Articles | Volume 13, issue 12
https://doi.org/10.5194/essd-13-5483-2021
https://doi.org/10.5194/essd-13-5483-2021
Data description paper
 | 
30 Nov 2021
Data description paper |  | 30 Nov 2021

GRQA: Global River Water Quality Archive

Holger Virro, Giuseppe Amatulli, Alexander Kmoch, Longzhu Shen, and Evelyn Uuemaa

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Cited articles

Abbaspour, K. C., Rouholahnejad, E., Vaghefi, S., Srinivasan, R., Yang, H., and Kløve, B.: A continental-scale hydrology and water quality model for Europe: Calibration and uncertainty of a high-resolution large-scale SWAT model, J. Hydrol., 524, 733–752, https://doi.org/10.1016/j.jhydrol.2015.03.027, 2015. a
Addor, N., Newman, A. J., Mizukami, N., and Clark, M. P.: The CAMELS data set: catchment attributes and meteorology for large-sample studies, Hydrol. Earth Syst. Sci., 21, 5293–5313, https://doi.org/10.5194/hess-21-5293-2017, 2017. a
Beck, H. E., De Roo, A., and van Dijk, A. I.: Global maps of streamflow characteristics based on observations from several thousand catchments, J. Hydrometeorol., 16, 1478–1501, 2015. a
Berndt, D. J. and Clifford, J.: Using dynamic time warping to find patterns in time series, in: KDD workshop, Seattle, WA, USA, 26 April 1994, 10, 359–370, available at: https://www.aaai.org/Papers/Workshops/1994/WS-94-03/WS94-03-031.pdf (last access: 27 January 2021), 1994. a
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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.
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