DELINEATING END-MEMBER TRACER BREAKTHROUGH CURVE GEOMETRIES: QUANTITATIVE FIELD AND MODELING APPLICATIONS IN SOUTHEASTERN MINNESOTA

Quantitative tracing efforts in southeastern Minnesota have aided in the delineation of springsheds and karst groundwater basins over the past 35 years. A suite of 55 traces performed through thick sequences of Paleozoic carbonate rocks aid in the characterization of regional, triple permeability, karst aquifers. Each of the 55 data sets were normalized to time and concentration at the curve peak and statistically analyzed to determine controlling variables in curve geometry.

The normalized tracer data can be separated into two end-member regimes (EM1, EM2) and one sub-regime (SEM1). EM1 breakthrough curves have rapid response times and small, skewed tails, while EM2 curves have variable response times and long, irregular tails. In SEM1, breakthrough curves are characterized by rapid solute response, followed by long tailing.

End-member breakthrough curve regimes appear to correlate with continuous temperature and conductivity data sets from associated springs. Karst basins with EM1 and SEM1 breakthrough curves respond to event-scale variability in temperature and conductivity following recharge events, while EM2 curves respond to seasonal variations in temperature with little variability in conductivity.

Forward and inverse modeling techniques using a two-region non-equilibrium model (CXTFIT 2) are being investigated to determine if variations in breakthrough curves for end-member geometries are a function of the presence of immobile and mobile flow regions. QTRACER2 is used to inversely calculate transport parameters from the breakthrough curves. CXTFIT 2 is used to fit the breakthrough curve geometry and yield values of the flow equation partitioning coefficient (β) to assess the impact of solute partitioning in EM1, EM2 and SEM1. Best fit approximations are found with EM1, where little tailing occurs in the breakthrough curves. Forward modeling then investigates the effect of varying β while holding other variables constant. Varying β initiates large modifications in curve geometry, with the largest deviations found with SEM1 followed by EM2 and EM1 respectively. Modeled data indicate that β variability alone significantly modifies geometry of end-member cases at different magnitudes.