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
14 citations as recorded by crossref.
- Swiss data quality: augmenting CAMELS-CH with isotopes, water quality, agricultural and atmospheric data T. do Nascimento et al. https://doi.org/10.1038/s41597-025-05625-1
- Diving into AI? Exploring the Potential for AI to Tackle Complex Water Quality Challenges E. Borgomeo et al. https://doi.org/10.1021/acs.est.5c15991
- Embracing large language model (LLM) technologies in hydrology research Z. Ma et al. https://doi.org/10.1088/3033-4942/addd43
- CAMELS-FI: hydrometeorological time series and landscape properties for 320 catchments in Finland I. Seppä et al. https://doi.org/10.5194/essd-18-4745-2026
- Evaluating a process‐guided deep learning approach for predicting dissolved oxygen in streams J. Sadler et al. https://doi.org/10.1002/hyp.15270
- From RNNs to Transformers: benchmarking deep learning architectures for hydrologic prediction J. Liu et al. https://doi.org/10.5194/hess-29-6811-2025
- Solute export patterns across the contiguous USA D. Kincaid et al. https://doi.org/10.1002/hyp.15197
- Multi-year time series of daily solute and isotope measurements from three Swiss pre-Alpine catchments J. Knapp et al. https://doi.org/10.1038/s41597-024-03192-5
- A Comprehensive Water Chemistry Dataset for Iranian Rivers E. Zarei et al. https://doi.org/10.1038/s41597-025-05932-7
- Caravan-Qual: A global scale integration of stream water quality observations into a large-sample hydrology dataset E. Jones et al. https://doi.org/10.1038/s41597-026-07352-7
- QUADICA v2: extending the large-sample data set for water QUAlity, DIscharge and Catchment Attributes in Germany P. Ebeling et al. https://doi.org/10.5194/essd-18-691-2026
- The importance of source data in river network connectivity modeling: A review C. Brinkerhoff https://doi.org/10.1002/lno.12706
- Spatially resolved rainfall streamflow modeling in central Europe M. Vischer et al. https://doi.org/10.5194/hess-29-5233-2025
- Drivers of spatiotemporal variability of river water quality L. Schauer et al. https://doi.org/10.1088/1748-9326/ae692c
14 citations as recorded by crossref.
- Swiss data quality: augmenting CAMELS-CH with isotopes, water quality, agricultural and atmospheric data T. do Nascimento et al. https://doi.org/10.1038/s41597-025-05625-1
- Diving into AI? Exploring the Potential for AI to Tackle Complex Water Quality Challenges E. Borgomeo et al. https://doi.org/10.1021/acs.est.5c15991
- Embracing large language model (LLM) technologies in hydrology research Z. Ma et al. https://doi.org/10.1088/3033-4942/addd43
- CAMELS-FI: hydrometeorological time series and landscape properties for 320 catchments in Finland I. Seppä et al. https://doi.org/10.5194/essd-18-4745-2026
- Evaluating a process‐guided deep learning approach for predicting dissolved oxygen in streams J. Sadler et al. https://doi.org/10.1002/hyp.15270
- From RNNs to Transformers: benchmarking deep learning architectures for hydrologic prediction J. Liu et al. https://doi.org/10.5194/hess-29-6811-2025
- Solute export patterns across the contiguous USA D. Kincaid et al. https://doi.org/10.1002/hyp.15197
- Multi-year time series of daily solute and isotope measurements from three Swiss pre-Alpine catchments J. Knapp et al. https://doi.org/10.1038/s41597-024-03192-5
- A Comprehensive Water Chemistry Dataset for Iranian Rivers E. Zarei et al. https://doi.org/10.1038/s41597-025-05932-7
- Caravan-Qual: A global scale integration of stream water quality observations into a large-sample hydrology dataset E. Jones et al. https://doi.org/10.1038/s41597-026-07352-7
- QUADICA v2: extending the large-sample data set for water QUAlity, DIscharge and Catchment Attributes in Germany P. Ebeling et al. https://doi.org/10.5194/essd-18-691-2026
- The importance of source data in river network connectivity modeling: A review C. Brinkerhoff https://doi.org/10.1002/lno.12706
- Spatially resolved rainfall streamflow modeling in central Europe M. Vischer et al. https://doi.org/10.5194/hess-29-5233-2025
- Drivers of spatiotemporal variability of river water quality L. Schauer et al. https://doi.org/10.1088/1748-9326/ae692c
Saved (final revised paper)
Latest update: 21 Jul 2026
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...
