Recession Curve Analysis is a common method to characterize karstic aquifers and their discharge dynamics. Although this technique provides crucial information on quantifying system hydrodynamic properties, the manually selected recession curves analysis is neither a practical technique to cover all candidate recession curves, nor it allows extracting the entire hydrological diversity of the recession behavior. This study aimed to comparatively evaluate the applicability of automated recession selection procedures to the late-time recession analysis of karst spring hydrograph. For the comparative evaluation of the three automated recession extraction methods (Vogel Method, Brutsaert Method, and Aksoy and Wittenberg Method), we quantified the late-time recession parameters of spring hydrographs by combining three extraction methods with four recession analysis methods (Maillet, 1905; Boussinesq, 1904; Coutagne, 1948; and Wittenberg, 1999). By applying our experimental design into the five karst springs located in Austria, we identified the possible weaknesses of the automated recession extraction procedures for the late-time recession analysis for spring hydrographs. To explore the value of the karst spring’s physicochemical data (electrical conductivity and water temperature) as a completion data for the recession curve analysis, we carried out the hydro-chemograph analysis to examine the recession time and its duration. The research provides a research direction as to how the automated recession extraction procedures for the karst spring hydrographs could be improved by the physicochemical signatures of karst springs.
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