Open storm: a complete framework for sensing and control of urban watersheds

Water Resources Research

2018

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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 a Linear Quadratic Regulator. The approach is simulated on a 4‐km2 urban 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 uncontrolled passive watershed 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.

Section: 1029/2018wr022657mentioning

confidence: 99%

Real‐Time Control of Urban Headwater Catchments Through Linear Feedback: Performance, Analysis, and Site Selection

Wong

Water Resources Research

2018

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“…The controller placement algorithm presented in this study provides a tool for designing stormwater control systems to better mitigate floods, regulate contaminants, and protect aquatic ecosystems. By reducing peak discharge, optimized placement of benefits, such as decreased first flush contamination [68], decreased sediment transport [69], improved potential for treatment in downstream green infrastructure [1,17], and regulation of flows in sensitive aquatic ecosystems [70].…”

Section: Discussionmentioning

confidence: 99%

“…For our test case, we use the Sycamore Creek watershed, a heavily urbanized creekshed located in the Dallas-Fort Worth Metroplex with a contributing 14 area of roughly 83 km 2 (see Figure 1). This site is the subject of a long-term monitoring study led by the authors [17], and is chosen for this analysis because (i) it is known to experience issues with flash flooding, and (ii) it is an appropriate size for our analysis-being large enough to capture fine-scale network topology, but not so large that computation time becomes burdensome.…”

Section: Experimental Designmentioning

confidence: 99%

“…Smart stormwater systems take this idea further by retrofitting static infrastructure with dynamically controlled valves, gates and pumps [1,[17][18][19]. By actuating small, distributed storage basins and conveyance structures in real-time, smart stormwater systems can halt combined sewer overflows [20], mitigate flooding [1], and improve water quality at a fraction of the cost of new construction [1,17]. While decentralized stormwater management tools show promise towards mitigating urban water problems, it is currently unclear how these systems can be designed to achieve maximal 2 benefits at the watershed scale.…”

Section: Introductionmentioning

confidence: 99%

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Hydrograph peak-shaving using a graph-theoretic algorithm for placement of hydraulic control structures

Bartos

Advances in Water Resources

2019

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The need to attenuate hydrograph peaks is central to the design of stormwater and flood control systems. However, few guidelines exist for siting hydraulic control structures such that system-scale benefits are maximized. This study presents a graph-theoretic algorithm for stabilizing the hydrologic response of watersheds by placing controllers at strategic locations in the drainage network. This algorithm identifies subcatchments that dominate the peak of the hydrograph, and then finds the "cuts" in the drainage network that maximally isolate these subcatchments, thereby flattening the hydrologic response. Evaluating the performance of the algorithm through an ensemble of hydrodynamic simulations, we find that our controller placement algorithm produces consistently flatter discharges than randomized controller configurations-both in terms of the peak discharge and the overall variance of the hydrograph. By attenuating flashy flows, our algorithm provides a powerful methodology for mitigating flash floods, reducing erosion, and protecting aquatic ecosystems. More broadly, we show that controller placement exerts an important influence on the hydrologic response and demonstrate that analysis of drainage network structure can inform more effective stormwater control policies.

“…Wireless sensor networks have become much more affordable (Jin et al, 2010) and cloud-based services are now readily available, even to small research groups (e.g., Amazon Web Services, Microsoft Azure, Google Cloud etc.). The open source hardware movement (e.g., Bartos et al, 2017;Bitella et al, 2014;Gilles et al, 2012;Wong & Kerkez, 2016) is empowering many technological nonexperts, such as decision makers and small research groups, who can now deploy their own sensors to measure a variety of water parameters in near real-time. This is allowing important, but limited, sources of data, such as USGS gauges, to be supplemented by a variety of smaller and stakeholder-relevant measurements.These advances still do not appear to be ushering in new wave of water management.…”

mentioning

confidence: 99%

Using Sensor Data to Dynamically Map Large‐Scale Models to Site‐Scale Forecasts: A Case Study Using the National Water Model

Fries

Water Resources Research

2018

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There has been an explosive growth in the ability to model large water systems. While these models are effective at routing water across massive scales, they do not yet forecast the street‐level information desired by local decision makers. Simultaneously, the increasing affordability of sensors has made it possible for even small communities to measure the state of their watersheds. However, these real‐time measurements are often not attached to a predictive model, thus making them less useful for applications like flood warnings. In this paper, we ask the question: how can highly localized forecasts be generated by fusing site‐scale sensor measurements with outputs from large‐scale models? Rather than altering the larger physical model, our approach uses the outputs of the unmodified model as the inputs to a dynamical system. To evaluate the approach, a case study is carried out across the U.S. state of Iowa using publicly available measurements from over 180 water level sensors and outputs from the National Water Model. The approach performs well across a third of the studied sites, as quantified by a high normalized root mean squared error. A performance classification is carried out based on Principal Component Analysis and Random Forests. We discuss how these results will enable stakeholders with local measurements to quickly benefit from large‐scale models without needing to run or modify the models themselves. The results are also placed into a broader sensor‐placement context to provide guidance on how investments into local measurements can be made to maximize predictive benefits.