Articles | Volume 15, issue 1
https://doi.org/10.5194/gmd-15-173-2022
https://doi.org/10.5194/gmd-15-173-2022
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

Related authors

Evaluating the performance of CE-QUAL-W2 version 4.5 sediment diagenesis model
Manuel Almeida and Pedro Coelho
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-202,https://doi.org/10.5194/gmd-2024-202, 2025
Preprint under review for GMD
Short summary
Modeling river water temperature with limiting forcing data: Air2stream v1.0.0, machine learning and multiple regression
Manuel C. Almeida and Pedro S. Coelho
Geosci. Model Dev., 16, 4083–4112, https://doi.org/10.5194/gmd-16-4083-2023,https://doi.org/10.5194/gmd-16-4083-2023, 2023
Short summary

Related subject area

Climate and Earth system modeling
TemDeep: a self-supervised framework for temporal downscaling of atmospheric fields at arbitrary time resolutions
Liwen Wang, Qian Li, Qi Lv, Xuan Peng, and Wei You
Geosci. Model Dev., 18, 2427–2442, https://doi.org/10.5194/gmd-18-2427-2025,https://doi.org/10.5194/gmd-18-2427-2025, 2025
Short summary
The ensemble consistency test: from CESM to MPAS and beyond
Teo Price-Broncucia, Allison Baker, Dorit Hammerling, Michael Duda, and Rebecca Morrison
Geosci. Model Dev., 18, 2349–2372, https://doi.org/10.5194/gmd-18-2349-2025,https://doi.org/10.5194/gmd-18-2349-2025, 2025
Short summary
Presentation, calibration and testing of the DCESS II Earth system model of intermediate complexity (version 1.0)
Esteban Fernández Villanueva and Gary Shaffer
Geosci. Model Dev., 18, 2161–2192, https://doi.org/10.5194/gmd-18-2161-2025,https://doi.org/10.5194/gmd-18-2161-2025, 2025
Short summary
Synthesizing global carbon–nitrogen coupling effects – the MAGICC coupled carbon–nitrogen cycle model v1.0
Gang Tang, Zebedee Nicholls, Alexander Norton, Sönke Zaehle, and Malte Meinshausen
Geosci. Model Dev., 18, 2193–2230, https://doi.org/10.5194/gmd-18-2193-2025,https://doi.org/10.5194/gmd-18-2193-2025, 2025
Short summary
Historical trends and controlling factors of isoprene emissions in CMIP6 Earth system models
Ngoc Thi Nhu Do, Kengo Sudo, Akihiko Ito, Louisa K. Emmons, Vaishali Naik, Kostas Tsigaridis, Øyvind Seland, Gerd A. Folberth, and Douglas I. Kelley
Geosci. Model Dev., 18, 2079–2109, https://doi.org/10.5194/gmd-18-2079-2025,https://doi.org/10.5194/gmd-18-2079-2025, 2025
Short summary

Cited articles

Almeida, M.: 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), Zenodo [code], https://doi.org/10.5281/zenodo.4803480, 2021a. 
Almeida, M.: 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), Zenodo [data set], https://doi.org/10.5281/zenodo.4756312, 2021b. 
Almeida, M. C., Coelho, P. S., Rodrigues, A. C., Diogo, P. A., Maurício, R., Cardoso, R. M., and Soares, P. M. M.: Thermal stratification of Portuguese reservoirs: Potential impact of extreme climate scenarios, J. Water Clim. Change, 6, 544–560, https://doi.org/10.2166/wcc.2015.071, 2015. 
Bates, G. T., Giorgi, F., and Hostetler, S. W.: Towards the simulation of the effects of the Great Lakes on climate, Mon. Weather Rev., 121, 1373–1387, https://doi.org/10.1175/1520-0493(1993)121<1373:TTSOTE>2.0.CO;2, 1993. 
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.
Share