Abstract:
Time series analyses are often used for the investigation of karst aquifers, but are only rarely employed in a way of using a large number of spatially distributed time series. Furthermore, only a small number of applications employ other types of hydrological data apart from rainfall, water level and discharge. The presented study of the Malenš?ica karst spring aquifer underlines the usefulness of the simultaneous auto and cross-correlation analysis of daily and hourly hydrological data sets, including discharge, water level, temperature, electrical conductivity and rainfall on a regional scale. The results of the autocorrelation analysis show that the storage capacity of the spring is moderate, but this does not indicate that the system, which is characterized by prevailing conduit porosity, is less intensively karstifed. This suggests that well karstied systems of a more complex structure can have higher memory effects than less complex systems. The results of the cross-correlation analysis show that karst springs and watercourses in the investigated area react instantly and simultaneously to rather homogeneous precipitation, yet with different intensity. In such cases a cross-correlation analysis between rainfall or ponors as inputs and springs as outputs does not provide sufficient information on the hydrogeological functioning of the system, whereas the results of a cross-correlation analysis of electrical conductivity data sets provide valuable information on its functioning and can be easily compared to those obtained by tracer tests. On the other hand, the applicability of a temperature time series in such complex karst systems is limited. A comparative analysis of the results of the time series analyses performed in successive hydrological years has proven that the selection of the hydrological year can have strong effects on the results of a time series analysis.