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Best practice for upscaling soil organic carbon stocks in salt marshes

Cai Ladd Orcid Logo, Craig Smeaton Orcid Logo, Martin W. Skov Orcid Logo, William E.N. Austin

Geoderma, Volume: 428, Start page: 116188

Swansea University Author: Cai Ladd Orcid Logo

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DOI (Published version): 10.1016/j.geoderma.2022.116188

Abstract

Calculating the amount of soil organic carbon (SOC) stored in coastal environments, including salt marshes, is needed to determine their role in mitigating the Climate Crisis. Several techniques exist to calculate the SOC content of a unit of land from the upscaling of soil cores. However, no compre...

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Published in: Geoderma
ISSN: 0016-7061
Published: Elsevier BV 2022
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URI: https://cronfa.swan.ac.uk/Record/cronfa64484
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However, no comprehensive assessment has been made on the performance of commonly used SOC upscaling techniques until now. We measured the SOC content of soil cores gathered from two Scottish salt marshes. Two SOC values were used for upscaling; SOC content for a 1 m standardised depth (as recommended by the IPCC), and SOC content of the modern marsh deposit (identified in the stratigraphy as a transition from organic-rich (marsh) to mineral-rich (intertidal flat) soil. Twenty-two upscaling techniques were used (SOC content × area, interpolative, and regression-based extrapolative calculations). Leave-one-out cross-validation procedures and prediction interval widths were used to assess the accuracy of each technique. Digital Terrain Models and Normalized Difference Vegetation Indices were used as covariate surfaces in the regression models. We found that marsh-scale SOC stocks varied by as much as fifty-two times depending on which sampling depth and upscaling technique was used. The largest differences emerged when comparing SOC stocks upscaled from 1 m deep and modern marsh deposits. Using the IPCC recommended 1 m sampling depth inflated the SOC stocks of salt marshes, as intertidal flat environments were included in the calculation. Ensemble regression models from the weighted average of seven machine learning algorithm outputs produced the highest upscaling accuracies across marshes and sampling depths. Simple SOC content × area calculations produced marsh-scale SOC stocks that were comparable to stock values produced by more advanced ensemble regression models. However, regression models produced detailed maps of SOC distribution across a marsh, and the associated uncertainty in the SOC values. 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spelling v2 64484 2023-09-08 Best practice for upscaling soil organic carbon stocks in salt marshes 134c870190db4c365e2ccc2d6c107462 0000-0001-5437-6474 Cai Ladd Cai Ladd true false 2023-09-08 SGE Calculating the amount of soil organic carbon (SOC) stored in coastal environments, including salt marshes, is needed to determine their role in mitigating the Climate Crisis. Several techniques exist to calculate the SOC content of a unit of land from the upscaling of soil cores. However, no comprehensive assessment has been made on the performance of commonly used SOC upscaling techniques until now. We measured the SOC content of soil cores gathered from two Scottish salt marshes. Two SOC values were used for upscaling; SOC content for a 1 m standardised depth (as recommended by the IPCC), and SOC content of the modern marsh deposit (identified in the stratigraphy as a transition from organic-rich (marsh) to mineral-rich (intertidal flat) soil. Twenty-two upscaling techniques were used (SOC content × area, interpolative, and regression-based extrapolative calculations). Leave-one-out cross-validation procedures and prediction interval widths were used to assess the accuracy of each technique. Digital Terrain Models and Normalized Difference Vegetation Indices were used as covariate surfaces in the regression models. We found that marsh-scale SOC stocks varied by as much as fifty-two times depending on which sampling depth and upscaling technique was used. The largest differences emerged when comparing SOC stocks upscaled from 1 m deep and modern marsh deposits. Using the IPCC recommended 1 m sampling depth inflated the SOC stocks of salt marshes, as intertidal flat environments were included in the calculation. Ensemble regression models from the weighted average of seven machine learning algorithm outputs produced the highest upscaling accuracies across marshes and sampling depths. Simple SOC content × area calculations produced marsh-scale SOC stocks that were comparable to stock values