskin, H., Grunwald, S., and Harris, W. G.: Digital mapping of soil carbon fractions with machine learning, Geoderma, 339, 40–58, https://doi.org/10.1016/j.geoderma.2018.12.037, 2019.
Lamichhane, S., Kumar, L., and Wilson, B.: Digital soil mapping algorithms and covariates for soil organic carbon mapping and their implications: A review., Geoderma, 352, 395–413, https://doi.org/10.1016/j.geoderma.2019.05.031, 2019.
Lawrence, I. and Lin, K.: A concordance correlation coefficient to evaluate reproducibility, Biometrics, 45, 255–268, https://doi.org/10.2307/2532051, 1989.
Le Noë, J., Manzoni, S., Abramoff, R., Bolscher T., Bruni, E., Cardinael, R., Ciais, P., Chenu, C., Clivot, H., Derrien, D., Ferchaud, F., Garnier, P., Goll, D., Lashermes, G., Martin, M., Rasse, D., Rees, F., Sainte-Marie J., Salmon, E., Schiedung, M., Schimel, J., Wieder, W., Abiven, S., Barre, P., Cecillon, L., and Guenet, B.: Soil organic carbon models need independent time-series validation for reliable prediction, Commun. Earth Environ., 4, 158, https://doi.org/10.1038/s43247-023-00830-5, 2023.
Lee, J., Viscarra Rossel, R. A., Zhang, M., Luo, Z., and Wang, Y.-P.: Assessing the response of soil carbon in Australia to changing inputs and climate using a consistent modelling framework, Biogeosciences, 18, 5185–5202, https://doi.org/10.5194/bg-18-5185-2021, 2021.
Lefèvre, C., Rekik, F., Alcantara, V., and Wiese, L.: Soil organic carbon: the hidden potential, Food and Agriculture Organization of the United Nations (FAO), http://www.fao.org/3/a-i6937e.pdf (last access: 1 June 2024), 2017.
Lehmann, J. and Kleber, M.: The contentious nature of soil organic matter, Nature, 528, 60–68, https://doi.org/10.1038/nature16069, 2015.
Liang, Z., Chen, S., Yang, Y., Zhou, Y., and Shi, Z.: High-resolution three-dimensional mapping of soil organic carbon in China: Effects of SoilGrids products on national modeling, Sci. Total Environ., 685, 480–489, https://doi.org/10.1016/j.scitotenv.2019.05.332, 2019.
Lorenz, K., Lal, R., and Ehlers, K.: Soil organic carbon stock as an indicator for monitoring land and soil degradation in relation to United Nations' Sustainable Development Goals, Land Degrad. Dev., 30, 824–838, https://doi.org/10.1002/ldr.3270, 2019.
Lunt, I. D., Eldridge, D. J., Morgan, J. W., and Witt, G. B.: A framework to predict the effects of livestock grazing and grazing exclusion on conservation values in natural ecosystems in Australia, Aust. J. Bot., 55, 401–415, https://doi.org/10.1071/BT06178, 2007.
Luo, Y., Ahlström, A., Allison, S. D., Batjes, N. H., Brovkin, V., Carvalhais, N., Chappell, A., Ciais, P., Davidson, E. A., and Finzi, A.: Toward more realistic projections of soil carbon dynamics by Earth system models, Global Biogeochem. Cy., 30, 40–56, https://doi.org/10.1002/2015GB005239, 2016.
Luo, Z., Wang, E., and Sun, O. J.: Soil carbon change and its responses to agricultural practices in Australian agro-ecosystems: a review and synthesis, Geoderma, 155, 211–223, https://doi.org/10.1016/j.geoderma.2009.12.012, 2010.
Malone, B.: Soil and Landscape Grid National Soil Attribute Maps – Bulk Density – Whole Earth – Release 2, v3, CSIRO Data Collection [data set], https://doi.org/10.25919/gxyn-pd07, 2023.
