Articles | Volume 26, issue 21
https://doi.org/10.5194/hess-26-5431-2022
https://doi.org/10.5194/hess-26-5431-2022
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

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Revised manuscript accepted for HESS
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Cited articles

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Arciniega-Esparza, S., Breña-Naranjo, J. A., Pedrozo-Acuña, A., and Appendini, C. M.: HYDRORECESSION: A Matlab toolbox for streamflow recession analysis, Comput. Geosci., 98, 87–92, https://doi.org/10.1016/j.cageo.2016.10.005, 2017. 
Atkinson, T. C.: Diffuse flow and conduit flow in limestone terrain in the Mendip Hills, Somerset (Great Britain), J. Hydrol., 35, 93–110, https://doi.org/10.1016/0022-1694(77)90079-8, 1977. 
Beck, H. E., Zimmermann, N. E., Mcvicar, T. R., Vergopolan, N., Berg, A., and Wood, E. F.: Data Descriptor: Present and future Köppen-Geiger climate classification maps at 1-km resolution, Sci. Data, 5, 180214, https://doi.org/10.1038/sdata.2018.214, 2018. 
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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.