Department of Natural Resources, Cornell University, Ithaca, NY 14853, USA.
Environ Manage. 2010 Apr;45(4):868-80. doi: 10.1007/s00267-010-9453-y. Epub 2010 Feb 27.
Non-point-source (NPS) pollution remains the primary source of stream impairment in the United States. Many problems such as eutrophication, sedimentation, and hypoxia are linked with NPS pollution which reduces the water quality for aquatic and terrestrial organisms. Increasingly, NPS pollution models have been used for landscape-scale pollution assessment and conservation strategy development. Our modeling approach functions at a scale between simple landscape-level assessments and complex, data-intensive modeling by providing a rapid, landscape-scale geographic information system (GIS) model with minimal data requirements and widespread applicability. Our model relies on curve numbers, literature-derived pollution concentrations, and land status to evaluate total phosphorus (TP), total nitrogen (TN), and suspended solids (SS) at the reach scale. Model testing in the Chesapeake Bay watershed indicated that predicted distributions of water quality classes were realistic at the reach scale, but precise estimates of pollution concentrations at the local scale can have errors. Application of our model in the tributary watersheds along Lake Ontario suggested that it is useful to managers in watershed planning by rapidly providing important information about NPS pollution conditions in areas where large data gaps exist, comparisons among stream reaches across numerous watersheds are required, or regional assessments are sought.
非点源（NPS）污染仍然是美国溪流受损的主要原因。富营养化、淤积和缺氧等许多问题都与 NPS 污染有关，这降低了水生和陆地生物的水质。越来越多的 NPS 污染模型被用于景观尺度的污染评估和保护策略制定。我们的建模方法在简单的景观尺度评估和复杂、数据密集型建模之间发挥作用，通过提供一个快速的、景观尺度的地理信息系统（GIS）模型，该模型所需数据最少，适用范围广泛。我们的模型依赖于曲线数、文献中得出的污染浓度和土地状况，以评估流域尺度的总磷（TP）、总氮（TN）和悬浮固体（SS）。在切萨皮克湾流域的模型测试表明，在流域尺度上，水质类别预测分布是现实的，但在局部尺度上对污染浓度的精确估计可能存在误差。我们的模型在安大略湖支流流域的应用表明，它通过快速提供存在大量数据空白的地区的 NPS 污染状况的重要信息、在众多流域的溪流之间进行比较，或者进行区域评估，对流域规划管理者很有用。
