Articles | Volume 16, issue 15
https://doi.org/10.5194/gmd-16-4481-2023
https://doi.org/10.5194/gmd-16-4481-2023
Model description paper
 | 
08 Aug 2023
Model description paper |  | 08 Aug 2023

DynQual v1.0: a high-resolution global surface water quality model

Edward R. Jones, Marc F. P. Bierkens, Niko Wanders, Edwin H. Sutanudjaja, Ludovicus P. H. van Beek, and Michelle T. H. van Vliet

Related authors

Country-level and gridded estimates of wastewater production, collection, treatment and reuse
Edward R. Jones, Michelle T. H. van Vliet, Manzoor Qadir, and Marc F. P. Bierkens
Earth Syst. Sci. Data, 13, 237–254, https://doi.org/10.5194/essd-13-237-2021,https://doi.org/10.5194/essd-13-237-2021, 2021
Short summary

Related subject area

Hydrology
Generalised drought index: a novel multi-scale daily approach for drought assessment
João António Martins Careto, Rita Margarida Cardoso, Ana Russo, Daniela Catarina André Lima, and Pedro Miguel Matos Soares
Geosci. Model Dev., 17, 8115–8139, https://doi.org/10.5194/gmd-17-8115-2024,https://doi.org/10.5194/gmd-17-8115-2024, 2024
Short summary
Development and performance of a high-resolution surface wave and storm surge forecast model: application to a large lake
Laura L. Swatridge, Ryan P. Mulligan, Leon Boegman, and Shiliang Shan
Geosci. Model Dev., 17, 7751–7766, https://doi.org/10.5194/gmd-17-7751-2024,https://doi.org/10.5194/gmd-17-7751-2024, 2024
Short summary
Deep dive into hydrologic simulations at global scale: harnessing the power of deep learning and physics-informed differentiable models (δHBV-globe1.0-hydroDL)
Dapeng Feng, Hylke Beck, Jens de Bruijn, Reetik Kumar Sahu, Yusuke Satoh, Yoshihide Wada, Jiangtao Liu, Ming Pan, Kathryn Lawson, and Chaopeng Shen
Geosci. Model Dev., 17, 7181–7198, https://doi.org/10.5194/gmd-17-7181-2024,https://doi.org/10.5194/gmd-17-7181-2024, 2024
Short summary
PyEt v1.3.1: a Python package for the estimation of potential evapotranspiration
Matevž Vremec, Raoul A. Collenteur, and Steffen Birk
Geosci. Model Dev., 17, 7083–7103, https://doi.org/10.5194/gmd-17-7083-2024,https://doi.org/10.5194/gmd-17-7083-2024, 2024
Short summary
Prediction of hysteretic matric potential dynamics using artificial intelligence: application of autoencoder neural networks
Nedal Aqel, Lea Reusser, Stephan Margreth, Andrea Carminati, and Peter Lehmann
Geosci. Model Dev., 17, 6949–6966, https://doi.org/10.5194/gmd-17-6949-2024,https://doi.org/10.5194/gmd-17-6949-2024, 2024
Short summary

Cited articles

Batjes, N. H.: ISRIC-WISE global data set of derived soil properties on a 0.5 by 0.5 degree grid (Version 3.0), World Soil Information, Wageningen, 24, d9eca770-29a4-4d95-bf93-f32e1ab419c3, 2005. 
Beusen, A. H. W., Dekkers, A. L. M., Bouwman, A. F., Ludwig, W., and Harrison, J.: Estimation of global river transport of sediments and associated particulate C, N, and P, Global Biogeochem. Cy., 19, GB4S05, https://doi.org/10.1029/2005gb002453, 2005. 
Beusen, A. H. W., Van Beek, L. P. H., Bouwman, A. F., Mogollón, J. M., and Middelburg, J. J.: Coupling global models for hydrology and nutrient loading to simulate nitrogen and phosphorus retention in surface water – description of IMAGE–GNM and analysis of performance, Geosci. Model Dev., 8, 4045–4067, https://doi.org/10.5194/gmd-8-4045-2015, 2015. 
Chapra, S. C., Pelletier, G. J., and Tao, H.: QUAL2K: A Modeling Framework for Simulating River and Stream Water Quality, Version 2.11: Documentation and Users Manual, Civil and Environmental Engineering Dept., Tufts University, Medford, MA, 2008. 
Cucchi, M., Weedon, G. P., Amici, A., Bellouin, N., Lange, S., Müller Schmied, H., Hersbach, H., and Buontempo, C.: WFDE5: bias-adjusted ERA5 reanalysis data for impact studies, Earth Syst. Sci. Data, 12, 2097–2120, https://doi.org/10.5194/essd-12-2097-2020, 2020. 
Download
Short summary
DynQual is a new high-resolution global water quality model for simulating total dissolved solids, biological oxygen demand and fecal coliform as indicators of salinity, organic pollution and pathogen pollution, respectively. Output data from DynQual can supplement the observational record of water quality data, which is highly fragmented across space and time, and has the potential to inform assessments in a broad range of fields including ecological, human health and water scarcity studies.