Frontiers in the application of Bayesian approaches in water quality modelling
Frontiers in the application of Bayesian approaches in water quality modelling
Editor(s): Miriam Glendell, Ibrahim Alameddine, Lorenz Ammann, Fabrizio Fenicia, Ann van Griensven, and James E. Sample
Bayesian approaches have become increasingly popular in water quality modelling, thanks to their ability to handle uncertainty comprehensively (data, model structure and parameter uncertainty) and as flexible statistical and data mining tools. Furthermore, graphical Bayesian belief networks can be powerful decision support tools that make it relatively easy for stakeholders to engage in the model building process, thus building trust in the modelled outcomes. The aim of this special issue is to review the state-of-the-art in this field in order to consolidate and set new directions for the emerging community of Bayesian water quality modellers.

This special issue invites high-quality contributions from participants in the EGU 2019–2020 special sessions on this topic, as well as the wider scientific community. Relevant contributions may be related to the use of Bayesian approaches to, for example but not exclusively,

  • quantify the uncertainty of model predictions;
  • quantify especially model structural error through, for example, Bayesian model averaging or structural error terms;
  • address the problem of scaling (e.g. disparity of scales between processes, observations, model resolution and predictions) through hierarchical models;
  • model water quality in data-sparse environments;
  • compare models with different levels of complexity and process representation;
  • use statistical emulators to allow probabilistic predictions of complex modelled systems;
  • integrate prior knowledge, especially problematizing the choice of Bayesian priors;
  • produce user-friendly decision support tools using graphical Bayesian belief networks;
  • involve stakeholders in model development and maximize the use of expert knowledge; and
  • use machine-learning and data-mining approaches to learn from large, possibly high-resolution data sets.

Download citations of all papers

14 Jun 2023
Developing a Bayesian network model for understanding river catchment resilience under future change scenarios
Kerr J. Adams, Christopher A. J. Macleod, Marc J. Metzger, Nicola Melville, Rachel C. Helliwell, Jim Pritchard, and Miriam Glendell
Hydrol. Earth Syst. Sci., 27, 2205–2225,,, 2023
Short summary
20 Jun 2022
Seasonal forecasting of lake water quality and algal bloom risk using a continuous Gaussian Bayesian network
Leah A. Jackson-Blake, François Clayer, Sigrid Haande, James E. Sample, and S. Jannicke Moe
Hydrol. Earth Syst. Sci., 26, 3103–3124,,, 2022
Short summary
25 Apr 2022
Spatially referenced Bayesian state-space model of total phosphorus in western Lake Erie
Timothy J. Maguire, Craig A. Stow, and Casey M. Godwin
Hydrol. Earth Syst. Sci., 26, 1993–2017,,, 2022
Short summary
09 Mar 2022
Probabilistic modelling of the inherent field-level pesticide pollution risk in a small drinking water catchment using spatial Bayesian belief networks
Mads Troldborg, Zisis Gagkas, Andy Vinten, Allan Lilly, and Miriam Glendell
Hydrol. Earth Syst. Sci., 26, 1261–1293,,, 2022
Short summary
04 Mar 2022
Quantifying input uncertainty in the calibration of water quality models: reordering errors via the secant method
Xia Wu, Lucy Marshall, and Ashish Sharma
Hydrol. Earth Syst. Sci., 26, 1203–1221,,, 2022
Short summary
28 Feb 2022
Temporally resolved coastal hypoxia forecasting and uncertainty assessment via Bayesian mechanistic modeling
Alexey Katin, Dario Del Giudice, and Daniel R. Obenour
Hydrol. Earth Syst. Sci., 26, 1131–1143,,, 2022
Short summary
03 Jan 2022
Synthesizing the impacts of baseflow contribution on concentration–discharge (CQ) relationships across Australia using a Bayesian hierarchical model
Danlu Guo, Camille Minaudo, Anna Lintern, Ulrike Bende-Michl, Shuci Liu, Kefeng Zhang, and Clément Duvert
Hydrol. Earth Syst. Sci., 26, 1–16,,, 2022
Short summary
26 May 2021
Assessing interannual variability in nitrogen sourcing and retention through hybrid Bayesian watershed modeling
Jonathan W. Miller, Kimia Karimi, Arumugam Sankarasubramanian, and Daniel R. Obenour
Hydrol. Earth Syst. Sci., 25, 2789–2804,,, 2021
Short summary
20 May 2021
A Bayesian approach to understanding the key factors influencing temporal variability in stream water quality – a case study in the Great Barrier Reef catchments
Shuci Liu, Dongryeol Ryu, J. Angus Webb, Anna Lintern, Danlu Guo, David Waters, and Andrew W. Western
Hydrol. Earth Syst. Sci., 25, 2663–2683,,, 2021
Short summary
CC BY 4.0