Detection of hidden model errors by combining single and multi-criteria calibration

Highlights: • A novel method to combine benefits of single and multi-criteria calibration • New method results in five posterior probability distribution which can reveal so far hidden errors in environmental models • Universal to detect process specific sources of uncertainty • Providing guidance for model improvement and selection of further validation data Environmental models aim to reproduce landscape processes with mathematical equations. Observations are used for validation. The performance and uncertainties are quantified either by single or multi-criteria model assessment. In a case-study, we combine both approaches. We use a coupled hydro-biogeochemistry landscape-scale model to simulate 14 target values on discharge, stream nitrate as well as soil moisture, soil temperature and trace gas emissions (N₂O, CO₂) from different land uses. We reveal typical mistakes that happen during both, single and multi-criteria model assessment. Such as overestimated uncertainty in multi-criteria and ignored wrong model processes in single-criterion calibration. These mistakes can mislead the development of water quality and in general all environmental models. Only the combination of both approaches reveals the five types of posterior probability distributions for model parameters. Each type allocates a specific type of error. We identify and locate mismatched parameter values, obsolete parameters, flawed model structures and wrong process representations. The presented method can guide model users and developers to the so far hidden errors in their models. We emphasize to include observations from physical, chemical, biological and ecological processes in the model assessment, rather than the typical discipline specific assessments.