Development of a Hydrologic Analysis Framework for Improved Treatment of Uncertainty

In the last few decades hydrologists have made tremendous progress in using dynamic simulation models for the analysis and understanding of hydrologic systems. However, predictions with these models are often deterministic, and as such they focus on the most probable forecast, without an explicit estimate of the associated uncertainty. This uncertainty primarily arises from incomplete process representation, uncertainty in initial conditions, input, output, and parameter error. In this talk I will highlight recent progress in uncertainty assessment in hydrologic modeling. In particular, I will present a combined parameter and state estimation method for improved treatment of input, output, parameter and model structural error, and will demonstrate how to extend this method to include conceptual model uncertainty. The methods will be illustrated using two case studies: an operational flood forecasting model, and a groundwater solute transport model.