Advances in Model Inference, Diagnostics, Sensitivity, Uncertainty Quantification and Bayesian Approaches in Environmental Systems Models

Convener: Thomas Wöhling | Co-conveners: Cristina PrietoECSECS, Jeremy Bennett, Anneli Guthke, Uwe Ehret, Cécile CoulonECSECS, Wolfgang Nowak

To properly characterize uncertainty remains a major research and operational challenge in the Environmental Sciences. Uncertainty is inherent to many aspects of modelling as it impacts model structure development; parameter estimation; appropriate representation of data (for model input, calibration and evaluation); initial and boundary conditions; and hypothesis testing. Addressing these uncertainties is particularly important for predictive models used to support water management and decision making.

To address this challenge, methods that have proved to be very helpful include a) sensitivity analysis (SA) that evaluate the role and significance of uncertain factors (in the functioning of systems/models) and b) the closely-related methods for uncertainty analyses (UA) that seek to identify, quantify and reduce the different sources of uncertainty, as well as propagating them through a system/model.

This session invites contributions that discuss advances, both in theory and/or application, in Bayesian or frequentist 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) Approaches to define meaningful priors for ML techniques in hydro(geo)logy,
3) Novel methods for spatial and temporal evaluation/analysis of models
4) The role of information and error on SA/UA as well as in evaluating model consistency and reliability
5) Novel approaches and benchmarking efforts for parameter estimation
6) Improving the computational efficiency of SA/UA (efficient sampling, surrogate modelling, parallel computing, model pre-emption, model ensembles, etc.)
7) Methods for detecting and characterizing model inadequacy
8) Problem formulation/decomposition and scripted workflows for prediction-focused modelling design
9) Cases studies on applied predictive modelling for decision support, management optimization under uncertainty and tools for communicating model results to stakeholders