Proper characterization of uncertainty remains a major research and operational challenge in Environmental Sciences and is inherent to many aspects of modelling impacting model structure development; parameter estimation; an adequate representation of the data (inputs data and data used to evaluate the models); initial and boundary conditions; and hypothesis testing. To address this challenge, methods that have proved to be very helpful include a) uncertainty analysis (UA) that seeks to identify, quantify and reduce the different sources of uncertainty, as well as propagating them through a system/model, and b) the closely-related methods for sensitivity analysis (SA) that evaluate the role and significance of uncertain factors (in the functioning of systems/models).
This session invites contributions that discuss advances, both in theory and/or application, in Bayesian methods and methods for SA/UA applicable to all Earth and Environmental Systems Models (EESMs), which embraces all areas of hydrology, such as classical hydrology, subsurface hydrology and soil science.
Topics of interest include (but are not limited to):
1) Novel methods for effective characterization of sensitivity and uncertainty including robust quantification of predictive uncertainty for model surrogates and machine learning (ML) models
2) Analyses of over-parameterised models enabled by AI/ML techniques
3) Approaches to define meaningful priors for ML techniques in hydro(geo)logy
4) Novel methods for spatial and temporal evaluation/analysis of models
5) The role of information and error on SA/UA (e.g., input/output data error, model structure error, parametric error, regionalization error in environments with no data, etc.)
6) The role of SA in evaluating model consistency and reliability
7) Novel approaches and benchmarking efforts for parameter estimation
8) Improving the computational efficiency of SA/UA (efficient sampling, surrogate modelling, parallel computing, model pre-emption, model ensembles, etc.)
9) Methods for detecting and characterizing model inadequacy
Speakers
- Giuseppe Brunetti, University of Calabria, Italy
- Nazanin Mohammadi, the University of Neuchatel, Switzerland
- Tim Jupe, University of Stuttgart, Germany
- Ankita Pradhan, University of Wisoncin-Madison, United States of America
- Saket Pande, Delft University of Technology, Netherlands
- Patricio Yeste, University of Potsdam, Germany
- Steven Weijs, University of British Columbia, Canada
- Thorsten Wagener, University of Potsdam, Germany
- Don Rajitha Malshan Athukorala, The University of Sydney, Australia
- Rajani Pandey, Indian Institute of Science, Bengaluru, India
- Omar Cenobio-Cruz, Uppsala University, Sweden
- Bastian Waldowski, Leibniz University Hannover, Germany
- Stanley Scott, Heidelberg University, Germany
- Max Gustav Rudolph, TUD Dresden University of Technology, Germany
- Konstantin Drach, Eberhard Karls Universität Tübingen, Germany
- Janek Geiger, University of Tuebingen, Germany
- Martijn van Leer, Utrecht University, Netherlands
- Ioulia Koroptsenko, Technical University of Crete, Greece