the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
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
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.
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Mercedes Román Dobarco et al.
Status: closed
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CC1: 'uncertainty aggregation and inter-comparison', Barnon Balaza, 19 Nov 2022
Very interesting research, good job to the authors!
As an ecologist/ machine learning practitioner myself, I can't help but state these two concerns if the authors mind:
1. It's not clearly stated how did the authors aggregate the SOC uncertainty when producing the Table 3 results (+- numbers).
2. Since this is not the first time that SOC products have been produced including Australia, why did the authors did not inter-compare their maps to existing Australia-wide or global-wide similar products.
Thank you in advance for the clarificaitons.
Sincerely,
Barnon
Citation: https://doi.org/10.5194/bg-2022-207-CC1 -
AC1: 'Reply on CC1', Mercedes Roman Dobarco, 28 Nov 2022
Dear Barnon,
Thank you for your comments and encouragement! We are glad that researchers from different disciplines give input for this paper. We try to clarify your concerns below:
1. The reviewer is right to ask how the values of the uncertainty for a spatial aggregate (here, by biome) would have been obtained. The uncertainty of a spatial average or total is not the average of the uncertainty at all points in the aggregate (as it is for the mean values), because this would lead to unrealistically small values of uncertainty since errors within the aggregate would cancel out. To obtain the uncertainty of a spatial aggregate, one need to account for spatial autocorrelation of map errors using, for example, a correlation function of the residuals and block kriging of the error. In Table 3, however, we do not report uncertainty estimates, but only the summary statistics of the calibration points by biome. The standard deviation values refer to the standard deviation of the observed SOC fraction data. We will clarify this in the revised manuscript.
2. You are right, a comparison with the previous SOC fraction products (Soil and Landscape Grid of Australia v1) (Viscarra-Rossel et al., 2019) is missing. We will incorporate a comparison between the three maps in the revised version of the manuscript. Both products used different datasets and mapping methodologies, so we expect to see broad differences.
Viscarra Rossel, R. A., Lee, J., Behrens, T., Luo, Z., Baldock, J., & Richards, A. (2019). Continental-scale soil carbon composition and vulnerability modulated by regional environmental controls. Nature Geoscience. https://doi.org/10.1038/s41561-019-0373-z
The comparison between previous and current versions of bulk SOC maps has been done in a separate paper (Wadoux et al., under review), but we mention it briefly in the discussion.
Kind regards,
Mercedes
Citation: https://doi.org/10.5194/bg-2022-207-AC1
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AC1: 'Reply on CC1', Mercedes Roman Dobarco, 28 Nov 2022
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RC1: 'Comment on bg-2022-207', Anonymous Referee #1, 25 Nov 2022
The comment was uploaded in the form of a supplement: https://bg.copernicus.org/preprints/bg-2022-207/bg-2022-207-RC1-supplement.pdf
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AC2: 'Reply on RC1', Mercedes Roman Dobarco, 22 Dec 2022
The comment was uploaded in the form of a supplement: https://bg.copernicus.org/preprints/bg-2022-207/bg-2022-207-AC2-supplement.pdf
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AC2: 'Reply on RC1', Mercedes Roman Dobarco, 22 Dec 2022
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RC2: 'Comment on bg-2022-207', Anonymous Referee #2, 07 Dec 2022
The comment was uploaded in the form of a supplement: https://bg.copernicus.org/preprints/bg-2022-207/bg-2022-207-RC2-supplement.pdf
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AC3: 'Reply on RC2', Mercedes Roman Dobarco, 10 Jan 2023
The comment was uploaded in the form of a supplement: https://bg.copernicus.org/preprints/bg-2022-207/bg-2022-207-AC3-supplement.pdf
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AC3: 'Reply on RC2', Mercedes Roman Dobarco, 10 Jan 2023
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RC3: 'Comment on bg-2022-207', Anonymous Referee #3, 03 Feb 2023
Publisher’s note: the content of this comment was removed on 27 February 2023 after approval of the BG co-editors-in-chief since the formulations were inappropriate.
Citation: https://doi.org/10.5194/bg-2022-207-RC3 -
AC4: 'Reply on RC3', Mercedes Roman Dobarco, 22 Feb 2023
Publisher’s note: the content of this comment as well as its supplement were removed on 27 February 2023 after approval of the BG co-editors-in-chief since the formulations were inappropriate.
Citation: https://doi.org/10.5194/bg-2022-207-AC4 -
AC5: 'Reply on RC3', Mercedes Roman Dobarco, 22 Feb 2023
Publisher’s note: the content of this comment as well as its supplement were removed on 27 February 2023 after approval of the BG co-editors-in-chief since the formulations were inappropriate.
Citation: https://doi.org/10.5194/bg-2022-207-AC5 -
AC6: 'Reply on RC3', Mercedes Roman Dobarco, 22 Feb 2023
Publisher’s note: the content of this comment was removed on 27 February 2023 after approval of the BG co-editors-in-chief since the formulations were inappropriate.
