Hydrology and Earth System Sciences
Hydrology and Earth System Sciences
HESS
Articles | Volume 26, issue 21
Articles | Volume 26, issue 21
https://doi.org/10.5194/hess-26-5431-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-26-5431-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 26, issue 21
Research article
 | 
01 Nov 2022
Research article |  | 01 Nov 2022

Karst spring recession and classification: efficient, automated methods for both fast- and slow-flow components

Tunde Olarinoye, Tom Gleeson, and Andreas Hartmann

Viewed

Total article views: 3,498 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
2,244 1,163 91 3,498 90 125
  • HTML: 2,244
  • PDF: 1,163
  • XML: 91
  • Total: 3,498
  • BibTeX: 90
  • EndNote: 125
Views and downloads (calculated since 18 May 2021)
Cumulative views and downloads (calculated since 18 May 2021)

Viewed (geographical distribution)

Total article views: 3,498 (including HTML, PDF, and XML) Thereof 3,294 with geography defined and 204 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 27 Nov 2025
Download
Short summary
Analysis of karst spring recession is essential for management of groundwater. In karst, recession is dominated by slow and fast components; separating these components is by manual and subjective approaches. In our study, we tested the applicability of automated streamflow recession extraction procedures for a karst spring. Results showed that, by simple modification, streamflow extraction methods can identify slow and fast components: derived recession parameters are within reasonable ranges.
Read more
Share