PICOs

HS2.3.5 | PICO

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. The aim of this session is to review the state-of-the-art in this field and compare software and procedural choices in order to consolidate and set new directions for the emerging community of Bayesian water quality modellers.

In particular, we seek contributions from water quality research that use 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 maximise the use of expert knowledge
• use machine-learning and data mining approaches to learn from large, possibly high-resolution data sets.

Keynote speaker:
Prof Peter Reichert: “The need for Bayesian approaches in water research and management.”
Eawag, Swiss Federal Institute of Aquatic Science and Technology; Department of Systems Analysis, Integrated Assessment and Modelling

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Convener: Miriam Glendell | Co-conveners: Tobias Krueger, James E. Sample
PICOs
| Wed, 10 Apr, 14:00–15:45
 
PICO spot 4

Wednesday, 10 April 2019

PICO spot 4
Chairperson: Miriam Glendell and James Sample
14:00–14:10 |
PICO4.1 |
EGU2019-2466
Peter Reichert
14:10–14:12 |
PICO4.2 |
EGU2019-6440
George Arhonditsis
14:12–14:14 |
PICO4.3 |
EGU2019-10662
Lorenz Ammann, Tobias Doppler, Peter Reichert, Christian Stamm, and Fabrizio Fenicia
14:14–14:16 |
PICO4.4 |
EGU2019-8778
Jeremy Piffady, Nadia Carluer, Veronique Gouy, Guy Le Henaff, Emilie Adoir, Claire Lauvernet, Thierry Tormos, and Nolwenn Bougon
14:16–14:18 |
PICO4.5 |
EGU2019-8426
Kirsty Forber, Donnacha Doody, Helen Jarvie, Shane Rothwell, Christopher Lyon, David Nash, and Paul Withers
14:18–14:20 |
PICO4.6 |
EGU2019-19134
Water Quality Prediction Using Climate Information within a Hierarchical Bayesian Modeling Framework
(withdrawn)
Min-Kyu Jung, Jong-Ho Ahn, Sumiya Uranchimeg, Ho-Jun Kim, and Hyun-Han Kwon
14:20–14:22 |
PICO4.7 |
EGU2019-964
Ana González-Nicolás Álvarez, Wolfgang Nowak, Michael Sinsbeck, and Marc Schwientek
14:22–14:24 |
PICO4.8 |
EGU2019-16266
Jonathan Gair, Ina Pohle, James Sample, and Miriam Glendell
14:24–14:26 |
PICO4.9 |
EGU2019-18926
Andy Vinten, Marc Stutter, and Luigi Spezia
14:26–14:28 |
PICO4.10 |
EGU2019-4186
Grace Rachid, Ibrahim Alameddine, and Mutasem El Fadel
14:28–14:30 |
PICO4.11 |
EGU2019-13785
Katri Rankinen, Maria Holmberg, and Jukka Aroviita
14:30–14:32 |
PICO4.12 |
EGU2019-17247
Jannicke Moe, Leah Jackson-Blake, Sigrid Haande, and Anne Lyche Solheim
14:32–15:45