Articles | Volume 28, issue 4
https://doi.org/10.5194/hess-28-851-2024
https://doi.org/10.5194/hess-28-851-2024
Research article
 | 
23 Feb 2024
Research article |  | 23 Feb 2024

Flow intermittence prediction using a hybrid hydrological modelling approach: influence of observed intermittence data on the training of a random forest model

Louise Mimeau, Annika Künne, Flora Branger, Sven Kralisch, Alexandre Devers, and Jean-Philippe Vidal

Viewed

Total article views: 1,193 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
927 217 49 1,193 71 40 35
  • HTML: 927
  • PDF: 217
  • XML: 49
  • Total: 1,193
  • Supplement: 71
  • BibTeX: 40
  • EndNote: 35
Views and downloads (calculated since 28 Jul 2023)
Cumulative views and downloads (calculated since 28 Jul 2023)

Viewed (geographical distribution)

Total article views: 1,193 (including HTML, PDF, and XML) Thereof 1,155 with geography defined and 38 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 29 Jun 2024
Download
Short summary
Modelling flow intermittence is essential for predicting the future evolution of drying in river networks and better understanding the ecological and socio-economic impacts. However, modelling flow intermittence is challenging, and observed data on temporary rivers are scarce. This study presents a new modelling approach for predicting flow intermittence in river networks and shows that combining different sources of observed data reduces the model uncertainty.