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Spatial optimization of watershed management practices for nitrogen load reduction using a modeling-optimization framework

机译:使用模型优化框架对流域管理实践进行空间优化以减少氮负荷

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Best management practices (BMPs) can be used effectively to reduce nutrient loads transported from non-point sources to receiving water bodies. However, methodologies of BMP selection and placement in a cost-effective way are needed to assist watershed management planners and stakeholders. We developed a novel modeling-optimization framework that can be used to find cost-effective solutions of BMP placement to attain nutrient load reduction targets. This was accomplished by integrating a GIS-based BMP siting method, a WQM-TMDL-N modeling approach to estimate total nitrogen (TN) loading, and a multi-objective optimization algorithm. Wetland restoration and buffer strip implementation were the two BMP categories used to explore the performance of this framework, both differing greatly in complexity of spatial analysis for site identification. Minimizing TN load and BMP cost were the two objective functions for the optimization process. The performance of this framework was demonstrated in the Tippecanoe River watershed, Indiana, USA. Optimized scenario-based load reduction indicated that the wetland subset selected by the minimum scenario had the greatest N removal efficiency. Buffer strips were more effective for load removal than wetlands. The optimized solutions provided a range of trade-offs between the two objective functions for both BMPs. This framework can be expanded conveniently to a regional scale because the NHDPlus catchment serves as its spatial computational unit. The present study demonstrated the potential of this framework to find cost-effective solutions to meet a water quality target, such as a 20% TN load reduction, under different conditions.
机译:最佳管理实践(BMP)可以有效地用于减少从非点源到接收水体的营养负荷。但是,需要以经济有效的方式选择和安置BMP的方法,以帮助流域管理规划者和利益相关者。我们开发了一种新颖的建模优化框架,该框架可用于找到BMP放置的经济有效的解决方案,以实现减少营养负荷的目标。这是通过集成基于GIS的BMP选址方法,用于估计总氮(TN)负载的WQM-TMDL-N建模方法以及多目标优化算法来实现的。湿地恢复和缓冲带实施是用于探索此框架性能的两个BMP类别,两者在用于站点识别的空间分析的复杂性方面差异很大。最小化TN负载和BMP成本是优化过程的两个目标功能。该框架的性能在美国印第安纳州的蒂皮卡诺河分水岭得到了证明。基于情景优化的负荷减少表明,由最小情景选择的湿地子集具有最大的氮去除效率。缓冲带比湿地更有效地减轻负荷。优化的解决方案为两个BMP提供了两个目标函数之间的权衡取舍。由于NHDPlus集水区是其空间计算单元,因此可以方便地将该框架扩展到区域规模。本研究证明了该框架在寻找经济有效的解决方案以实现水质目标(例如在不同条件下将总氮负荷降低20%)方面的潜力。

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