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

TY - JOUR

T1 - Learning from a large-scale calibration effort of multiple lake temperature models

AU - Feldbauer, Johannes

AU - Mesman, Jorrit P.

AU - Andersen, Tobias K.

AU - Ladwig, Robert

N1 - Publisher Copyright: © 2025 Johannes Feldbauer et al.

PY - 2025/3

Y1 - 2025/3

N2 - 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.

AB - 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.

UR - https://www.scopus.com/pages/publications/85219752475

U2 - 10.5194/hess-29-1183-2025

DO - 10.5194/hess-29-1183-2025

M3 - Journal article

AN - SCOPUS:85219752475

SN - 1027-5606

VL - 29

SP - 1183

EP - 1199

JO - Hydrology and Earth System Sciences

JF - Hydrology and Earth System Sciences

IS - 4

ER -