Comparing different approaches for modeling reactive transport on aquifer-scale – which level of complexity is legitimate?

For reactive transport of solutes on aquifer-scale, measurements are usually costly and time-consuming and therefore observation data is scarce. Consequently, the system is often not fully understood and modelers cannot be sure which processes are relevant on the considered spatial and temporal scale. This lack of system understanding leads to so-called conceptual uncertainty, which is the uncertainty in choosing between competing hypotheses for a model formulation.

To account for conceptual uncertainty, modelers should work with several model alternatives that differ in their system representation. In the case of aerobic respiration and denitrification in a heterogeneous aquifer, several modeling concepts have been proposed. The approaches used in this study range from 2D spatially explicit to streamline-based models and vary considerably in their underlying assumptions and their computational costs. Typically, models that are more complex require more measurement data to constrain their parameters. Therefore, model complexity and the effort for acquiring field data have to be balanced.

In this study, we apply a concept called Bayesian model legitimacy analysis to assess which level of model complexity is justifiable given a certain amount of realistically available measurement data. This analysis reveals which number of measurements in a specific experimental setup is needed to justify a certain level of model complexity. Our results indicate that the complexity of the reference model (spatially explicit, dispersion and growth/decay of biomass included) is justifiable even by the smallest amount of synthetic measured data.