The approach is a clean uncertainty analysis of several competing models. The competing models will differ vastly in complexity (e.g., distributed hydrological versus zonation/pde-based versus geostatistics/pde-based). The uncertainty analysis will reveal major sources of uncertainty, featuring both parametric uncertainty and model-conceptual uncertainty. Using Bayesian analysis tools in synthetic across-model numerical studies, we will identify the required level of data availability that marks the transition point in model legitimacy between the competing models. While the above analysis is based on reasonable field investigation scenarios mimicked from real catchments, a second analysis will perform a formal optimization of data collection schemes. This will reveal whether different (or even optimal) data collection schemes are helpful for installing the operational legitimacy and adequacy of more complex models at lower data availability.
The novelty of this approach is twofold:
- A Bayesian model analysis has never been applied to models of vastly different complexity and different model schools in the field of hydro(geo)logy.
- The application of optimal design of experiments in this field is new as well.
This is fundamental method-oriented research. To begin with, it is restricted to flow only (no transport) and the use of synthetic numerical studies.