HS2.3.8 on ‘The application of Bayesian approaches in water quality modelling, decision support and risk analysis’

Dear colleagues,

I’d just like to share a quick reminder that the abstract submission for EGU 2022 Session ‘The application of Bayesian approaches in water quality modelling, decision support and risk analysis’ is due on 12 Jan 2022.

Please find more details on the session below.

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HS2.3.8 on ‘The application of Bayesian approaches in water quality modelling, decision support and risk analysis’  is scheduled at the EGU General Assembly on 3–8 April 2022. The session, with some modifications, has been running for four years now, The varied contributions have been appreciated by a growing community of Bayesian modellers, with some being published in a HESS Special Issue this year.

We are contacting you as we believe this session would be of interest to your research area and would like to invite you to submit an abstract. Next year we are planning to run the session in a vPICO format that will allow both in-person and remote participation.

Electronic submission system and full description of the session is available at https://meetingorganizer.copernicus.org/EGU22/session/42996

The deadline for abstract submission is 12 January 2022.

Session Description

Bayesian approaches have become increasingly popular in water quality modelling, thanks to their ability to handle uncertainty comprehensively. This is particularly relevant in environmental decision making where Bayesian inference enables to consider the reliability of predictions of the consequences of decision alternatives, alongside uncertainties related to decision makers’ risk attitudes and preferences, uncertainty related to system understanding and random processes. Graphical Bayesian Belief Networks and related approaches (hierarchical models, ‘hybrid’ mechanistic/data-driven models) can be particularly powerful decision support tools that make it relatively easy for stakeholders to engage in the model building process and inform adaptive water quality management within an uncertainty framework. The aim of this session is to review the state-of-the-art in this field and compare software and procedural choices to consolidate and set new directions for the emerging community of Bayesian water quality modellers. Building on past three years’ success of this session, a specific new emphasize for this year’s session is to explore the utility of Bayesian water quality models in supporting decision making.

We seek contributions from water quality research that use Bayesian approaches to, for example but not exclusively:

• involve stakeholders in model development and maximise the use of expert knowledge

• integrate prior knowledge, especially problematizing the choice of Bayesian priors

• inform risk analysis and decision support using diverse data and evidence

• represent the preferences of the stakeholders in the form of value functions through elicitation, and account for the uncertainty in preferences

• produce accessible decision support tools

• model water quality in data sparse environments

• compare models with different levels of complexity and process representation

• quantify the uncertainty of model predictions (due to data, model structure and parameter uncertainty)

• address the problem of scaling (e.g. disparity of scales between processes, observations, model resolution and predictions) through hierarchical models

• quantify especially model structural error through, for example, Bayesian Model Averaging or structural error terms

• use statistical emulators to allow probabilistic predictions of complex modelled systems

• use machine-learning and data mining approaches to learn from large, possibly high-resolution data sets.

We hope that you will be able to join us in 2022 either in Vienna or online.

 

With best regards,

Session chairs: Miriam Glendell, Ibrahim Alameddine, Danlu Guo, James Sample, Ambuj Sriwastawa