Malone, B. and Searle, R.: Soil and Landscape Grid National Soil Attribute Maps – Clay (3” resolution) – Release 2. v5, CSIRO Data Collection [data set], https://doi.org/10.25919/hc4s-3130, 2022.
Marshall, T. J., Holmes, J. W., and Rose, C. W.: Soil physics, 3rd ed., Cambridge University Press, Cambridge, https://doi.org/10.1017/CBO9781139170673, 1996.
McBratney, A. B., Santos, M. M., and Minasny, B.: On digital soil mapping, Geoderma, 117, 3–52, https://doi.org/10.1016/S0016-7061(03)00223-4, 2003.
Minasny, B., McBratney, A. B., Malone, B. P., and Wheeler, I.: Digital mapping of soil carbon, Adv. Agron., 118, 1–47, https://doi.org/10.1016/B978-0-12-405942-9.00001-3, 2013.
Mishra, U. and Riley, W. J.: Scaling impacts on environmental controls and spatial heterogeneity of soil organic carbon stocks, Biogeosciences, 12, 3993–4004, https://doi.org/10.5194/bg-12-3993-2015, 2015.
Mokany, K., Raison, R. J., and Prokushkin, A. S.: Critical analysis of root: shoot ratios in terrestrial biomes, Glob. Change Biol., 12, 84–96, https://doi.org/10.1111/j.1365-2486.2005.001043.x, 2006.
Murphy, B. W.: Impact of soil organic matter on soil properties – a review with emphasis on Australian soils, Soil Res., 53, 605–635, https://doi.org/10.1071/SR14246, 2015.
Nyaupane, K., Mishra, U., Tao, F., Yeo, K., Riley, W. J., Hoffman, F. M., and Gautam, S.: Observational benchmarks inform representation of soil organic carbon dynamics in land surface models, Biogeosciences Discuss. [preprint], https://doi.org/10.5194/bg-2023-50, in review, 2023.
Panchal, P., Preece, C., Penuelas, J., and Giri, J.: Soil carbon sequestration by root exudates, Trends Plant Sci., 27, 749–757, https://doi.org/10.1016/j.tplants.2022.04.009, 2022.
Poulter, B., MacBean, N., Hartley, A., Khlystova, I., Arino, O., Betts, R., Bontemps, S., Boettcher, M., Brockmann, C., Defourny, P., Hagemann, S., Herold, M., Kirches, G., Lamarche, C., Lederer, D., Ottlé, C., Peters, M., and Peylin, P.: Plant functional type classification for earth system models: results from the European Space Agency's Land Cover Climate Change Initiative, Geosci. Model Dev., 8, 2315–2328, https://doi.org/10.5194/gmd-8-2315-2015, 2015.
Queensland Government: SILO – Australian climate data from 1889 to yesterday, https://www.longpaddock.qld.gov.au/silo/gridded-data/, last access: 1 April 2024.
Ren, C., Mo, F., Zhou, Z., Bastida, F., Delgado-Baquerizo, M., Wang, J., Zhang, X., Luo, Y., Griffis, T. J., and Han, X.: The global biogeography of soil priming effect intensity, Global Ecol. Biogeogr., 31, 1679–1687, https://doi.org/10.1111/geb.13524, 2022.
Running, S. and Zhao, M.: MODIS/Terra Net Primary Production Gap-Filled Yearly L4 Global 500m SIN Grid V061, NASA EOSDIS Land Processes Distributed Active Archive Centre [data set], https://doi.org/10.5067/MODIS/MOD17A3HGF.061, 2021.
Rumpel, C., Amiraslani, F., Koutika, L.-S., Smith, P., Whitehead, D., and Wollenberg, E.: Put more carbon in soils to meet Paris climate pledges, Nature, 564, 32–34, https://doi.org/10.1038/d41586-018-07587-4, 2018.
Six, J., Conant, R. T., Paul, E. A., and Paustian, K.: Stabilization mechanisms of soil organic matter: Implications for C-saturation of soils, Plant Soil, 241, 155–176, https://doi.org/10.1023/A:1016125726789, 2002.
