Leveraging recent advances in technologies surrounding the Internet of Things, “smart” water systems are poised to transform water resources management by enabling ubiquitous real-time sensing and control. Recent applications have demonstrated the potential to improve flood forecasting, enhance rainwater harvesting, and prevent combined sewer overflows. However, adoption of smart water systems has been hindered by a limited number of proven case studies, along with a lack of guidance on how smart water systems should be built. To this end, we review existing solutions, and introduce open storm—an open-source, end-to-end platform for real-time monitoring and control of watersheds. Open storm includes (i) a robust hardware stack for distributed sensing and control in harsh environments (ii) a cloud services platform that enables system-level supervision and coordination of water assets, and (iii) a comprehensive, web-based “how-to” guide, available on open-storm.org, that empowers newcomers to develop and deploy their own smart water networks. We illustrate the capabilities of the open storm platform through two ongoing deployments: (i) a high-resolution flash-flood monitoring network that detects and communicates flood hazards at the level of individual roadways and (ii) a real-time stormwater control network that actively modulates discharges from stormwater facilities to improve water quality and reduce stream erosion. Through these case studies, we demonstrate the real-world potential for smart water systems to enable sustainable management of water resources.

The real‐time control of urban watersheds is now being enabled by a new generation of “smart” and connected technologies. By retrofitting stormwater systems with sensors and valves, it becomes possible to adapt entire watersheds dynamically to individual storms. A catchment‐scale control algorithm is introduced, which abstracts an urban watershed as a linear integrator delay dynamical system, parameterizes it using physical watershed characteristics, and then controls network flows using aLinear Quadratic Regulator. The approach is simulated on a 4‐km²urban headwater catchment in Ann Arbor, Michigan, demonstrating the gains of a stormwater system that can adaptively balance between flood mitigation and flow reduction. We introduce an equivalence analysis and illustrate the performance of the controlled watershed across large events (30‐year storms) to show the uncontrolledpassivewatershed can only match it during smaller events (10‐year storm). For these smaller events, the storage volume of the controlled storage nodes (ponds, basins, and wetlands) could be reduced as much as 50% and still achieve the same performance of the controlled watershed. A controller placement analysis is also carried out, whereby all possible combinations of controlled sites are simulated across a wide spectrum of design storms. We show that the control of every storage node may not be needed in a watershed, but rather that in our case study a small subset (30%) of the overall watershed can be controlled in coordination to achieve outcomes that match a fully controlled system, even when tested across a long‐term rainfall record and under noisy sensor measurements.

Smart stormwater systems will transform cities into coordinated and real-time controlled treatment plants.

“Smart” water systems are transforming the field of stormwater management by enabling real-time monitoring and control of previously static infrastructure. While the localized benefits of active control are well-established, the potential for system-scale control of watersheds is poorly understood. This study shows how a real-world smart stormwater system can be leveraged to shape streamflow within an urban watershed. Specifically, we coordinate releases from two internet-controlled stormwater basins to achieve desired control objectives downstream—such as maintaining the flow at a set-point, and generating interleaved waves. In the first part of the study, we describe the construction of the control network using a low-cost, open-source hardware stack and a cloud-based controller scheduling application. Next, we characterize the system’s control capabilities by determining the travel times, decay times, and magnitudes of various waves released from the upstream retention basins. With this characterization in hand, we use the system to generate two desired responses at a critical downstream junction. First, we generate a set-point hydrograph, in which flow is maintained at an approximately constant rate. Next, we generate a series of overlapping and interleaved waves using timed releases from both retention basins. We discuss how these control strategies can be used to stabilize flows, thereby mitigating streambed erosion and reducing contaminant loads into downstream waterbodies.

Connected vehicles are poised to transform the field of environmental sensing by enabling acquisition of scientific data at unprecedented scales. Drawing on a real-world dataset collected from almost 70 connected vehicles, this study generates improved rainfall estimates by combining weather radar with windshield wiper observations. Existing methods for measuring precipitation are subject to spatial and temporal uncertainties that compromise high-precision applications like flash flood forecasting. Windshield wiper measurements from connected vehicles correct these uncertainties by providing precise information about the timing and location of rainfall. Using co-located vehicle dashboard camera footage, we find that wiper measurements are a stronger predictor of binary rainfall state than traditional stationary gages or radar-based measurements. We introduce a Bayesian filtering framework that generates improved rainfall estimates by updating radar rainfall fields with windshield wiper observations. We find that the resulting rainfall field estimate captures rainfall events that would otherwise be missed by conventional measurements. We discuss how these enhanced rainfall maps can be used to improve flood warnings and facilitate real-time operation of stormwater infrastructure.