Collaborative Research: Reimagining Urban Watershed Management: A Systems Approach to Stormwater Control and Ecological Rehabilitation

预计更多更大、更密集的城市将对全球河流生态系统产生更强大和更普遍的压力。城市化导致雨水径流增加，从而导致一系列生态损害，统称为“城市河流综合症”（USS）。因此，雨水控制对于保护和恢复城市水道至关重要，但雨水管理在很大程度上优先考虑了由社会关注推动的目标，例如防洪和保护基础设施，而不是考虑受纳水域的生态功能。因此，在“解决综合症”方面几乎没有取得任何进展。该项目旨在通过设计和演示一种高地干预系统方法来恢复河流生态条件，从而改变城市径流的管理方式。预计这项工作将取得根本性的技术和概念进步，以实现急需的下一代雨水基础设施，解决国家工程院提出的恢复和改善城市基础设施的巨大挑战。该项目可以显着改善美国和国外的地表水状况。据美国环保署称，城市径流可能是美国已评估的超过 49,000 英里河流和溪流的损害来源，这一数字令人震惊，说明了创新雨水管理解决方案的潜在影响。该项目的目标是通过创建恢复和保护地表水的新框架，为改变城市径流的管理方式奠定基础。该项目将雨水径流重新定义为一个环境流动问题，可以通过将传统的雨水基础设施升级为能够自适应地满足生态和监管目标的网络物理“智能雨水”系统来解决。用绿色替代品（例如生物沼泽、湿池塘）静态和分散地取代灰色雨水基础设施并没有为USS提供纠正措施。这导致人们越来越多地倡导在流域范围内协调雨水干预措施的操作转变，以及从环境流的角度看待城市雨水管理的概念转变。传感器和控制装置的最新进展现在允许雨水基础设施作为“智能”系统运行，用于管理径流如何以及何时排入接收流，为基于一组多个目标（重新）建立所需的流态提供了前所未有的机会。在该项目中，目标是确定并演示如何调整雨水系统以实现环境流量作为目标函数，从而满足生态和监管目标。该项目将模型参数化和模拟与实证测试相结合，旨在评估是否可以使用“智能雨水”方法实现环境流量（1）； (2) 改变城市河流的生态系统属性和功能；如果是这样，(3)减轻了USS的损伤特征。这将需要 (1) 确定和定义城市流域的生态绩效目标； (2) 采用系统方法来优化雨水干预措施，利用可控出口和自适应控制算法； (3) 在两个实验测试台上对现场和系统级智能雨水管理进行实证论证和评估。该项目还将通过教育合作伙伴关系进行公众宣传。该项目团队将继续与田纳西州诺克斯县学校合作，举办每两年一次的“遇见小溪”公民科学活动，为学生提供了解城市化对河流的影响和雨水控制自动化的机会，并收集有关河流条件的物理和生物数据，以支持 K-12 课程目标。此外，这项研究的各个方面将被纳入研究人员在各自机构教授的主题课程中，包括生态恢复研究生研讨会和专注于建模和分析的工程课程。该项目将通过让来自代表性不足群体的学生参与进来，帮助促进 STEM 领域的多样性、公平性和包容性，这些学生将通过 UTK 多元化项目工程办公室等组织招募，并将为研究生教育和职业发展提供极好的机会。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

It is expected that a greater number of larger and denser cities will manifest more potent and pervasive pressures on stream ecosystems worldwide. Urbanization leads to greater stormwater runoff, which can result in a slate of ecological impairments that collectively have been identified as the “urban stream syndrome” (USS). Stormwater control is thus central to protecting and rehabilitating urban waterways, but stormwater management has largely prioritized objectives motivated by societal concerns, like flood control and protecting infrastructure, over consideration of ecological functions in receiving waters. Consequently, little progress has made towards ‘solving the syndrome’. This project aims to transform how urban runoff is managed by designing and demonstrating a systems approach to upland interventions to rehabilitate in-stream ecological conditions. The expectation is that the work will yield fundamental technological and conceptual advances to realize much-needed next generation stormwater infrastructure, addressing the grand challenge laid out by the National Academy of Engineering to Restore and Improve Urban Infrastructure. The project could substantively improve the condition of surface waters across the US and abroad. According to the US EPA, urban runoff is the probable source of impairment for over 49,000 miles of rivers and streams that have been assessed in the US, a staggering number that illustrates the potential impact of innovative stormwater management solutions. It is the goal of the project to provide the foundation for transforming how urban runoff is managed by creating a new framework for rehabilitating and protecting surface waters. The project will recast stormwater runoff as an environmental flows problem that can be addressed by upgrading traditional stormwater infrastructure into cyber-physical ‘smart stormwater’ systems capable of adaptively meeting ecological and regulatory objectives.Static and decentralized replacement of grey stormwater infrastructure with green alternatives (e.g., bioswales, wet ponds) has not provided corrective remedies of USS. This has led to increasing advocacy for operational shifts towards basin-scale coordination of stormwater interventions and conceptual shifts towards viewing urban stormwater management through the lens of environmental flows. Recent advances in sensors and controls now allow stormwater infrastructure to be operated as a ‘smart’ system for managing how and when runoff is discharged into a receiving stream, affording unprecedented opportunities to (re)establish desired flow regimes based on a set of multiple objectives. In this project, the target is to determine and demonstrate how stormwater systems can be tuned to achieve environmental flows as an objective function to meet ecological and regulatory goals. The project will combine model parameterization and simulations with empirical tests, with the aim of assessing whether achieving environmental flows (1) is possible using a ‘smart stormwater’ approach; (2) alters ecosystem attributes and functioning in urban streams; and if so, (3) alleviates impairments characteristic of USS. This will entail (1) identifying and defining ecologically-informed performance objectives for urban watersheds; (2) using a systems approach to optimize stormwater interventions that leverage controllable outlets and adaptive control algorithms; and (3) empirically demonstrating and evaluating site- and system-level smart stormwater management at two experimental testbeds. The project also will also pursue public outreach through educational partnerships. The project team will continue to work with Tennessee's Knox County Schools to host biannual “Meet the Creek” citizen science events that offer students opportunities to learn about the effects of urbanization on streams and automation of storm water control, as well as collect physical and biological data on in-stream conditions that will support K-12 curricular goals. Additionally, aspects of this study will be integrated into topical courses taught by the investigators at their respective institutions, including a graduate seminar on ecological restoration and engineering courses focusing on modeling and analytics. The project will help advance diversity, equity and inclusion in STEM fields by involving students from underrepresented groups, who will be recruited through organizations like the UTK Engineering Office of Diversity Programs, and will afford excellent opportunities for graduate education and career advancement.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.