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

Abstract

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 a karst spring recession hydrograph typically involve manual and subjective procedures. This subjectivity introduces a bias that exists, while manual procedures can introduce errors into 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 parameter optimization approaches into Mangin's model to increase the degree of freedom, thereby allowing for more parameter interaction. The modified recession extraction and parameter optimization approaches were tested on three karst springs under different climate conditions. Our results showed that the modified extraction methods are capable of distinguishing different recession components and derived parameters that reasonably represent the analyzed karst systems. We recorded an average Kling–Gupta efficiency KGE > 0.85 among all recession events simulated by the recession parameters derived from all combinations of recession extraction methods and parameter optimization approaches. While there are variabilities among parameters estimated by different combinations of extraction methods, optimization approaches, and seasons, we found much higher variability among individual recession events. We provided suggestions to reduce the uncertainty among individual recession events and raised questions about how to improve confidence in the system's attributes derived from recession parameters.

XLKarst, an Excel tool for time series analysis, spring recession curve analysis and classification of karst aquifers

“…Based on the analysis on spring recessions over several (> 3) hydrological cycles, the maximum value of V d is used by Mangin (1975) to get an estimate of the dynamical volume of the karst system. Some authors compute this volume from the beginning of the flood recession using the fictive extrapolation Q R0 of the discharge for t ≤ t i (see for instance El-Hakim and Bakalowicz (2007), Paiva and Cunha 2020 or Olarinoye et al 2022). The evolution of the baseflow is however unknown during that time and may greatly deviate from an exponential extrapolation (Bailly-Comte et al 2010;Kovács 2021).…”

“…As pointed out by Olarinoye et al (2022), there is a need for automated, robust and objective methods for extracting karst spring recession components but also for fitting the parameters of any mathematical model that reproduce the whole recession dynamics. The model of karst spring recession proposed by Mangin (1975) requires one to fit 3 parameters: t i , and .The automated procedure that is proposed by XLKarst for optimizing the parameters of Mangin's model is to find the set of parameters that allow the maximum number of points to be observed around the theoretical model, regardless of the deviations between the model and the observations for the points that deviate from the theoretical model.…”

“…Other methods have recently been proposed by Olarinoye et al (2022) for the optimization of Mangin's recession model. These methods require setting a parameter for the duration of the period influenced by infiltration, or assuming that the baseflow recession begins when there are no significant variations (low CV) in the flow time series.…”