Learning from a large-scale calibration effort of multiple lake models

Abstract. Process-based hydrodynamic lake models are commonly used for simulating water temperature, enabling testing of different scenarios and drawing conclusions about possible water quality developments or changes in important ecological processes such as methane gas emissions. Even though there are several models available, a systematic comparison regarding their performance is missing so far. In this study, we calibrated four different one-dimensional hydrodynamic lake models for a global dataset of 73 lakes to compare their performance in reproducing water temperature and estimated parameter sensitivity for the calibrated parameters. The models performance and parameter sensitivity showed a relation to the lake characteristics and model structure. No single model was the best, with each model performing better than the rest in at least some of the lakes. From the findings, we advocate the application of model ensembles. Nonetheless, we also highlight the need to further improve both weather forcing data, individual models, and multi-model ensemble techniques.