Preprints
https://doi.org/10.5194/hess-2021-249
https://doi.org/10.5194/hess-2021-249

  18 May 2021

18 May 2021

Review status: this preprint is currently under review for the journal HESS.

Karst spring recession curve analysis: efficient, accurate methods for both fast and slow flow components

Tunde Olarinoye1, Tom Gleeson2, and Andreas Hartmann1,3 Tunde Olarinoye et al.
  • 1Chair of Hydrological Modeling and Water Resource, University of Freiburg, Germany
  • 2Department of Civil Engineering, University of Victoria, BC, Canada
  • 3Department of Civil Engineering, Bristol University, UK

Abstract. Analysis of karst spring recession hydrographs is essential for determining hydraulic parameters, geometric characteristics and transfer mechanisms that describe the dynamic nature of karst aquifer systems. The extraction and separation of different fast and slow flow components constituting karst spring recession hydrograph typically involve manual and subjective procedures. This subjectivity introduces bias, while manual procedures can introduce errors to the derived parameters representing the system. To provide an alternative recession extraction procedure that is automated, fully objective and easy to apply, we modified traditional streamflow extraction methods to identify components relevant for karst spring recession analysis. Mangin’s karst-specific recession analysis model was fitted to individual extracted recession segments to determine matrix and conduit recession parameters. We introduced different parameters optimisation approaches of the Mangin’s model to increase degree of freedom thereby allowing for more parameters interaction. The modified recession extraction and parameters optimisation approaches were tested on 3 karst springs in different climate conditions. The results show that the modified extraction methods are capable of distinguishing different recession components and derived parameters reasonably represent the analysed karst systems. We recorded an average KGE > 0.7 among all recession events simulated by recession parameters derived from all combinations of recession extraction methods and parameters optimisation approaches. While there are variability among parameters estimated by different combinations of extraction methods and optimisation approaches, we find even much higher variability among individual recession events. We provide suggestions to reduce the uncertainty among individual recession events and to create a more robust analysis by using multiple pairs of recession extraction method and parameters optimisation approach.

Tunde Olarinoye et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2021-249', Anonymous Referee #1, 14 Jun 2021
    • AC2: 'Reply on RC1', Tunde Olarinoye, 31 Aug 2021
  • RC2: 'Comment on hess-2021-249', Anonymous Referee #2, 19 Jul 2021
    • AC1: 'Reply on RC2', Tunde Olarinoye, 31 Aug 2021
    • AC3: 'Reply on RC2', Tunde Olarinoye, 31 Aug 2021

Tunde Olarinoye et al.

Tunde Olarinoye et al.

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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 karst spring. Results showed that by simple modification, streamflow extraction methods can identify slow and fast components and derived recession parameters are within reasonable ranges.