Handling model complexity with parsimony: Numerical analysis of the nitrogen turnover in a controlled aquifer model setup

Highly parameterized numerical models are frequently used in environmental sciences to interpret physicochemical processes. Despite their popularity, unjustified model complexity often undermines model generalizability, resulting in poor model predictive capabilities. A trade-off between model performance and complexity can be achieved using model selection techniques, which are largely unexplored in biogeochemical modeling. Our study aims to address this scientific gap by investigating the main advantages and findings of model selection in a typical biogeochemical modeling application. In particular, the study focuses on the numerical description, at five different levels of complexity, of the reactive transport processes of nitrogen species in a controlled aquifer model laboratory setup. To this end, the mechanistic model HYDRUS is coupled with the multimodal Nested Sampling algorithm for the estimation of the Bayesian model evidence and posterior parameter distributions. After successfully calibrating the HYDRUS-2D model against measurements from a tracer test, a lower-fidelity surrogate-based analysis is carried out to tackle the problem of computational cost, as well as to compare five different models which combine first-order and Monod-based parameterization of the microbial nitrogen degradation. Results demonstrate that measured data contain enough information content to support an increase in the complexity of the Monod model, which can reproduce the transient accumulation of nitrite at the beginning of the experiment. The Bayesian analysis reveals that such behavior is related to the particular structure of measured data, which plays a fundamental role in the model selection. The analysis concludes with a validation of the surrogate-based analysis, which confirms the reliability of the proposed numerical approach, opening up new perspectives on the use of multi-fidelity surrogates for the Bayesian model selection in environmental modeling.