Environmental models are key simulation and prediction tools used in science and engineering. These complex models not only have uncertain inputs and structure, which propagate into uncertain model outputs, but also continually increase in computational demand with the pursuit of greater realism. Sensitivity and Uncertainty Analyses (SA/UA) are used to understand the implications of choices around model structure, identify important model parameters, and characterize uncertainties. This session invites contributions on both theory and application of model diagnostics, sensitivity and uncertainty analysis. We welcome submissions exploring the model-data interface, which plays an important role in uncertainty quantification. We invite applications to real-world problems such as studies focussing on water scarcity, contamination, and potential impacts of the COVID pandemic.

Particular topics of interest include:

• Strategies for more comprehensive and computationally efficient SA/UA
• Input data error quantification
• Parameterization alternatives: the dance of simple concepts and data fitting
• SA/UA improvements enabled by AI/ML techniques