Preprints
https://doi.org/10.5194/bg-2022-207
https://doi.org/10.5194/bg-2022-207
28 Oct 2022
 | 28 Oct 2022
Status: a revised version of this preprint was accepted for the journal BG and is expected to appear here in due course.

Mapping soil organic carbon fractions for Australia, their stocks and uncertainty

Mercedes Román Dobarco, Alexandre M. J-C. Wadoux, Brendan Malone, Budiman Minasny, Alex B. McBratney, and Ross Searle

Abstract. Soil organic carbon (SOC) is the largest terrestrial carbon pool. SOC is composed of a continuum set of compounds with different chemical composition, origin and susceptibilities to decomposition, that are commonly separated into pools characterised by different responses to anthropogenic and environmental disturbance. Here we map the contribution of three SOC fractions to the total SOC content of Australia’s soils. The three SOC fractions: mineral-associated organic carbon (MAOC), particulate organic carbon (POC) and pyrogenic organic carbon (PyOC), represent SOC composition with distinct turnover rates, chemistry, and pathway formation. Data for MAOC, POC, and PyOC were obtained with near- and mid-infrared spectral models calibrated with measured SOC fractions. We transformed the data using an isometric log-ratio transformation (ilr) to account for the closed compositional nature of SOC fractions. The resulting, back-transformed ilr components were mapped across Australia. SOC fraction stocks for the 0–30 cm were derived with maps of total organic carbon concentration, bulk density, coarse fragments and soil thickness. Mapping was done by quantile regression forest fitted with the ilr transformed data and a large set of environmental variables as predictors. The resulting maps along with the quantified uncertainty show the unique spatial pattern of SOC fractions in Australia. MAOC dominated the total SOC with an average of 59 % ± 17.5 %, whereas 28 % ± 17.5 % was PyOC and 13 % ± 11.1 % was POC. The allocation of TOC into the MAOC fractions increased with depth. SOC vulnerability (i.e., POC/[MAOC + PyOC]) was greater in areas with Mediterranean and temperate climate. TOC and the distribution among fractions were the most influential variables on SOC fraction uncertainty. Further, the diversity of climatic and pedological conditions suggests that different mechanisms will control SOC stabilisation and dynamics across the continent, as shown by the model covariates importance metric. We estimated the total SOC stocks (0–30 cm) to be 12.7 Pg MAOC, 2 Pg POC and 5.1 Pg PyOC, which is consistent with previous estimates. The maps of SOC fractions and their stocks can be used for modelling SOC dynamics and forecasting changes in SOC stocks as response to land use change, management, and climate change.

Mercedes Román Dobarco et al.

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CC1: 'uncertainty aggregation and inter-comparison', Barnon Balaza, 19 Nov 2022
    • AC1: 'Reply on CC1', Mercedes Roman Dobarco, 28 Nov 2022
  • RC1: 'Comment on bg-2022-207', Anonymous Referee #1, 25 Nov 2022
  • RC2: 'Comment on bg-2022-207', Anonymous Referee #2, 07 Dec 2022
  • RC3: 'Comment on bg-2022-207', Anonymous Referee #3, 03 Feb 2023
    • AC4: 'Reply on RC3', Mercedes Roman Dobarco, 22 Feb 2023
    • AC5: 'Reply on RC3', Mercedes Roman Dobarco, 22 Feb 2023
    • AC6: 'Reply on RC3', Mercedes Roman Dobarco, 22 Feb 2023

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CC1: 'uncertainty aggregation and inter-comparison', Barnon Balaza, 19 Nov 2022
    • AC1: 'Reply on CC1', Mercedes Roman Dobarco, 28 Nov 2022
  • RC1: 'Comment on bg-2022-207', Anonymous Referee #1, 25 Nov 2022
  • RC2: 'Comment on bg-2022-207', Anonymous Referee #2, 07 Dec 2022
  • RC3: 'Comment on bg-2022-207', Anonymous Referee #3, 03 Feb 2023
    • AC4: 'Reply on RC3', Mercedes Roman Dobarco, 22 Feb 2023
    • AC5: 'Reply on RC3', Mercedes Roman Dobarco, 22 Feb 2023
    • AC6: 'Reply on RC3', Mercedes Roman Dobarco, 22 Feb 2023

Mercedes Román Dobarco et al.

Data sets

SOC fractions SLGA Version 2 Mercedes Román Dobarco, Alexandre M.J-C. Wadoux, Brendan Malone, Budiman Minasny, Alex B. McBratney, and Ross Searle https://data.tern.org.au/landscapes/slga/NationalMaps

Model code and software

SOC fractions - scripts Mercedes Roman, Alexandre Wadoux, Brendan Malone, Ross Searle https://github.com/AusSoilsDSM/SLGA/tree/main/Production/DSM

Mercedes Román Dobarco et al.

Viewed

Total article views: 1,126 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
830 266 30 1,126 47 5 8
  • HTML: 830
  • PDF: 266
  • XML: 30
  • Total: 1,126
  • Supplement: 47
  • BibTeX: 5
  • EndNote: 8
Views and downloads (calculated since 28 Oct 2022)
Cumulative views and downloads (calculated since 28 Oct 2022)

Viewed (geographical distribution)

Total article views: 1,143 (including HTML, PDF, and XML) Thereof 1,143 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 20 Mar 2023
Download
Short summary
Soil organic carbon (SOC) is has an heterogeneous nature and varies in chemistry, stabilization mechanisms and persistence in soil. In this study we mapped the stocks of SOC fractions with different characteristics and turnover rate (presumably PyOC >= MAOC > POC) across Australia, combining spectroscopy and digital soil mapping. The SOC stocks (0–30 cm) were estimated as 12.7 Pg MAOC, 2 Pg POC and 5.1 Pg PyOC.
Altmetrics