Smarter Stormwater Systems

Kerkez, Branko; Gruden, Cyndee; Lewis, Matthew; Montestruque, Luis A.; Quigley, Marcus; Wong, Brandon; Bedig, Alex; Kertesz, Ruben; Braun, Tim; Cadwalader, Owen; Poresky, Aaron; Pak, Carrie

doi:10.1021/acs.est.5b05870

Cited by 198 publications

(140 citation statements)

References 38 publications

(53 reference statements)

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“…For example, previous studies found that a CMAC retrofit of an existing stormwater detention facility would be approximately three times lower in cost than the equivalent passive alternative based on whole life cycle costs [18], but the scalability of these savings is still unclear. Previous research has identified that one of the biggest foreseeable challenges to advancing the state of the CMAC practice will be to apply systems thinking to understanding the complex spatiotemporal dynamics that govern water flow and quality across large urban areas-i.e., benefits achieved at a local scale may be masked or eliminated at the city scale if the performance of an individual element is not designed in a broader systems context [9]. Regional implementation of many distributed CMAC systems will require potentially complex and careful logic implementation to ensure that that unintended consequences are minimized.…”

Section: Discussionmentioning

confidence: 99%

“…A controlled basin in Pflugerville, Texas, achieved a 6-fold reduction in nitrate plus nitrite-nitrogen compared to the same pre-retrofit dry basin (0.66-0.11 mg/L) by extending detention time and releasing water before a storm to create additional storage [7]. Many other studies and field implementation sites demonstrating similar benefits for a variety of goals (e.g., flood control, wet-weather reduction, water quality) have been performed across the county [4,5,8,9]. …”

Section: Introductionmentioning

confidence: 99%

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Design and Modeling of an Adaptively Controlled Rainwater Harvesting System

Riaño

Braga

Shetty

et al. 2017

Water

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Management of urban stormwater to mitigate Combined Sewer Overflows (CSOs) is a priority for many cities; yet, few truly innovative approaches have been proposed to address the problem. Recent advances in information technology are now, however, providing cost-effective opportunities to achieve better performance of conventional stormwater infrastructure through a Continuous Monitoring and Adaptive Control (CMAC) approach. The primary objective of this study was to demonstrate that a CMAC approach can be applied to a conventional rainwater harvesting system in New York City to improve performance by minimizing discharge to the combined sewer during rainfall events, reducing water use for irrigation of local vegetation, and optimizing vegetation health. To achieve this objective, a hydrologic and hydraulic model was developed for a planned and designed rainwater harvesting system to explore multiple potential scenarios prior to the system's actual construction. Model results indicate that the CMAC rainwater harvesting system is expected to provide significant performance improvements over conventional rainwater harvesting systems. The CMAC system is expected to capture and retain 76.6% of roof runoff per year on average, as compared to just 14.8% and 41.3% for conventional moisture and timer based systems, respectively. Similarly, the CMAC system is expected to use 81.4% and 18.0% less harvested rainwater than conventional moisture and timer based irrigation approaches, respectively. The flexibility of the CMAC approach to meet competing objectives is promising for widespread implementation in New York City and other heavily urbanized areas challenged by stormwater management issues.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Design and Modeling of an Adaptively Controlled Rainwater Harvesting System

Riaño

Braga

Shetty

et al. 2017

Water

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“…The development of a new generation of sensors, more accurate, smaller, cheaper to manufacture, and able to transmit the information in almost real time, is a contributing factor to the ubiquity of devices generating data of use for the water industry [59][60][61][62][63]. However, despite having access to a broad range of data sources and technical resources, the water utility sector appears to make very limited use of it for the improvement of water quality and source apportionment.…”

Section: Key Opportunities and Challengesmentioning

confidence: 99%

Leveraging Big Data Tools and Technologies: Addressing the Challenges of the Water Quality Sector

2017

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Abstract:The water utility sector is subject to stringent legislation, seeking to address both the evolution of practices within the chemical/pharmaceutical industry, and the safeguarding of environmental protection, and which is informed by stakeholder views. Growing public environmental awareness is balanced by fair apportionment of liability within-sector. This highly complex and dynamic context poses challenges for water utilities seeking to manage the diverse chemicals arising from disparate sources reaching Wastewater Treatment Plants, including residential, commercial, and industrial points of origin, and diffuse sources including agricultural and hard surface water run-off. Effluents contain broad ranges of organic and inorganic compounds, herbicides, pesticides, phosphorus, pharmaceuticals, and chemicals of emerging concern. These potential pollutants can be in dissolved form, or arise in association with organic matter, the associated risks posing significant environmental challenges. This paper examines how the adoption of new Big Data tools and computational technologies can offer great advantage to the water utility sector in addressing this challenge. Big Data approaches facilitate improved understanding and insight of these challenges, by industry, regulator, and public alike. We discuss how Big Data approaches can be used to improve the outputs of tools currently in use by the water industry, such as SAGIS (Source Apportionment GIS system), helping to reveal new relationships between chemicals, the environment, and human health, and in turn provide better understanding of contaminants in wastewater (origin, pathways, and persistence). We highlight how the sector can draw upon Big Data tools to add value to legacy datasets, such as the Chemicals Investigation Programme in the UK, combined with contemporary data sources, extending the lifespan of data, focusing monitoring strategies, and helping users adapt and plan more efficiently. Despite the relative maturity of the Big Data technology and adoption in many wider sectors, uptake within the water utility sector remains limited to date. By contrast with the extensive range of applications of Big Data in in other sectors, highlight is drawn to how improvements are required to achieve the full potential of this technology in the water utility industry.

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“…Engineers have historically responded to these problems by expanding and upsizing stormwater control infrastructure [10]. However, larger infrastructure frequently brings adverse side-effects, such as dam-induced disruption of riparian ecosystems [11], and erosive discharges due to overdesigned conveyance infrastructure [1].…”

Section: Introductionmentioning

confidence: 99%

“…Green infrastructure, for instance, uses lowimpact rain gardens, bioswales, and green roofs to condition flashy flows and remove contaminants [14][15][16]. 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].…”

Section: Introductionmentioning

confidence: 99%

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

Bartos

Kerkez

2019

Advances in Water Resources

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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.

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Smarter Stormwater Systems

Cited by 198 publications

References 38 publications

Design and Modeling of an Adaptively Controlled Rainwater Harvesting System

Design and Modeling of an Adaptively Controlled Rainwater Harvesting System

Leveraging Big Data Tools and Technologies: Addressing the Challenges of the Water Quality Sector

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

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