Have you ever tried to address flood risk reduction to protect infrastructure, or reduce pollutant loading to meet a TMDL, only to be faced with the frustration that in an urban setting there is little to no space left to build new stormwater features at grade, and underground systems are unachievably expensive? Existing stormwater infrastructure retrofitted with Adaptive Level Control Systems (ALCS), is one of the keys to unlocking these benefits where managers and engineers have previously been limited by space and budget. These adaptive systems are connected and responsive. They are connected to observed data and current flow and water level conditions, to weather forecasts, and to hydrologic models, adaptively altering pond outlet structures to respond to current and predicted conditions. ALCS can improve the primary functions of existing designed and constructed stormwater ponds, controlling flow rates and water levels, increasing sediment and nutrient capture, and reducing erosive energy in the downstream flow. However, these systems are relatively new with few precedent projects and use of these systems is inherently more complicated than traditional BMPs. In addition, regulations and associated permitting processes are not necessarily well-suited for evaluating these systems. Through this discovery project, we aim to incorporate a new technology in Minnesota’s stormwater management toolbox, by demonstrating effectiveness in improving primary stormwater pond function, and providing a roadmap to other practitioners and managers to design, permit, and implement their own ALCS.
Projected Outcomes
- The project will provide valuable knowledge and guidance to cities, watershed districts, and other water managers regarding the effectiveness of ALCS in providing contaminant load reduction and flood protection services systemwide. We expect this project to result in new documentation on ALCS suitable for the MPCA’s Minnesota Stormwater Manual.
- The project will provide a practical example and best practices (roadmap) for design and implementation of an ALCS, through effectively engaging stakeholders, developing shared perspectives on tradeoffs and risks, and permitting an ALCS.
- The project will examine and summarize the capabilities and inadequacies of available technical and modeling tools for assessing clean water and flood risk reduction services of ALCS. The project will also review and comment on the potential use of tools currently in development such as Machine Learning used for optimization or hydrologic applications.
- The project will provide guidance on developing operational algorithms that balance stakeholder interests, values, and risks by minimizing overall loss and pursuing equitable value throughout the system.
- The project will aim to provide the documented information necessary to support implementation of these systems as readily as other well-known and trusted BMPs. We will aim to make this relatively new technology more comfortable for practitioners, managers, and engineers to recommend, design and implement these systems.