TALKS: A systematic framework for resolving model-data discrepancies

Abstract

Models and data play an important role in informing decision-making in environmental systems, providing different and complementary information. Multiple frameworks have been developed to address model limitations and there is a large body of research focused on improving the quality of data. However, when models and data disagree the focus is usually on fixing the model, rather than the data. In this study, we introduce the framework TALKS (Trigger, Articulate, List, Knowledge elicitation, Solve) as a way of resolving model-data discrepancies. The framework emphasises that a mismatch between data and model outputs could be due to issues in the model, the data or both. Through three case studies, we exemplify how models can be used to identify and improve issues with the data, and hence make the most out of models and data. The framework can be applied more broadly to better integrate models and data in environmental decision making.