produced by more advanced ensemble regression models. However, regression models produced detailed maps of SOC distribution across a marsh, and the associated uncertainty in the SOC values. Our findings are broadly applicable for other environments where large-scale SOC stock assessments and uncertainty are needed. Journal Article Geoderma 428 116188 Elsevier BV 0016-7061 Soil organic carbon, Salt marshes, Geostatistics, Upscaling, Soil coring, Pedometric mapping 15 12 2022 2022-12-15 10.1016/j.geoderma.2022.116188 http://dx.doi.org/10.1016/j.geoderma.2022.116188 COLLEGE NANME Geography COLLEGE CODE SGE Swansea University Another institution paid the OA fee 2023-10-16T14:09:07.9727326 2023-09-08T11:44:05.4050320 Faculty of Science and Engineering School of Biosciences, Geography and Physics - Geography Cai Ladd 0000-0001-5437-6474 1 Craig Smeaton 0000-0003-4535-2555 2 Martin W. Skov 0000-0002-7204-3865 3 William E.N. Austin 4 64484__28712__2c3a7a7b11ea4235aad0ba83bd24c931.pdf 64484.VOR.pdf 2023-10-05T11:03:47.8573931 Output 14607296 application/pdf Version of Record true Distributed under the terms of a Creative Commons Attribution 4.0 License (CC BY 4.0). true eng https://creativecommons.org/licenses/by/4.0/
title Best practice for upscaling soil organic carbon stocks in salt marshes
spellingShingle Best practice for upscaling soil organic carbon stocks in salt marshes
Cai Ladd
title_short Best practice for upscaling soil organic carbon stocks in salt marshes
title_full Best practice for upscaling soil organic carbon stocks in salt marshes
title_fullStr Best practice for upscaling soil organic carbon stocks in salt marshes
title_full_unstemmed Best practice for upscaling soil organic carbon stocks in salt marshes
title_sort Best practice for upscaling soil organic carbon stocks in salt marshes
author_id_str_mv 134c870190db4c365e2ccc2d6c107462
author_id_fullname_str_mv 134c870190db4c365e2ccc2d6c107462_***_Cai Ladd
author Cai Ladd
author2 Cai Ladd
Craig Smeaton
Martin W. Skov
William E.N. Austin
format Journal article
container_title Geoderma
container_volume 428
container_start_page 116188
publishDate 2022
institution Swansea University
issn 0016-7061
doi_str_mv 10.1016/j.geoderma.2022.116188
publisher Elsevier BV
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Biosciences, Geography and Physics - Geography{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Biosciences, Geography and Physics - Geography
url http://dx.doi.org/10.1016/j.geoderma.2022.116188
document_store_str 1
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description Calculating the amount of soil organic carbon (SOC) stored in coastal environments, including salt marshes, is needed to determine their role in mitigating the Climate Crisis. Several techniques exist to calculate the SOC content of a unit of land from the upscaling of soil cores. However, no comprehensive assessment has been made on the performance of commonly used SOC upscaling techniques until now. We measured the SOC content of soil cores gathered from two Scottish salt marshes. Two SOC values were used for upscaling; SOC content for a 1 m standardised depth (as recommended by the IPCC), and SOC content of the modern marsh deposit (identified in the stratigraphy as a transition from organic-rich (marsh) to mineral-rich (intertidal flat) soil. Twenty-two upscaling techniques were used (SOC content × area, interpolative, and regression-based extrapolative calculations). Leave-one-out cross-validation procedures and prediction interval widths were used to assess the accuracy of each technique. Digital Terrain Models and Normalized Difference Vegetation Indices were used as covariate surfaces in the regression models. We found that marsh-scale SOC stocks varied by as much as fifty-two times depending on which sampling depth and upscaling technique was used. The largest differences emerged when comparing SOC stocks upscaled from 1 m deep and modern marsh deposits. Using the IPCC recommended 1 m sampling depth inflated the SOC stocks of salt marshes, as intertidal flat environments were included in the calculation. Ensemble regression models from the weighted average of seven machine learning algorithm outputs produced the highest upscaling accuracies across marshes and sampling depths. Simple SOC content × area calculations produced marsh-scale SOC stocks that were comparable to stock values produced by more advanced ensemble regression models. However, regression models produced detailed maps of SOC distribution across a marsh, and the associated uncertainty in the SOC values. Our findings are broadly applicable for other environments where large-scale SOC stock assessments and uncertainty are needed.
published_date 2022-12-15T14:09:09Z
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