Smith, P.: Soil carbon sequestration and biochar as negative emission technologies, Glob. Change Biol., 22, 1315–1324, https://doi.org/10.1111/gcb.13178, 2016.
Stockmann, U., Adams, M. A., Crawford, J. W., Field, D. J., Henakaarchchi, N., Jenkins, M., Minasny, B., McBratney, A. B., De Courcelles, V. d. R., and Singh, K.: The knowns, known unknowns and unknowns of sequestration of soil organic carbon, Agr. Ecosyst. Environ., 164, 80–99, https://doi.org/10.1016/j.agee.2012.10.001, 2013.
Stockmann, U., Padarian, J., McBratney, A., Minasny, B., de Brogniez, D., Montanarella, L., Hong, S. Y., Rawlins, B. G., and Field, D. J.: Global soil organic carbon assessment, Glob. Food Sec., 6, 9–16, https://doi.org/10.1016/j.gfs.2015.07.001, 2015.
Terrer, C., Phillips, R. P., Hungate, B. A., Rosende, J., Pett-Ridge, J., Craig, M. E., van Groenigen, K. J., Keenan, T. F., Sulman, B. N., Stocker, B. D., Reich, P. B., Pellegrini, A. F. A., Pendall, E., Zhang, H., Evans, R. D., Carrillo, Y., Fisher, J. B., Van Sundert, K., Vicca, S., and Jackson, R. B.: A trade-off between plant and soil carbon storage under elevated CO2, Nature, 591, 599–603, https://doi.org/10.1038/s41586-021-03306-8, 2021.
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.
Todd-Brown, K. E. O., Randerson, J. T., Hopkins, F., Arora, V., Hajima, T., Jones, C., Shevliakova, E., Tjiputra, J., Volodin, E., Wu, T., Zhang, Q., and Allison, S. D.: Changes in soil organic carbon storage predicted by Earth system models during the 21st century, Biogeosciences, 11, 2341–2356, https://doi.org/10.5194/bg-11-2341-2014, 2014.
Viscarra Rossel, R. A., Webster, R., Bui, E. N., and Baldock, J. A.: Baseline map of organic carbon in Australian soil to support national carbon accounting and monitoring under climate change, Glob. Change Biol., 20, 2953–2970, https://doi.org/10.1111/gcb.12569, 2014.
Viscarra Rossel, R. A., Chen, C., Grundy, M. J., Searle, R., Clifford, D., and Campbell, P. H.: The Australian three-dimensional soil grid: Australia's contribution to the GlobalSoilMap project, Soil Res., 53, 845–864, https://doi.org/10.1071/SR14366, 2015.
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.
Viscarra Rossel, R. A., Webster, R., Zhang M., Shen, Z., Dixon, K., Wang, Y. P., and Walden, L.: How much organic carbon could the soil store? The carbon sequestration potential of Australian soil, Glob. Change Biol., 30, e17053, https://doi.org/10.1111/gcb.17053, 2023.
Wadoux, A. M. J., Román Dobarco, M., Malone, B., Minasny, B., McBratney, A. B., and Searle, R.: Baseline high-resolution maps of organic carbon content in Australian soils, Sci. Data, 10, 181, https://doi.org/10.1038/s41597-023-02056-8, 2023.
Walden, L., Serrano, O., Zhang, M., Shen, Z., Sippo, J. Z., Bennett, L. T., Maher, D. T., Lovelock, C. E., Macreadie, P. I., and Gorham, C.: Multi-scale mapping of Australia's terrestrial and blue carbon stocks and their continental and bioregional drivers, Commun. Earth Environ., 4, 189, https://doi.org/10.1038/s43247-023-00838-x, 2023.
