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

Olarinoye, Tunde; Gleeson, Tom; Hartmann, Andreas

doi:https://doi.org/10.5194/hess-26-5431-2022

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

Data sets

Global karst springs hydrograph dataset for research and management of the world’s fastest-flowing groundwater T. Olarinoye, T. Gleeson, V. Marx, S. Seeger, R. Adinehvand, A. Hartmann, et al. https://doi.org/10.6084/m9.figshare.9638939.v2

Model code and software

KarstHub/Karst-recession GitHub https://github.com/KarstHub/Karst-recession

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.