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 (Q_quick-old), preferential flow of new water (Q_quick-new), slow flow of old water (Q_slow-old), and slow flow of new water (Q_slow-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.

中文翻译：

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量化瓦片排水中的水文途径和源连通性动态：对磷浓度的影响

流动路径和源水连通性动态被广泛认为会影响瓷砖排水水质。在这项研究中，我们开发并评估了一个框架，该框架将基于事件的水文过程线衰退和特定电导端元混合分析 (SC-EMMA) 结合起来，以提供一个更强大的框架，用于量化瓷砖中排水的流动路径动力学和源连通性- 排水景观。高频（30 分钟）流量和电导率数据是从位于俄亥俄州西北部的田边瓷砖干管收集的，并将新开发的框架应用于 2019 年水年收集的数据。 多元线性回归 (MLR) 分析用于评估通路连通性动态对流量加权平均溶解活性磷 (DRP) 浓度的影响，这些数据是作为 USDA-ARS 现场监测网络的一部分收集的。水文过程线衰退和 SC-EMMA 结果突出了事件期间快速（优先）流动和新（降水）水之间传输的内部和事件间差异，挑战了新水通过优先流动路径反映排水的普遍假设。水文流动路径的分析表明基质-大孔交换（Q_快老），新水优先流（Q_快新），旧水慢流（Q_慢老），新水慢流（Q_慢新）) 对瓷砖放电的贡献平均为 9%、39%、42% 和 10%，具有事件间可变性。发现通过大孔流道输送到瓷砖排水管的基质水全年都被激活，即使在较干燥的前因条件下，这表明与前因条件相比，基质-大孔交换对事件内水文过程更敏感。MLR 结果强调，路径连通性水文图部分改进了 DRP 浓度的预测，尽管在基质 - 大孔交换速率较高的景观中改进可能更为明显。