Abstract
AbstractFlowpathways 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.
Highlights
Agricultural subsurface tile drainage across the midwestern United States has increased eutrophication and the persistence of harmful and nuisance algal blooms (Kleinman et al, 2015; Simard et al, 2000; Van Esbroeck et al, 2016)
Once Qquick, Qslow, Qnew, and Qold were calculated, we developed the following piecewise functions for each time step (t) to estimate the portion of old water that drains to the quick-flow reservoir (Qquick-old), the portion of new water that drains to the quick-flow reservoir (Qquick-new), the portion of new water that drains through the slow-flow reservoir (Qslow-new), and the portion of old water that drains to the slow-flow reservoir (Qslow-old)
Results of the master recession curve suggest that Reservoir 1 (R1) accounted for 54% of the subsurface flow while the remainder, or 46% was attributed to Reservoir 2 (R2)
Summary
Agricultural subsurface tile drainage across the midwestern United States has increased eutrophication and the persistence of harmful and nuisance algal blooms (Kleinman et al, 2015; Simard et al, 2000; Van Esbroeck et al, 2016). Tile drainage networks in fine-textured soils are often the primary field-scale discharge pathway during stormflows and can disproportionately affect watershed-scale water and nutrient budgets (King et al, 2014; Schilling et al, 2020; Williams et al, 2015). Tile-drainage nutrient loadings during stormflows reflect variability in flow pathway dynamics and source water connectivity (Jiang et al, 2021; King et al, 2015; Ortega-Pieck et al, 2020; Pluer et al, 2020; Smith & Capel, 2018). Development and evaluation of a framework that considers both flow pathway and source connectivity dynamics at the field point of discharge (referred to as “edge-of-field”) to assess the implications for tile-drain water quality is a major need and research gap
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