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

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

doi:10.5194/hess-29-1183-2025

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

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

^*Corresponding author for this work

Department of Ecoscience - Freshwater Ecology

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaper › Journal article › Research › peer-review

2 Citations (Scopus)

Abstract

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.

Original language	English
Journal	Hydrology and Earth System Sciences
Volume	29
Issue	4
Pages (from-to)	1183-1199
Number of pages	17
ISSN	1027-5606
DOIs	https://doi.org/10.5194/hess-29-1183-2025
Publication status	Published - Mar 2025

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title = "Learning from a large-scale calibration effort of multiple lake temperature models",

abstract = "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.",

author = "Johannes Feldbauer and Mesman, \{Jorrit P.\} and Andersen, \{Tobias K.\} and Robert Ladwig",

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year = "2025",

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doi = "10.5194/hess-29-1183-2025",

language = "English",

volume = "29",

pages = "1183--1199",

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Learning from a large-scale calibration effort of multiple lake temperature models. / Feldbauer, Johannes; Mesman, Jorrit P.; Andersen, Tobias K. et al.
In: Hydrology and Earth System Sciences, Vol. 29, No. 4, 03.2025, p. 1183-1199.

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaper › Journal article › Research › peer-review

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

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.

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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 -

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

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