Oxygen Isotopes in Different Recession Subregimes of Karst Springs in the Brezovské Karpaty Mts. (Slovakia)

Peter Malík, Juraj Michalko

DOI: https://doi.org/10.3986/ac.v39i2.99


Karst spring hydrograph separation method based on quick iterative solution of several simple exponential and linear equations, was developed for linking small datasets of sam­ples to various hydrologic situations. The method is based on a presumption, that a spring’s discharge depends on the level of aquifer saturation by groundwater, and that the same dis­charge reflects the same groundwater saturation (piezometric level) in the aquifer. Every spring can be described by unique sets of constant starting discharges, Q0 values, recession coef­ficients α (laminar flow components in exponential equations), and β (turbulent flow components in linear equations). Eachsubregime can be detected by recession curve analyses of the complete spring’s discharge time series. In this hydrographseparation, every measured discharge value, Qt, is then deter­mined by a representative time, t; i.e., theoretical elapsed time t from the total maximum discharge value Qmax. The aim of the iteration process is to obtain this representative time t for eachdischarge. The individual flow components are calculated us­ing the same t value. These variances in subregime discharges in a certain moment can be linked to the components analysed in the same moment, in order to obtain the end members of the theoretical mixture. This technique was developed and ap­plied on the discharge time series of the four karstic springs in the Brezovské Karpaty Mts. (Slovakia), built mainly by karsti­fied Middle and Upper Triassic dolomites and limestones. Groundwater of individual springs were characterised by δ18O (SMOW) and groundwater temperature values and end mem­bers of two laminar and one turbulent subregimes were calcu­lated. Results were based on sparsely populated datasets and manual discharge records, but represent a perspective method for future development and interpretations on limited dataset results.

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DOI: https://doi.org/10.3986/ac.v39i2.99


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