Articles | Volume 16, issue 14
https://doi.org/10.5194/gmd-16-4083-2023
https://doi.org/10.5194/gmd-16-4083-2023
Model evaluation paper
 | 
20 Jul 2023
Model evaluation paper |  | 20 Jul 2023

Modeling river water temperature with limiting forcing data: Air2stream v1.0.0, machine learning and multiple regression

Manuel C. Almeida 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
Revised manuscript under review for GMD
Short summary
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
Geosci. Model Dev., 15, 173–197, https://doi.org/10.5194/gmd-15-173-2022,https://doi.org/10.5194/gmd-15-173-2022, 2022
Short summary

Related subject area

Climate and Earth system modeling
Advanced climate model evaluation with ESMValTool v2.11.0 using parallel, out-of-core, and distributed computing
Manuel Schlund, Bouwe Andela, Jörg Benke, Ruth Comer, Birgit Hassler, Emma Hogan, Peter Kalverla, Axel Lauer, Bill Little, Saskia Loosveldt Tomas, Francesco Nattino, Patrick Peglar, Valeriu Predoi, Stef Smeets, Stephen Worsley, Martin Yeo, and Klaus Zimmermann
Geosci. Model Dev., 18, 4009–4021, https://doi.org/10.5194/gmd-18-4009-2025,https://doi.org/10.5194/gmd-18-4009-2025, 2025
Short summary
ICON-HAM-lite 1.0: simulating the Earth system with interactive aerosols at kilometer scales
Philipp Weiss, Ross Herbert, and Philip Stier
Geosci. Model Dev., 18, 3877–3894, https://doi.org/10.5194/gmd-18-3877-2025,https://doi.org/10.5194/gmd-18-3877-2025, 2025
Short summary
Process-based modeling framework for sustainable irrigation management at the regional scale: integrating rice production, water use, and greenhouse gas emissions
Yan Bo, Hao Liang, Tao Li, and Feng Zhou
Geosci. Model Dev., 18, 3799–3817, https://doi.org/10.5194/gmd-18-3799-2025,https://doi.org/10.5194/gmd-18-3799-2025, 2025
Short summary
Implementing deep soil and dynamic root uptake in Noah-MP (v4.5): impact on Amazon dry-season transpiration
Carolina A. Bieri, Francina Dominguez, Gonzalo Miguez-Macho, and Ying Fan
Geosci. Model Dev., 18, 3755–3779, https://doi.org/10.5194/gmd-18-3755-2025,https://doi.org/10.5194/gmd-18-3755-2025, 2025
Short summary
Reducing time and computing costs in EC-Earth: an automatic load-balancing approach for coupled Earth system models
Sergi Palomas, Mario C. Acosta, Gladys Utrera, and Etienne Tourigny
Geosci. Model Dev., 18, 3661–3679, https://doi.org/10.5194/gmd-18-3661-2025,https://doi.org/10.5194/gmd-18-3661-2025, 2025
Short summary

Cited articles

Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I. J., Harp, A., Irving, G., Isard, M., Jia, Y., Józefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mane, D., Monga, R., Moore, S., Murray, D. G., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., 620 Tucker, P. A., Vanhoucke, V., Vasudevan, V., Viégas, F. B., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., and Zheng, X.: TensorFlow: Large-scale machine learning on heterogeneous distributed systems, in: Proceedings of the 12th USENIX conference on Operating Systems Design and Implementation, Savannah, GA, USA, 2–4 November 2016, 265–283, https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/45166.pdf (last access: 17 July 2023), 2016. 
Agrawal, A. and Petersen, M. R.: Detecting Arsenic Contamination Using Satellite Imagery and Machine Learning, Toxics, 9, 333, https://doi.org/10.3390/toxics9120333, 2021. 
Ahmadi-Nedushan, B., St-Hilaire, A., Ouarda, T. B. M. J., Bilodeau, L., Robichaud, É., Thiémonge, N., and Bobée, B.: Predicting river water temperatures using stochastic models: case study of the Moisie River (Québec, Canada), Hydrol. Process., 21, 21–34, https://doi.org/10.1002/hyp.6353, 2007. 
Almeida, M. C. and Coelho, P. S.: mcvta/WaterPythonTemp: Release 0.2.0, Zenodo [code and data set], https://doi.org/10.5281/zenodo.7870379, 2023. 
Araújo, C. S. P., Silva, I. A. C., Ippolito, M., and Almeida, C. D.: Evaluation of air temperature estimated by ERA5-Land reanalysis using surface data in Pernambuco, Brazil. Environ. Monit. Assess., 194, 381, https://doi.org/10.1007/s10661-022-10047-2, 2022. 
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.
Share