Abstract：
Surface water nutrient pollution, the primary cause of eutrophication, remains a major environmental concern in Western Lake Erie despite intergovernmental efforts to regulate nutrient sources. The Maumee River Basin has been the largest nutrient contributor. The two primary nutrient sources are inorganic fertilizer and livestock manure applied to croplands, which are later carried to the streams via runoff and soil erosion. Prior studies of nutrient source attribution have focused on large watersheds or counties at annual time scales. Source attribution at finer spatiotemporal scales, which enables more effective nutrient management, remains a substantial challenge. This study aims to address this challenge by developing a generalizable Bayesian network model for phosphorus source attribution at the subwatershed scale (12-digit Hydrologic Unit Code). Since phosphorus release is uncertain, we combine excess phosphorus derived from manure and fertilizer application and crop uptake data, flow information simulated by the SWAT model, and in-stream water quality measurements using Approximate Bayesian Computation to derive a posterior that attributes phosphorus contributions to subwatersheds. Our results show significant variability in subwatershed-scale phosphorus release that is lost in coarse-scale attribution. Phosphorus contributions attributed to the subwatersheds are on average lower than the excess phosphorus estimated by the nutrient balance approach currently adopted by environmental agencies. Fertilizer contributes more soluble reactive phosphorus than manure, while manure contributes most of the unreactive phosphorus. While developed for the specific context of Maumee River Basin, our lightweight and generalizable model framework could be adapted to other regions and pollutants and could help inform targeted environmental regulation and enforcement.
摘要：
地表水养分污染，富营养化的主要原因，尽管政府间努力调节营养源，但仍然是伊利湖西部的主要环境问题。莫美河流域一直是最大的营养贡献者。两种主要的营养来源是施用于农田的无机肥料和牲畜粪便，后来通过径流和土壤侵蚀被带到溪流中。先前对营养源归属的研究集中在年度时间尺度上的大型流域或县。在更精细的时空尺度上进行来源归因，这使得更有效的营养管理，仍然是一个巨大的挑战。本研究旨在通过在分水岭尺度（12位水文单位代码）上开发用于磷源归属的通用贝叶斯网络模型来解决这一挑战。由于磷的释放是不确定的，我们结合了来自肥料和肥料施用的过量磷和作物吸收数据，SWAT模型模拟的流量信息，并使用近似贝叶斯计算对河流中的水质进行测量，以得出将磷的贡献归因于亚流域的后验。我们的结果表明，亚分水岭规模的磷释放存在显着差异，而粗尺度的归因却失去了磷。归因于小流域的磷贡献平均低于环境机构目前采用的养分平衡方法估计的过量磷。肥料比粪肥贡献更多的可溶性活性磷，而粪肥贡献了大部分的非活性磷。虽然是针对莫美河流域的特定背景而开发的，我们的轻量级和可推广的模型框架可以适应其他地区和污染物，并有助于为有针对性的环境监管和执法提供信息。
