Articles | Volume 16, issue 14
https://doi.org/10.5194/gmd-16-4083-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-16-4083-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Modeling river water temperature with limiting forcing data: Air2stream v1.0.0, machine learning and multiple regression
MARE – Marine and Environmental Sciences Centre, ARNET – Aquatic
Research Network Associate Laboratory, NOVA School of Science and
Technology, NOVA University Lisbon, Caparica, Portugal
Pedro S. Coelho
MARE – Marine and Environmental Sciences Centre, ARNET – Aquatic
Research Network Associate Laboratory, NOVA School of Science and
Technology, NOVA University Lisbon, Caparica, Portugal
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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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Short summary
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.
Water temperature (WT) datasets of low-order rivers are scarce. In this study, five different...