Flowpathways and source water connectivity dynamics are widely recognized to affect tile-drainage water quality. In this study, we developed and evaluated a framework that couples event-based hydrograph recession and specific conductance end-member mixing analysis (SC-EMMA) to provide a more robust framework for quantifying both flow pathway dynamics and source connectivity of drainage water in tile-drained landscapes. High-frequency (30-min) flow and conductivity data were collected from an edge-of-field tile main located in northwestern Ohio, and the newly developed framework was applied for data collected in water year 2019. Multiple linear regression (MLR) analysis was used to evaluate the impact of pathway-connectivity dynamics on flow-weighted mean dissolved reactive P (DRP) concentrations, which were collected as part of the USDA-ARS edge-of-field monitoring network. The hydrograph recession and SC-EMMA results highlighted intra- and interevent differences between quick (preferential) flow and new (precipitation) water transported during events, challenging a common assumption that new water reflects drainage through preferential flow paths. The analysis of hydrologic flow pathways demonstrated matrix–macropore exchange (Qquick-old), preferential flow of new water (Qquick-new), slow flow of old water (Qslow-old), and slow flow of new water (Qslow-new) contributed 9, 39, 42, and 10% to tile discharge, on average, with interevent variability. Matrix water that is transported to tile drains via macropore flowpaths was found to be activated throughout the year, even under drier antecedent conditions, suggesting that matrix–macropore exchange was more sensitive to within-event hydrological processes as compared with antecedent conditions. The MLR results highlighted that pathway-connectivity hydrograph fractions improved prediction of DRP concentrations, although improvement may be more pronounced in landscapes with higher rates of matrix–macropore exchange.

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Published in Vadose Zone Journal, v. 20, issue 5, e20154.

© 2021 The Authors

This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Current funding for the USDA-ARS edge-of-field research network is in part through the NRCS Conservation Effects Assessment Project (CEAP).