Articles | Volume 25, issue 6
https://doi.org/10.5194/hess-25-3397-2021
https://doi.org/10.5194/hess-25-3397-2021
Research article
 | 
17 Jun 2021
Research article |  | 17 Jun 2021

Machine learning deciphers CO2 sequestration and subsurface flowpaths from stream chemistry

Andrew R. Shaughnessy, Xin Gu, Tao Wen, and Susan L. Brantley

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AR: Author's response | RR: Referee report | ED: Editor decision
ED: Publish subject to revisions (further review by editor and referees) (05 Mar 2021) by Monica Riva
AR by Andrew Shaughnessy on behalf of the Authors (16 Apr 2021)  Author's response   Author's tracked changes   Manuscript 
ED: Publish as is (07 May 2021) by Monica Riva
AR by Andrew Shaughnessy on behalf of the Authors (13 May 2021)  Manuscript 
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Short summary
It is often difficult to determine the sources of solutes in streams and how much each source contributes. We developed a new method of unmixing stream chemistry via machine learning. We found that sulfate in three watersheds is related to groundwater flowpaths. Our results emphasize that acid rain reduces a watershed's capacity to remove CO2 from the atmosphere, a key geological control on climate. Our method will help scientists unmix stream chemistry in watersheds where sources are unknown.