Point-scale multi-objective calibration of the Community Land Model (version 5.0) using in situ observations of water and energy fluxes and variables
<p>This study evaluates water and energy fluxes and variables in combination with parameter optimization of version 5 of the state-of-the-art Community Land Model (CLM5) land surface model, using 6 years of hourly observations of latent heat flux, sensible heat flux, groundwater recharge, soil...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2023-07-01
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| Series: | Hydrology and Earth System Sciences |
| Online Access: | https://hess.copernicus.org/articles/27/2827/2023/hess-27-2827-2023.pdf |
| Summary: | <p>This study evaluates water and energy fluxes and variables in combination with parameter optimization of version 5 of the state-of-the-art Community Land Model (CLM5) land surface model, using 6 years of hourly
observations of latent heat flux, sensible heat flux, groundwater recharge,
soil moisture and soil temperature from an agricultural observatory in
Denmark. 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 lookup tables to define parameter values in land surface models. Using measurements of
turbulent fluxes as the target variable, parameter optimization is capable
of matching simulations and observations of latent heat, especially during
the summer period, whereas simulated sensible heat is clearly biased. Of the
30 parameters considered, the soil texture, monthly leaf area index (LAI) in summer, stomatal
conductance and root distribution have the highest influence on the
local-scale simulation results. The results from this study contribute to
improvements of the model characterization of water and energy fluxes. This work highlights the importance of performing parameter calibration using
observations of hydrologic and energy fluxes and variables to obtain the optimal parameter values for a land surface model.</p> |
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| ISSN: | 1027-5606 1607-7938 |