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.
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.
the Creative Commons Attribution 4.0 License.
Modeling reservoir surface temperatures for regional and global climate models: a multi-model study on the inflow and level variation effects
Faculdade de Ciências e Tecnologia, Universidade
Nova de Lisboa, Lisbon, 2825–516, Portugal
Yurii Shevchuk
MX Automotive GmbH, 13355 Berlin, Germany
Georgiy Kirillin
Department of Ecohydrology, Leibniz Institute of Freshwater Ecology
and Inland Fisheries (IGB), 12587 Berlin, Germany
Pedro M. M. Soares
Instituto Dom Luís (IDL), Faculdade de Ciências,
Universidade de Lisboa, Lisbon, 1749-016, Portugal
Rita M. Cardoso
Instituto Dom Luís (IDL), Faculdade de Ciências,
Universidade de Lisboa, Lisbon, 1749-016, Portugal
José P. Matos
Stucky SA, Rue du Lac 33, 1020 Renens, Switzerland
Ricardo M. Rebelo
Faculdade de Ciências e Tecnologia, Universidade
Nova de Lisboa, Lisbon, 2825–516, Portugal
António C. Rodrigues
Faculdade de Ciências e Tecnologia, Universidade
Nova de Lisboa, Lisbon, 2825–516, Portugal
Pedro S. Coelho
Faculdade de Ciências e Tecnologia, Universidade
Nova de Lisboa, Lisbon, 2825–516, Portugal
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Water temperature (WT) datasets of low-order rivers are scarce. In this study, five different models are used to predict the WT of 83 rivers. Generally, the results show that the models' hyperparameter optimization is essential and that to minimize the prediction error it is relevant to apply all the models considered in this study. Results also show that there is a logarithmic correlation among the error of the predicted river WT and the watershed time of concentration.
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This work focuses on the added value of high-resolution models relative to their forcing simulations, with an observational gridded dataset as a reference covering the Iberian Peninsula. The availability of such datasets with a spatial resolution close to that of regional models encouraged this study. For the max and min temperature, although most models reveal added value, some display losses. At more local scales, coastal sites display important gains, contrasting with the interior.
Giannis Sofiadis, Eleni Katragkou, Edouard L. Davin, Diana Rechid, Nathalie de Noblet-Ducoudre, Marcus Breil, Rita M. Cardoso, Peter Hoffmann, Lisa Jach, Ronny Meier, Priscilla A. Mooney, Pedro M. M. Soares, Susanna Strada, Merja H. Tölle, and Kirsten Warrach Sagi
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Afforestation is currently promoted as a greenhouse gas mitigation strategy. In our study, we examine the differences in soil temperature and moisture between grounds covered either by forests or grass. The main conclusion emerged is that forest-covered grounds are cooler but drier than open lands in summer. Therefore, afforestation disrupts the seasonal cycle of soil temperature, which in turn could trigger changes in crucial chemical processes such as soil carbon sequestration.
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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.
In this study, we have evaluated the importance of the input of energy conveyed by river inflows...