Hydrology and Earth System Sciences
Hydrology and Earth System Sciences
HESS
Articles | Volume 28, issue 14
Articles | Volume 28, issue 14
https://doi.org/10.5194/hess-28-3367-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-28-3367-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 28, issue 14
Research article
 | 
29 Jul 2024
Research article |  | 29 Jul 2024

Regionalization of GR4J model parameters for river flow prediction in Paraná, Brazil

Louise Akemi Kuana, Arlan Scortegagna Almeida, Emílio Graciliano Ferreira Mercuri, and Steffen Manfred Noe

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Almagro, A., Oliveira, P. T. S., Neto, A. A. M., Roy, T., and Troch, P.: CABra: a novel large-sample dataset for Brazilian catchments, Hydrol. Earth Syst. Sci., 25, 3105–3135, https://doi.org/10.5194/hess-25-3105-2021, 2021. a
Arsenault, R., Breton-Dufour, M., Poulin, A., Dallaire, G., and Romero-Lopez, R.: Streamflow prediction in ungauged basins: analysis of regionalization methods in a hydrologically heterogeneous region of Mexico, Hydrolog. Sci. J., 64, 1297–1311, https://doi.org/10.1080/02626667.2019.1639716, 2019. a, b, c, d
Auler, A. and Farrant, A.: A brief introduction to karst and caves in Brazil, Proceedings of the University of Bristol Spelaeological Society, 20, 187–200, 1996. a, b
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The authors compared regionalization methods for river flow prediction in 126 catchments from the south of Brazil, a region with humid subtropical and hot temperate climate. The regionalization method based on physiographic–climatic similarity had the best performance for predicting daily and Q95 reference flow. We showed that basins without flow monitoring can have a good approximation of streamflow using machine learning and physiographic–climatic information as inputs.
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