Articles | Volume 13, issue 12
https://doi.org/10.5194/essd-13-5483-2021
© 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/essd-13-5483-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
30 Nov 2021
Data description paper |
| 30 Nov 2021
| 30 Nov 2021
GRQA: Global River Water Quality Archive
Department of Geography, Institute of Ecology and Earth Sciences, University of Tartu, Vanemuise 46, Tartu, 51003, Estonia
Giuseppe Amatulli
School of the Environment, Yale University, New Haven, CT, 06511, USA
Center for Research Computing, Yale University, New Haven, CT, 06511, USA
Alexander Kmoch
Department of Geography, Institute of Ecology and Earth Sciences, University of Tartu, Vanemuise 46, Tartu, 51003, Estonia
Longzhu Shen
HyperAmp, Barnwell Road, Cambridge CB5 8RQ, UK
Spatial-Ecology, Meaderville House, Wheal Buller, Redruth TR16 6ST, UK
Evelyn Uuemaa
Department of Geography, Institute of Ecology and Earth Sciences, University of Tartu, Vanemuise 46, Tartu, 51003, Estonia
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22 citations as recorded by crossref.
- CAMELS-Chem: augmenting CAMELS (Catchment Attributes and Meteorology for Large-sample Studies) with atmospheric and stream water chemistry data G. Sterle et al. 10.5194/hess-28-611-2024
- QUADICA: water QUAlity, DIscharge and Catchment Attributes for large-sample studies in Germany P. Ebeling et al. 10.5194/essd-14-3715-2022
- Hydrography90m: a new high-resolution global hydrographic dataset G. Amatulli et al. 10.5194/essd-14-4525-2022
- Future directions for river carbon biogeochemistry observations J. Dean & T. Battin 10.1038/s44221-024-00207-8
- How does the choice of DEMs affect catchment hydrological modeling? D. Moges et al. 10.1016/j.scitotenv.2023.164627
- Random forest-based modeling of stream nutrients at national level in a data-scarce region H. Virro et al. 10.1016/j.scitotenv.2022.156613
- Overlooked riverine contributions of dissolved neodymium and hafnium to the Amazon estuary and oceans A. Xu et al. 10.1038/s41467-023-39922-3
- River water quality shaped by land–river connectivity in a changing climate L. Li et al. 10.1038/s41558-023-01923-x
- Economic risks hidden in local water pollution and global markets: A retrospective analysis (1995–2010) and future perspectives on sustainable development goal 6 J. Yang et al. 10.1016/j.watres.2024.121216
- Modeling hydraulic heads with impulse response functions in different environmental settings of the Baltic countries M. Jemeļjanova et al. 10.1016/j.ejrh.2023.101416
- River reach-level machine learning estimation of nutrient concentrations in Great Britain C. Tso et al. 10.3389/frwa.2023.1244024
- Disaster mitigation comics as a communication medium for youth regarding the impact of water pollution S. Mataram & R. Margaretha 10.1088/1755-1315/1314/1/012119
- Human and natural activities regulate organic matter transport in Chinese rivers D. Liu et al. 10.1016/j.watres.2023.120622
- Making China’s water data accessible, usable and shareable J. Lin et al. 10.1038/s44221-023-00039-y
- Examining the open-source datasets for water quantity and quality using the soil and water assessment tool (SWAT) İ. Peker & S. Gülbaz 10.2166/wst.2023.297
- An extensive spatiotemporal water quality dataset covering four decades (1980–2022) in China J. Lin et al. 10.5194/essd-16-1137-2024
- Mitigating nitrogen losses with almost no crop yield penalty during extremely wet years W. Liu et al. 10.1126/sciadv.adi9325
- Deep learning for water quality W. Zhi et al. 10.1038/s44221-024-00202-z
- Data limitations in developing countries make river restoration planning challenging. Study case of the Cesar River, Colombia S. Vega et al. 10.1016/j.ecohyd.2024.01.012
- Sharing FAIR monitoring program data improves discoverability and reuse J. Bayer et al. 10.1007/s10661-023-11788-4
- Fit-for-Purpose Information for Offshore Wind Farming Applications—Part-II: Gap Analysis and Recommendations J. Schulz-Stellenfleth et al. 10.3390/jmse11091817
- Determination of Optimal Water Intake Layer Using Deep Learning-Based Water Quality Monitoring and Prediction Y. Kim et al. 10.3390/w16010015
1 citations as recorded by crossref.
Latest update: 17 Apr 2024
Short summary
Water quality modeling is essential for understanding and mitigating water quality deterioration in river networks due to agricultural and industrial pollution. Improving the availability and usability of open data is vital to support global water quality modeling efforts. The GRQA extends the spatial and temporal coverage of previously available water quality data and provides a reproducible workflow for combining multi-source water quality datasets.
Water quality modeling is essential for understanding and mitigating water quality deterioration...
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