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
Advances in Model Inference, Diagnostics, Sensitivity, Uncertainty Quantification and Bayesian Approaches in Environmental Systems Models
Convener:
Thomas Wöhling
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Co-conveners:
Juliane Mai,
Anneli GuthkeECSECS,
Cristina PrietoECSECS,
Wolfgang Nowak,
Uwe Ehret