Process-based models are able to predict velocity fields, sediment transport and associated morphodynamic developments over time. These models can generate realistic morphological patterns and stable morphodynamic developments over time scales of millennia under schematized model settings. However, more realistic case studies raise questions on model skill and confidence levels. Process-based models require detailed information on initial conditions (e.g. sediment characteristics, initial distribution of sediment fractions over the model domain), process descriptions (e.g. roughness and sediment transport formulations) and forcing conditions (e.g. time varying hydrodynamic and sediment forcing). The value of the model output depends to a high degree on the uncertainty associated with these model input parameters.
Our study explores a methodology to quantify model output uncertainty levels and to determine which parameters are responsible for largest output uncertainty. Furthermore we explore how model skill and uncertainty develop over time. We describe the San Pablo Bay (USA) case study and the Western Scheldt (Netherlands) case study in a 100 year hindcast and a more than 100 year forecast.
Remarkably, model skill and uncertainty levels depend on model input parameter variations only to a limited extent. Model skill is low first decades, but increases afterwards to become excellent after 70 years. The possible explanation is that the interaction of the major tidal forcing and the estuarine plan form governs morphodynamic development in confined environments to a high degree.