Preprints
https://doi.org/10.5194/egusphere-2022-406
https://doi.org/10.5194/egusphere-2022-406
 
19 Jul 2022
19 Jul 2022
Status: this preprint is open for discussion.

Multi-objective calibration of the Community Land Model Version 5.0 using in-situ observations of water and energy fluxes and variables

Tanja Denager1, Torben O. Sonnenborg2, Majken C. Looms1, Heye Bogena3, and Karsten H. Jensen1 Tanja Denager et al.
  • 1Department of Geosciences and Natural Resource Management, University of Copenhagen, Øster Voldgade 10, 1350 Copenhagen, Denmark
  • 2Geological Survey of Denmark and Greenland, Øster Voldgade 10, 1350 Copenhagen, Denmark
  • 3Agrosphere Institute, IBG-3, Forschungszentrum Jülich GmbH, 52428 Jülich, Germany

Abstract. This study evaluate water and energy fluxes and variables in combination with parameter optimization of the state-of-the-art land surface model Community Land Model version 5 (CLM5), using six years of hourly observations of latent heat flux, sensible heat flux, groundwater recharge and soil moisture.

The results show that multi-objective calibration in combination with truncated singular value decomposition and Tikhonov regularization is a powerful method to improve the current practice of using look-up tables to define parameter values in land surface models. Furthermore, reliability of the optimized model parameters can be estimated by statistical measures such as identifiability and relative error variance reduction. As in most other eddy covariance studies, closure of the land surface energy balance is not achieved on observation data. However, using direct measurement of turbulent fluxes as target variable, the parameter optimization is capable of matching simulations and observations of latent heat, especially during the summer period, while simulated sensible heat is clearly biased. The fact that CLM5 is not capable of matching sensible heat, not even with advanced parameter optimization of model parameter values, suggests that the lack of energy closure is due to biases in the sensible heat flux. The results from this study contribute to improvements in model characterization of water and energy fluxes. It is underlined that parameter calibration using available observations of hydrologic and energy fluxes and variables is necessary to obtain the optimal parameter set of a land surface model.

Tanja Denager et al.

Status: open (until 13 Sep 2022)

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Tanja Denager et al.

Tanja Denager et al.

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Short summary
This study contribute to improvements in model characterization of water and energy fluxes. The results show that multi-objective auto-calibration in combination with mathematical regularization is powerful to improve land surface models. Land surface energy balance closure is not achieved on observation data. Using direct measurement of turbulent fluxes as target variable, the parameter optimization is matching simulations and observations of latent heat, while sensible heat is clearly biased.