The management of the environment and natural resources requires decisions that often rely on the results of numerical modelling. However, the use of hydro(geo)logical models to support these decisions can be confounded by the inherent uncertainty of model conceptualisation, simulation methods, and model parameters when predicting natural system behaviour. This uncertainty must be addressed within the modelling processes to ensure that model outputs can support robust and transparent decision making.

This session will provide an opportunity to present and openly discuss the practical challenges of predictive environmental modelling for decision support. We encourage practitioners to contribute case studies that demonstrate application of:
• Problem formulation/decomposition, including strategic model abstraction for prediction-focussed modelling design.
• Model Parameter estimation, history matching, and uncertainty analysis in predictive environmental modelling, including hydro(geo)logy and other disciplines.
• Management Optimization, including multi-objective optimization under uncertainty for finding optimal management strategies under risk.
• Decision support for communicating model results to decision makers through innovative tools, visualisations and metrics.
• Scripted workflows for repeatable, transparent predictions.