Wang, B., Waters, C., Orgill, S., Gray, J., Cowie, A., Clark, A., and Li Liu, D.: High resolution mapping of soil organic carbon stocks using remote sensing variables in the semi-arid rangelands of eastern Australia, Sci. Total Environ., 630, 367–378, https://doi.org/10.1016/j.scitotenv.2018.02.204, 2018a.
Wang, B., Waters, C., Orgill, S., Cowie, A., Clark, A., Li Liu, D., Simpson, M., McGowen, I., and Sides, T.: Estimating soil organic carbon stocks using different modelling techniques in the semi-arid rangelands of eastern Australia, Ecol. Indic., 88, 425–438, https://doi.org/10.1016/j.ecolind.2018.01.049, 2018b.
Wang, B., Gray, J. M., Waters, C. M., Anwar, M. R., Orgill, S. E., Cowie, A. L., Feng, P., and Li Liu, D.: Modelling and mapping soil organic carbon stocks under future climate change in south-eastern Australia, Geoderma, 405, 115442, https://doi.org/10.1016/j.geoderma.2021.115442, 2022.
Wang, L.: Wanglingfei170/MIMICS: MIMICS-Australia (v1.0-MIMICS-Aus), Zenodo [code], https://doi.org/10.5281/zenodo.13638194, 2024.
Wang, Y. P., Zhang, H., Ciais, P., Goll, D., Huang, Y., Wood, J. D., Ollinger, S. V., Tang, X., and Prescher, A. K.: Microbial activity and root carbon inputs are more important than soil carbon diffusion in simulating soil carbon profiles, J. Geophys. Res.-Biogeo., 126, e2020JG006205, https://doi.org/10.1029/2020JG006205, 2021.
Wieder, W. R., Grandy, A. S., Kallenbach, C. M., Taylor, P. G., and Bonan, G. B.: Representing life in the Earth system with soil microbial functional traits in the MIMICS model, Geosci. Model Dev., 8, 1789–1808, https://doi.org/10.5194/gmd-8-1789-2015, 2015.
Wiesmeier, M., Barthold, F., Spörlein, P., Geuß, U., Hangen, E., Reischl, A., Schilling, B., Angst, G., von Lützow, M., and Kögel-Knabner, I.: Estimation of total organic carbon storage and its driving factors in soils of Bavaria (southeast Germany), Geoderma Regional, 1, 67–78, https://doi.org/10.1016/j.geodrs.2014.09.001, 2014.
Wiesmeier, M., Urbanski, L., Hobley, E., Lang, B., von Lützow, M., Marin-Spiotta, E., van Wesemael, B., Rabot, E., Ließ, M., and Garcia-Franco, N.: Soil organic carbon storage as a key function of soils-A review of drivers and indicators at various scales. Geoderma, 333, 149–162, https://doi.org/10.1016/j.geoderma.2018.07.026, 2019.
Wynn, J. G., Bird, M. I., Vellen, L., Grand-Clement, E., Carter, J., and Berry, S. L.: Continental-scale measurement of the soil organic carbon pool with climatic, edaphic, and biotic controls. Global Biogeochem. Cy., 20, GB1007, https://doi.org/10.1029/2005GB002576, 2006.
Zhang, H., Goll, D. S., Wang, Y. P., Ciais, P., Wieder, W. R., Abramoff, R., Huang, Y., Guenet, B., Prescher, A. K., and Viscarra Rossel, R. A.: Microbial dynamics and soil physicochemical properties explain large-scale variations in soil organic carbon, Glob. Change Biol., 26, 2668–2685, https://doi.org/10.1111/gcb.14994, 2020.
Short summary
Effective management of soil organic carbon (SOC) requires accurate knowledge of its distribution and factors influencing its dynamics. We identify the importance of variables in spatial SOC variation and estimate SOC stocks in Australia using various models. We find there are significant disparities in SOC estimates when different models are used, highlighting the need for a critical re-evaluation of land management strategies that rely on the SOC distribution derived from a single approach.
Effective management of soil organic carbon (SOC) requires accurate knowledge of its...
