Transformation rate maps of dissolved organic carbon in the contiguous US
<p>Riverine dissolved organic carbon (DOC) plays a vital role in regional and global carbon cycles. However, the processes of DOC conversion from soil organic carbon (SOC) and leaching into rivers are insufficiently understood, inconsistently represented, and poorly parameterized, particularly...
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Copernicus Publications
2025-06-01
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| Series: | Earth System Science Data |
| Online Access: | https://essd.copernicus.org/articles/17/2713/2025/essd-17-2713-2025.pdf |
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| author | L. Li H.-Y. Li G. Abeshu J. Tang L. R. Leung C. Liao Z. Tan H. Tian P. Thornton X. Yang |
| author_facet | L. Li H.-Y. Li G. Abeshu J. Tang L. R. Leung C. Liao Z. Tan H. Tian P. Thornton X. Yang |
| author_sort | L. Li |
| collection | DOAJ |
| description | <p>Riverine dissolved organic carbon (DOC) plays a vital role in regional and global carbon cycles. However, the processes of DOC conversion from soil organic carbon (SOC) and leaching into rivers are insufficiently understood, inconsistently represented, and poorly parameterized, particularly in land surface and Earth system models. As a first attempt to fill this gap, we propose a generic formula that directly connects SOC concentration with DOC concentration in headwater streams, where a single parameter, the transformation rate from SOC in the soil to DOC leaching flux (<span class="inline-formula"><i>P</i><sub>r</sub></span>), accounts for the overall processes governing SOC conversion to DOC and leaching from soils (along with runoff) into headwater streams. We then derive high-resolution <span class="inline-formula"><i>P</i><sub>r</sub></span> maps over the contiguous US (CONUS) using SOC data from two different sources: the Harmonized World Soil Database v1.2 (HWSD) and SoilGrids 2.0. Both maps are developed following the same five major steps: (1) selecting independent catchments where observed riverine DOC data are available with reasonable quality; (2) estimating catchment-average SOC for the independent catchments; (3) estimating the <span class="inline-formula"><i>P</i><sub>r</sub></span> values for these catchments based on the generic formula and catchment-average SOC; (4) developing a predictive model of <span class="inline-formula"><i>P</i><sub>r</sub></span> with machine learning (ML) techniques and catchment-scale climate, hydrology, geology, and other attributes; and (5) deriving a national map of <span class="inline-formula"><i>P</i><sub>r</sub></span> based on the ML model. For evaluation, we compare the DOC concentration derived using the <span class="inline-formula"><i>P</i><sub>r</sub></span> map and the observed DOC concentration values at evaluation catchments. The resulting mean absolute scaled error and coefficient of determination are 0.73 and 0.47 for the HWSD-based model and 0.58 and 0.72 for the SoilGrids-based model, respectively, suggesting the effectiveness of the overall methodology. Efforts to constrain uncertainty and evaluate sensitivity of <span class="inline-formula"><i>P</i><sub>r</sub></span> to different factors are discussed. To illustrate the use of such maps, we derive a riverine DOC concentration reanalysis dataset over CONUS. The two <span class="inline-formula"><i>P</i><sub>r</sub></span> maps, robustly derived and empirically validated, lay a critical cornerstone for better simulating the terrestrial carbon cycle in land surface and Earth system models. Our findings not only set a foundation for improving our predictive understanding of the terrestrial carbon cycle at the regional and global scales, but also hold promises for informing policy decisions related to decarbonization and climate change mitigation. The data presented in this study are publicly available at <a href="https://doi.org/10.5281/zenodo.14563816">https://doi.org/10.5281/zenodo.14563816</a> (Li et al., 2024).</p> |
| format | Article |
| id | doaj-art-efb0e04e942d42fc99b21cc6459dc13b |
| institution | DOAJ |
| issn | 1866-3508 1866-3516 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Earth System Science Data |
