the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
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.
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CC1: 'Comment on egusphere-2024-2447', John Ding, 10 Aug 2024
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Figure 2. Distribution of the six evaluated performance metrics
In Figure 2, the histograms of the NSE (Nash-Sutcliffe efficiency) and R (Pearson correlation coefficient) highlight calibration results of four lake models on a daily step. The former is a variance-based, and the latter, a correlation-based metric. The simulated data counts cluster around a median value of 0.96 (an exact value not shown in the text) and of 0.98 (shown in Line 220), respectively, both at the upper end of their performance scale.
In a different context of rainfall-runoff modelling, for an observed hydrograph, Duc and Sawada (2023, Equation 25, Figure 2) show that the upper end/bound of the NSE is related to the correlation coefficient, R, as follows: NSEu=2-1/(RxR).
The median NSE and R values for simulated lake water temperatures appear to follow the equality.
Reference
Duc, L. and Sawada, Y.: A signal-processing-based interpretation of the Nash–Sutcliffe efficiency, Hydrol. Earth Syst. Sci., 27, 1827–1839, https://doi.org/10.5194/hess-27-1827-2023, 2023.
Citation: https://doi.org/10.5194/egusphere-2024-2447-CC1
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
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