Numerous studies have documented the linkages between agricultural nitrogen loads and surface water degradation. In contrast, potential water quality improvements due to agricultural best management practices are difficult to detect because of the confounding effect of background nitrate removal rates, as well as the groundwater-driven delay between land surface action and stream response. To characterize background controls on nitrate removal in two agricultural catchments, we calibrated groundwater travel time distributions with subsurface environmental tracer data to quantify the lag time between historic agricultural inputs and measured baseflow nitrate. We then estimated spatially distributed loading to the water table from nitrate measurements at monitoring wells, using machine learning techniques to extrapolate the loading to unmonitored portions of the catchment to subsequently estimate catchment removal controls. Multiple models agree that in-stream processes remove as much as 75% of incoming loads for one subcatchment while removing <20% of incoming loads for the other. The use of a spatially variable loading field did not result in meaningfully different optimized parameter estimates or model performance when compared with spatially constant loading derived directly from a county-scale agricultural nitrogen budget. Although previous studies using individual well measurements have shown that subsurface denitrification due to contact with a reducing argillaceous confining unit plays an important role in nitrate removal, the catchment-scale contribution of this process is difficult to quantify given the available data. Nonetheless, the study provides a baseline characterization of nitrate transport timescales and removal mechanisms that will support future efforts to detect water quality benefits from ongoing best management practice implementation.

中文翻译：

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用对比子汇水面积基流浓度量化农业流域中的背景硝酸盐去除机制

许多研究已经记录了农业氮负荷与地表水退化之间的联系。相比之下，由于背景硝酸盐去除率的混杂效应以及地下水驱动的地表作用和河流响应之间的延迟，由于农业最佳管理实践而导致的潜在水质改善难以检测。为了描述两个农业集水区硝酸盐去除的背景控制，我们使用地下环境示踪数据校准地下水传播时间分布，以量化历史农业投入与测量的基流硝酸盐之间的滞后时间。然后，我们根据监测井的硝酸盐测量值估计了地下水位的空间分布负荷，使用机器学习技术将负荷外推到流域的未监测部分，以随后估计流域移除控制。多个模型一致认为，流内过程为一个子汇水面积去除多达 75% 的传入负载，同时为另一个子汇水面积去除 <20% 的传入负载。与直接从县级农业氮预算得出的空间恒定载荷相比，空间可变载荷场的使用不会产生有意义的不同优化参数估计或模型性能。尽管先前使用单井测量的研究表明，由于与还原性泥质围压单元接触而导致的地下反硝化作用在硝酸盐去除中起着重要作用，鉴于现有数据，这一过程的流域规模贡献难以量化。尽管如此，该研究提供了硝酸盐运输时间尺度和去除机制的基线特征，这将支持未来从持续的最佳管理实践实施中检测水质效益的努力。