| spelling | doaj-art-efb0e04e942d42fc99b21cc6459dc13b2025-08-20T03:21:35ZengCopernicus PublicationsEarth System Science Data1866-35081866-35162025-06-01172713273310.5194/essd-17-2713-2025Transformation rate maps of dissolved organic carbon in the contiguous USL. Li0H.-Y. Li1G. Abeshu2J. Tang3L. R. Leung4C. Liao5Z. Tan6H. Tian7P. Thornton8X. Yang9Department of Civil and Environmental Engineering, University of Houston, Houston, Texas, USADepartment of Civil and Environmental Engineering, University of Houston, Houston, Texas, USAPacific Northwest National Laboratory, Richland, Washington, USALawrence Berkeley National Laboratory, Berkeley, California, USAPacific Northwest National Laboratory, Richland, Washington, USAPacific Northwest National Laboratory, Richland, Washington, USAPacific Northwest National Laboratory, Richland, Washington, USADepartment of Earth and Environmental Sciences, Boston College, Massachusetts, USAEnvironmental Sciences Division, and Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, Tennessee, USAEnvironmental Sciences Division, and Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA<p>Riverine dissolved organic carbon (DOC) plays a vital role in regional and global carbon cycles. However, the processes of DOC conversion from soil organic carbon (SOC) and leaching into rivers are insufficiently understood, inconsistently represented, and poorly parameterized, particularly in land surface and Earth system models. As a first attempt to fill this gap, we propose a generic formula that directly connects SOC concentration with DOC concentration in headwater streams, where a single parameter, the transformation rate from SOC in the soil to DOC leaching flux (<span class="inline-formula"><i>P</i><sub>r</sub></span>), accounts for the overall processes governing SOC conversion to DOC and leaching from soils (along with runoff) into headwater streams. We then derive high-resolution <span class="inline-formula"><i>P</i><sub>r</sub></span> maps over the contiguous US (CONUS) using SOC data from two different sources: the Harmonized World Soil Database v1.2 (HWSD) and SoilGrids 2.0. Both maps are developed following the same five major steps: (1) selecting independent catchments where observed riverine DOC data are available with reasonable quality; (2) estimating catchment-average SOC for the independent catchments; (3) estimating the <span class="inline-formula"><i>P</i><sub>r</sub></span> values for these catchments based on the generic formula and catchment-average SOC; (4) developing a predictive model of <span class="inline-formula"><i>P</i><sub>r</sub></span> with machine learning (ML) techniques and catchment-scale climate, hydrology, geology, and other attributes; and (5) deriving a national map of <span class="inline-formula"><i>P</i><sub>r</sub></span> based on the ML model. For evaluation, we compare the DOC concentration derived using the <span class="inline-formula"><i>P</i><sub>r</sub></span> map and the observed DOC concentration values at evaluation catchments. The resulting mean absolute scaled error and coefficient of determination are 0.73 and 0.47 for the HWSD-based model and 0.58 and 0.72 for the SoilGrids-based model, respectively, suggesting the effectiveness of the overall methodology. Efforts to constrain uncertainty and evaluate sensitivity of <span class="inline-formula"><i>P</i><sub>r</sub></span> to different factors are discussed. To illustrate the use of such maps, we derive a riverine DOC concentration reanalysis dataset over CONUS. The two <span class="inline-formula"><i>P</i><sub>r</sub></span> maps, robustly derived and empirically validated, lay a critical cornerstone for better simulating the terrestrial carbon cycle in land surface and Earth system models. Our findings not only set a foundation for improving our predictive understanding of the terrestrial carbon cycle at the regional and global scales, but also hold promises for informing policy decisions related to decarbonization and climate change mitigation. The data presented in this study are publicly available at <a href="https://doi.org/10.5281/zenodo.14563816">https://doi.org/10.5281/zenodo.14563816</a> (Li et al., 2024).</p>https://essd.copernicus.org/articles/17/2713/2025/essd-17-2713-2025.pdf |
| spellingShingle | L. Li H.-Y. Li G. Abeshu J. Tang L. R. Leung C. Liao Z. Tan H. Tian P. Thornton X. Yang Transformation rate maps of dissolved organic carbon in the contiguous US Earth System Science Data |
| title | Transformation rate maps of dissolved organic carbon in the contiguous US |
| title_full | Transformation rate maps of dissolved organic carbon in the contiguous US |
| title_fullStr | Transformation rate maps of dissolved organic carbon in the contiguous US |
| title_full_unstemmed | Transformation rate maps of dissolved organic carbon in the contiguous US |
| title_short | Transformation rate maps of dissolved organic carbon in the contiguous US |
| title_sort | transformation rate maps of dissolved organic carbon in the contiguous us |
| url | https://essd.copernicus.org/articles/17/2713/2025/essd-17-2713-2025.pdf |
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