Citation: https://doi.org/10.5194/bg-2022-207-AC6
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AC4: 'Reply on RC3', Mercedes Roman Dobarco, 22 Feb 2023
Status: closed
-
CC1: 'uncertainty aggregation and inter-comparison', Barnon Balaza, 19 Nov 2022
Very interesting research, good job to the authors!
As an ecologist/ machine learning practitioner myself, I can't help but state these two concerns if the authors mind:
1. It's not clearly stated how did the authors aggregate the SOC uncertainty when producing the Table 3 results (+- numbers).
2. Since this is not the first time that SOC products have been produced including Australia, why did the authors did not inter-compare their maps to existing Australia-wide or global-wide similar products.
Thank you in advance for the clarificaitons.
Sincerely,
Barnon
Citation: https://doi.org/10.5194/bg-2022-207-CC1 -
AC1: 'Reply on CC1', Mercedes Roman Dobarco, 28 Nov 2022
Dear Barnon,
Thank you for your comments and encouragement! We are glad that researchers from different disciplines give input for this paper. We try to clarify your concerns below:
1. The reviewer is right to ask how the values of the uncertainty for a spatial aggregate (here, by biome) would have been obtained. The uncertainty of a spatial average or total is not the average of the uncertainty at all points in the aggregate (as it is for the mean values), because this would lead to unrealistically small values of uncertainty since errors within the aggregate would cancel out. To obtain the uncertainty of a spatial aggregate, one need to account for spatial autocorrelation of map errors using, for example, a correlation function of the residuals and block kriging of the error. In Table 3, however, we do not report uncertainty estimates, but only the summary statistics of the calibration points by biome. The standard deviation values refer to the standard deviation of the observed SOC fraction data. We will clarify this in the revised manuscript.
2. You are right, a comparison with the previous SOC fraction products (Soil and Landscape Grid of Australia v1) (Viscarra-Rossel et al., 2019) is missing. We will incorporate a comparison between the three maps in the revised version of the manuscript. Both products used different datasets and mapping methodologies, so we expect to see broad differences.
Viscarra Rossel, R. A., Lee, J., Behrens, T., Luo, Z., Baldock, J., & Richards, A. (2019). Continental-scale soil carbon composition and vulnerability modulated by regional environmental controls. Nature Geoscience. https://doi.org/10.1038/s41561-019-0373-z
The comparison between previous and current versions of bulk SOC maps has been done in a separate paper (Wadoux et al., under review), but we mention it briefly in the discussion.
Kind regards,
Mercedes
Citation: https://doi.org/10.5194/bg-2022-207-AC1
-
AC1: 'Reply on CC1', Mercedes Roman Dobarco, 28 Nov 2022
-
RC1: 'Comment on bg-2022-207', Anonymous Referee #1, 25 Nov 2022
The comment was uploaded in the form of a supplement: https://bg.copernicus.org/preprints/bg-2022-207/bg-2022-207-RC1-supplement.pdf
-
AC2: 'Reply on RC1', Mercedes Roman Dobarco, 22 Dec 2022
The comment was uploaded in the form of a supplement: https://bg.copernicus.org/preprints/bg-2022-207/bg-2022-207-AC2-supplement.pdf
-
AC2: 'Reply on RC1', Mercedes Roman Dobarco, 22 Dec 2022
-
RC2: 'Comment on bg-2022-207', Anonymous Referee #2, 07 Dec 2022
The comment was uploaded in the form of a supplement: https://bg.copernicus.org/preprints/bg-2022-207/bg-2022-207-RC2-supplement.pdf
-
AC3: 'Reply on RC2', Mercedes Roman Dobarco, 10 Jan 2023
The comment was uploaded in the form of a supplement: https://bg.copernicus.org/preprints/bg-2022-207/bg-2022-207-AC3-supplement.pdf
-
AC3: 'Reply on RC2', Mercedes Roman Dobarco, 10 Jan 2023
-
RC3: 'Comment on bg-2022-207', Anonymous Referee #3, 03 Feb 2023
Publisher’s note: the content of this comment was removed on 27 February 2023 after approval of the BG co-editors-in-chief since the formulations were inappropriate.
Citation: https://doi.org/10.5194/bg-2022-207-RC3 -
AC4: 'Reply on RC3', Mercedes Roman Dobarco, 22 Feb 2023
Publisher’s note: the content of this comment as well as its supplement were removed on 27 February 2023 after approval of the BG co-editors-in-chief since the formulations were inappropriate.
Citation: https://doi.org/10.5194/bg-2022-207-AC4 -
AC5: 'Reply on RC3', Mercedes Roman Dobarco, 22 Feb 2023
Publisher’s note: the content of this comment as well as its supplement were removed on 27 February 2023 after approval of the BG co-editors-in-chief since the formulations were inappropriate.
Citation: https://doi.org/10.5194/bg-2022-207-AC5 -
AC6: 'Reply on RC3', Mercedes Roman Dobarco, 22 Feb 2023
Publisher’s note: the content of this comment was removed on 27 February 2023 after approval of the BG co-editors-in-chief since the formulations were inappropriate.
Citation: https://doi.org/10.5194/bg-2022-207-AC6
-
AC4: '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.
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