Navigating lake modelling and calibration: insights from literature and community practice

Calibrating process-based lake models can be challenging, especially for newcomers. Existing literature is extensive but often assumes substantial statistical expertise and is scattered across models and disciplines, making it less accessible to new practitioners. This paper provides an overview of the modelling workflow with emphasis on calibration, which is the most resource-intensive task. Rather than offering a one-size-fits-all recipe, we present approaches that help cope with common challenges, including limited direct measurements, conflicting objectives, and risks of overfitting. Drawing on literature, survey responses, and our own experience, we chart five preparatory steps: clarifying model aims, assessing data suitability, configuring the model, preparing for calibration, and performing sensitivity analysis. We discuss manual versus automatic calibration, emphasising the value of mixed, iterative stepwise strategies that incorporate prior knowledge to constrain parameters to realistic values. Validation is considered as an assessment of whether the model adequately serves its intended purpose. By outlining these steps and considerations, we aim to reduce effort, improve results, and advance lake model development and application.