Articles | Volume 25, issue 6
https://doi.org/10.5194/hess-25-3397-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-25-3397-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
17 Jun 2021
Research article |
| 17 Jun 2021
| 17 Jun 2021
Machine learning deciphers CO2 sequestration and subsurface flowpaths from stream chemistry
Department of Geosciences, Pennsylvania State University, University Park, PA, USA
Earth and Environmental Systems Institute, Pennsylvania State University, University Park, PA, USA
Earth and Environmental Systems Institute, Pennsylvania State University, University Park, PA, USA
Department of Earth and Environmental Sciences, Syracuse University, Syracuse, NY, USA
Susan L. Brantley
Department of Geosciences, Pennsylvania State University, University Park, PA, USA
Earth and Environmental Systems Institute, Pennsylvania State University, University Park, PA, USA
Viewed
Total article views: 2,915 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 27 Oct 2020)
| HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 2,135 | 730 | 50 | 2,915 | 356 | 41 | 50 |
- HTML: 2,135
- PDF: 730
- XML: 50
- Total: 2,915
- Supplement: 356
- BibTeX: 41
- EndNote: 50
Total article views: 2,115 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 17 Jun 2021)
| HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 1,591 | 484 | 40 | 2,115 | 213 | 36 | 42 |
- HTML: 1,591
- PDF: 484
- XML: 40
- Total: 2,115
- Supplement: 213
- BibTeX: 36
- EndNote: 42
Total article views: 800 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 27 Oct 2020)
| HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 544 | 246 | 10 | 800 | 143 | 5 | 8 |
- HTML: 544
- PDF: 246
- XML: 10
- Total: 800
- Supplement: 143
- BibTeX: 5
- EndNote: 8
Viewed (geographical distribution)
Total article views: 2,915 (including HTML, PDF, and XML)
Thereof 2,430 with geography defined
and 485 with unknown origin.
Total article views: 2,115 (including HTML, PDF, and XML)
Thereof 1,826 with geography defined
and 289 with unknown origin.
Total article views: 800 (including HTML, PDF, and XML)
Thereof 604 with geography defined
and 196 with unknown origin.
| Country | # | Views | % |
|---|
| Country | # | Views | % |
|---|
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
Cited
15 citations as recorded by crossref.
- Can machine learning accelerate process understanding and decision‐relevant predictions of river water quality? C. Varadharajan et al. 10.1002/hyp.14565
- Mathematical and Machine Learning Models for Groundwater Level Changes: A Systematic Review and Bibliographic Analysis S. Afrifa et al. 10.3390/fi14090259
- Regional Drivers of Stream Chemical Behavior: Leveraging Lithology, Land Use, and Climate Gradients Across the Colorado River, Texas USA G. Goldrich‐Middaugh et al. 10.1029/2022WR032155
- The Potential of Machine Learning for Enhancing CO2 Sequestration, Storage, Transportation, and Utilization-based Processes: A Brief Perspective S. Gupta & L. Li 10.1007/s11837-021-05079-x
- How temperature-dependent silicate weathering acts as Earth’s geological thermostat S. Brantley et al. 10.1126/science.add2922
- Linking Stream Chemistry to Subsurface Redox Architecture A. Shaughnessy et al. 10.1029/2022WR033445
- Applying Machine Learning to investigate metal isotope variations at the watershed scale: A case study with lithium isotopes across the Yukon River Basin S. Cotroneo et al. 10.1016/j.scitotenv.2023.165165
- Carbonates in the Critical Zone M. Covington et al. 10.1029/2022EF002765
- Predicting Rare Earth Element Potential in Produced and Geothermal Waters of the United States via Emergent Self-Organizing Maps M. Engle et al. 10.3390/en15134555
- A review of machine learning in geochemistry and cosmochemistry: Method improvements and applications Y. He et al. 10.1016/j.apgeochem.2022.105273
- Road salting and natural brine migration revealed as major sources of groundwater contamination across regions of northern Appalachia with and without unconventional oil and gas development F. Epuna et al. 10.1016/j.watres.2022.119128
- How do silicate weathering rates in shales respond to climate and erosion? A. Shaughnessy & S. Brantley 10.1016/j.chemgeo.2023.121474
- Geochemical Evidence of Potential Groundwater Contamination with Human Health Risks Where Hydraulic Fracturing Overlaps with Extensive Legacy Hydrocarbon Extraction S. Shaheen et al. 10.1021/acs.est.2c00001
- Applications of machine learning in computational nanotechnology W. Liu et al. 10.1088/1361-6528/ac46d7
- Low rates of rock organic carbon oxidation and anthropogenic cycling of rhenium in a slowly denuding landscape M. Ogrič et al. 10.1002/esp.5543
14 citations as recorded by crossref.
- Can machine learning accelerate process understanding and decision‐relevant predictions of river water quality? C. Varadharajan et al. 10.1002/hyp.14565
- Mathematical and Machine Learning Models for Groundwater Level Changes: A Systematic Review and Bibliographic Analysis S. Afrifa et al. 10.3390/fi14090259
- Regional Drivers of Stream Chemical Behavior: Leveraging Lithology, Land Use, and Climate Gradients Across the Colorado River, Texas USA G. Goldrich‐Middaugh et al. 10.1029/2022WR032155
- The Potential of Machine Learning for Enhancing CO2 Sequestration, Storage, Transportation, and Utilization-based Processes: A Brief Perspective S. Gupta & L. Li 10.1007/s11837-021-05079-x
- How temperature-dependent silicate weathering acts as Earth’s geological thermostat S. Brantley et al. 10.1126/science.add2922
- Linking Stream Chemistry to Subsurface Redox Architecture A. Shaughnessy et al. 10.1029/2022WR033445
- Applying Machine Learning to investigate metal isotope variations at the watershed scale: A case study with lithium isotopes across the Yukon River Basin S. Cotroneo et al. 10.1016/j.scitotenv.2023.165165
- Carbonates in the Critical Zone M. Covington et al. 10.1029/2022EF002765
- Predicting Rare Earth Element Potential in Produced and Geothermal Waters of the United States via Emergent Self-Organizing Maps M. Engle et al. 10.3390/en15134555
- A review of machine learning in geochemistry and cosmochemistry: Method improvements and applications Y. He et al. 10.1016/j.apgeochem.2022.105273
- Road salting and natural brine migration revealed as major sources of groundwater contamination across regions of northern Appalachia with and without unconventional oil and gas development F. Epuna et al. 10.1016/j.watres.2022.119128
- How do silicate weathering rates in shales respond to climate and erosion? A. Shaughnessy & S. Brantley 10.1016/j.chemgeo.2023.121474
- Geochemical Evidence of Potential Groundwater Contamination with Human Health Risks Where Hydraulic Fracturing Overlaps with Extensive Legacy Hydrocarbon Extraction S. Shaheen et al. 10.1021/acs.est.2c00001
- Applications of machine learning in computational nanotechnology W. Liu et al. 10.1088/1361-6528/ac46d7
1 citations as recorded by crossref.
Latest update: 29 Jun 2024
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.
It is often difficult to determine the sources of solutes in streams and how much each source...
