Springs are sources of freshwater supply. Furthermore, they can also deliver valuable insight into the hydrogeologic processes of a mountainous region, a natural conservation area or a remote study site with no wells. In order to assess the appearance, peculiarities, quality, stability, longevity and resilience of springs and related ecosystems, they need to be regarded in the context of basin-scale groundwater flow systems. The application of spring data evaluation on a basin scale was demonstrated via the carbonate system of Transdanubian Mts., Hungary. The readily measurable physical parameters of springs, the elevation of spring orifice, temperature and volumetric discharge rate provided reasonable classification and characterisation of springs and the related groundwater flow systems. Applying these parameters seemed prospective in a basin-scale understanding of flow systems in data-scarce regions, as monitoring discharge rate and water temperature are cost-effective, requiring no specific tools and analysing procedures. The combined cluster and discriminant analysis (CCDA) can handle uneven data distribution, unequal length and spacing of time series, data gaps, and consider the time-dependent variability of parameters. The optimal number of groups can be determined based on frequently sampled springs (or other entities). The less monitored springs (or other entities) can be classified using a similarity-based approach and linear discriminant analysis (LDA). Diagnosing the relation of springs to groundwater flow systems can advance sustainable water resources management, considering the ecological water needs maintaining various ecosystem services, therefore enhancing the resilience of springs and groundwater-dependent ecosystems.
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