Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 15, issue 1
Articles | Volume 15, issue 1
https://doi.org/10.5194/gmd-15-173-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/gmd-15-173-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 15, issue 1
Model experiment description paper
 | 
11 Jan 2022
Model experiment description paper |  | 11 Jan 2022

Modeling reservoir surface temperatures for regional and global climate models: a multi-model study on the inflow and level variation effects

Manuel C. Almeida, Yurii Shevchuk, Georgiy Kirillin, Pedro M. M. Soares, Rita M. Cardoso, José P. Matos, Ricardo M. Rebelo, António C. Rodrigues, and Pedro S. Coelho

Data sets

Model input files (hydrometric, water quality and meteorological data sets): CE-QUAL-W2 v3.6, FLake (windows version), Hostetler and ANN (momentum alg.) -- Modeling reservoir surface temperatures for regional and global climate models (Version 1.0) Manuel Almeida https://doi.org/10.5281/zenodo.4756312

Model code and software

Models source code: CE-QUAL-W2 v3.6, FLake (windows version~1.0), Hostetler and ANN (momentum alg.) -- Modeling reservoir surface temperatures for regional and global climate models (Version 1.0) Manuel Almeida https://doi.org/10.5281/zenodo.4803480

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Short summary
In this study, we have evaluated the importance of the input of energy conveyed by river inflows into lakes and reservoirs when modeling surface water energy fluxes. Our results suggest that there is a strong correlation between water residence time and the surface water temperature prediction error and that the combined use of process-based physical models and machine-learning models will considerably improve the modeling of air–lake heat and moisture fluxes.
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Special issue
Modelling inland waters in a changing climate (GMD/ESD/TC...