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

Feldbauer, Johannes; Mesman, Jorrit P.; Andersen, Tobias K.; Ladwig, Robert

doi:https://doi.org/10.5194/hess-29-1183-2025

Articles | Volume 29, issue 4

https://doi.org/10.5194/hess-29-1183-2025

© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/hess-29-1183-2025

© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 29, issue 4

Research article

|

03 Mar 2025

Research article |

| 03 Mar 2025

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

Johannes Feldbauer, Jorrit P. Mesman, Tobias K. Andersen, and Robert Ladwig

Supplement

https://doi.org/10.5194/hess-29-1183-2025-supplement

Data sets

Data analysis and plots Johannes Feldbauer and Jorrit P. Mesman https://zenodo.org/doi/10.5281/zenodo.13150422

Set up and run calibration Jorrit P. Mesman and Johannes Feldbauer https://zenodo.org/doi/10.5281/zenodo.13165427

ISIMIP3b bias-adjusted atmospheric climate input data Stefan Lange and Matthias Büchner https://doi.org/10.48364/ISIMIP.842396

Model code and software

ISIMIP_Local_Lakes Daniel Mercado-Bettín https://github.com/icra/ISIMIP_Local_Lakes

aemon-j/LakeEnsemblR: LakeEnsemblR v1.0.0 Tadhg Moore et al. https://doi.org/10.5281/zenodo.4146899

Short summary

Models help to understand natural systems and are used to predict changes based on scenarios (e.g., climate change). To simulate water temperature and deduce impacts on water quality in lakes, 1D lake models are often used. There are several such models that differ regarding their assumptions and mathematical process description. This study examines the performance of four such models on a global dataset of 73 lakes and relates their performance to the model structure and lake characteristics.