Articles | Volume 28, issue 3
https://doi.org/10.5194/hess-28-611-2024
© Author(s) 2024. 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-28-611-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
13 Feb 2024
Research article |
| 13 Feb 2024
| 13 Feb 2024
CAMELS-Chem: augmenting CAMELS (Catchment Attributes and Meteorology for Large-sample Studies) with atmospheric and stream water chemistry data
Gary Sterle
Department of Natural Resources and Environmental Science, University of Nevada, Reno, USA
Julia Perdrial
CORRESPONDING AUTHOR
Department of Geography and Geosciences, University of Vermont, Vermont, USA
GUND Institute of the Environment, University of Vermont, Vermont, USA
Dustin W. Kincaid
Department of Civil and Environmental Engineering, University of Vermont, Vermont, USA
GUND Institute of the Environment, University of Vermont, Vermont, USA
Kristen L. Underwood
Department of Civil and Environmental Engineering, University of Vermont, Vermont, USA
GUND Institute of the Environment, University of Vermont, Vermont, USA
Donna M. Rizzo
Department of Civil and Environmental Engineering, University of Vermont, Vermont, USA
GUND Institute of the Environment, University of Vermont, Vermont, USA
Ijaz Ul Haq
Department of Computer Science, University of Vermont, Vermont, USA
Department of Civil and Environmental Engineering, Pennsylvania State University, University Park, USA
Byung Suk Lee
Department of Computer Science, University of Vermont, Vermont, USA
Thomas Adler
Department of Geography and Geosciences, University of Vermont, Vermont, USA
GUND Institute of the Environment, University of Vermont, Vermont, USA
School of Earth System Science, Tianjin University, Tianjin, China
Helena Middleton
Department of Natural Resources and Environmental Science, University of Nevada, Reno, USA
Department of Natural Resources and Environmental Science, University of Nevada, Reno, USA
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Cited
7 citations as recorded by crossref.
- Multi-year time series of daily solute and isotope measurements from three Swiss pre-Alpine catchments J. Knapp et al. 10.1038/s41597-024-03192-5
- Machine‐Learning Reveals Equifinality in Drivers of Stream DOC Concentration at Continental Scales K. Underwood et al. 10.1029/2021WR030551
- MacroSheds: A synthesis of long‐term biogeochemical, hydroclimatic, and geospatial data from small watershed ecosystem studies M. Vlah et al. 10.1002/lol2.10325
- Soil CO2 Controls Short‐Term Variation but Climate Regulates Long‐Term Mean of Riverine Inorganic Carbon B. Stewart et al. 10.1029/2022GB007351
- Train, Inform, Borrow, or Combine? Approaches to Process‐Guided Deep Learning for Groundwater‐Influenced Stream Temperature Prediction J. Barclay et al. 10.1029/2023WR035327
- Leveraging Groundwater Dynamics to Improve Predictions of Summer Low‐Flow Discharges K. Johnson et al. 10.1029/2023WR035126
- CAMELS-CH: hydro-meteorological time series and landscape attributes for 331 catchments in hydrologic Switzerland M. Höge et al. 10.5194/essd-15-5755-2023
1 citations as recorded by crossref.
6 citations as recorded by crossref.
- Machine‐Learning Reveals Equifinality in Drivers of Stream DOC Concentration at Continental Scales K. Underwood et al. 10.1029/2021WR030551
- MacroSheds: A synthesis of long‐term biogeochemical, hydroclimatic, and geospatial data from small watershed ecosystem studies M. Vlah et al. 10.1002/lol2.10325
- Soil CO2 Controls Short‐Term Variation but Climate Regulates Long‐Term Mean of Riverine Inorganic Carbon B. Stewart et al. 10.1029/2022GB007351
- Train, Inform, Borrow, or Combine? Approaches to Process‐Guided Deep Learning for Groundwater‐Influenced Stream Temperature Prediction J. Barclay et al. 10.1029/2023WR035327
- Leveraging Groundwater Dynamics to Improve Predictions of Summer Low‐Flow Discharges K. Johnson et al. 10.1029/2023WR035126
- CAMELS-CH: hydro-meteorological time series and landscape attributes for 331 catchments in hydrologic Switzerland M. Höge et al. 10.5194/essd-15-5755-2023
Latest update: 11 May 2024
Short summary
We develop stream water chemistry to pair with the existing CAMELS (Catchment Attributes and Meteorology for Large-sample Studies) dataset. The newly developed dataset, termed CAMELS-Chem, includes common stream water chemistry constituents and wet deposition chemistry in 516 catchments. Examples show the value of CAMELS-Chem to trend and spatial analyses, as well as its limitations in sampling length and consistency.
We develop stream water chemistry to pair with the existing CAMELS (Catchment Attributes and...
