Abstract

Tracer testing is the only method in karst hydrogeology that can definitively determine whether a particular site belongs to a watershed of a particular karst spring. Therefore, it is an essential technique for delineating groundwater basins in karst areas. The availability of tracer test results is often limited due to the complicated and relatively expensive application of this approach, especially for large regional watersheds. The Croatian part of the Dinaric karst region extends for several hundred kilometers along the Adriatic coast and consists almost entirely of highly karstified carbonate rocks. The groundwater basins in these areas almost never match the surface morphology of the terrain. In practice, all available results of previous surveys are often used to define watersheds, regardless of the methodology and age of their implementation. This is also true for the earlier delineations of the Gacka River watershed, a regional karst basin in the Croatian Dinaric karst. However, tracer testing methods, especially the accuracy of tracer determination and monitoring, have improved significantly during this time. In order to assess the reliability of past tracing results in this significant karst basin, we reviewed reports of previous tracer tests. More recent tests, in particular the most recent multitracer injection test with continuous tracer detection on the major springs, produced high-quality data that allowed us to assess the reliability of the findings from prior research. A number of large karst springs with partially overlapping subcatchments feed the Gacka River. After discarding unreliable tracing data, we reevaluated the subcatchments of the main springs as well as the characteristics of the regional groundwater flow patterns throughout the basin, which is particularly important for water quality protection measures of the springs. The Gacka River basin is used as a case study to emphasize the importance of thoroughly assessing the reliability of previous tracing data before using them in regional analyses.

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