No Single Lake Temperature Model Works Best, Study Finds
Researchers calibrated four leading lake temperature models across 73 global lakes and found each excels in different conditions—meaning no single tool can reliably predict water temperatures or forecast ecological changes like greenhouse gas emissions. The finding pushes scientists toward using combined models rather than betting on one.
Originaltitel: Learning from a large-scale calibration effort of multiple lake temperature models
<p>Process-based lake temperature models, formulated on hydrodynamic principles, are commonly used to simulate water temperature, enabling one to test different scenarios and draw conclusions about possible water quality developments or changes in important ecological processes such as greenhouse gas emissions. Even though there are several models available, a systematic comparison regarding their performance is currently missing. In this study, we calibrated four different one-dimensional (1D) lake temperature models for a global dataset of 73 lakes to compare their performance with respect to reproducing water temperature, and we estimated parameter sensitivity for the calibrated parameters. The parameter values, model performance, and parameter sensitivity differed between lake models and between clusters that were defined based on lake characteristics. No single model performed best, with each model performing better than the others 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 weather forcing data, individual models, and multi-model ensemble techniques.</p>