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

Viewed

Total article views: 4,452 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
3,185 1,133 134 4,452 140 210
  • HTML: 3,185
  • PDF: 1,133
  • XML: 134
  • Total: 4,452
  • BibTeX: 140
  • EndNote: 210
Views and downloads (calculated since 21 Aug 2023)
Cumulative views and downloads (calculated since 21 Aug 2023)

Viewed (geographical distribution)

Total article views: 4,452 (including HTML, PDF, and XML) Thereof 4,353 with geography defined and 99 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 06 May 2026
Download
Short summary
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.
Read more
Share