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pubs.acs.org/est
Smarter Stormwater Systems
Branko Kerkez,*,† Cyndee Gruden,‡ Matthew Lewis,§ Luis Montestruque,∥ Marcus Quigley,⊥
Brandon Wong,† Alex Bedig,⊥ Ruben Kertesz,∥ Tim Braun,∥ Owen Cadwalader,⊥ Aaron Poresky,#
and Carrie Pak∇
†
University of Michigan, Department of Civil and Environmental Engineering, Ann Arbor, Michigan 48109, United States
University of Toledo, Department of Civil Engineering, Toledo, Ohio 43606, United States
§
Michigan Aerospace Corporation, Ann Arbor, Michigan 48108, United States
∥
Emnet LLC, South Bend, Indiana 46617, United States
⊥
OptiRTC, Inc., Boston, Massachusetts 02116, United States
#
Geosyntec Consultants, Atlanta, Georgia, United States
∇
Clean Water Services, Hillsboro, Oregon 97123, United States
Downloaded via 54.163.42.124 on July 14, 2020 at 12:58:40 (UTC).
See https://pubs.acs.org/sharingguidelines for options on how to legitimately share published articles.
‡
ABSTRACT: Existing stormwater systems require significant investments to meet challenges imposed by climate change, rapid urbanization,
and evolving regulations. There is an unprecedented opportunity to
improve urban water quality by equipping stormwater systems with lowcost sensors and controllers. This will transform their operation from
static to adaptive, permitting them to be instantly “redesigned” to
respond to individual storms and evolving land uses.
cities in the U.S. are adversely impacted by exfiltration and
overflows from combined sewers.3−5 These overflows have
increased due to leaks in aging infrastructure and shrinking
municipal budgets.
The increase in the volume, velocity and contaminants in
stormwater runoff has caused a crisis in receiving water
bodies.6−9 Harmful algal blooms, associated with anthropogenic
inputs of nutrients, have resulted in unsafe drinking water,
impaired fisheries and damage to recreational waters.10−14 As
such, managing pollutant loadings from urban stormwater has
become one of our most pressing environmental challenges.15,16
Expansion and upsizing of gray infrastructure are perhaps the
most common solutions to coping with increased runoff
resulting from changing weather and land use.17 Aggressive
climate adaptation via traditional tools may lead to overdesigned gray infrastructure, which conveys water too quickly
to streams, leading to floodplain encroachment, increases in
runoff volumes, and stream erosion. To preserve stream
stability and ecological function, advances in stormwater
science are calling for traditional peak attenuation designs to
be replaced with those that reduce stream erosion during
smaller, more frequent storms.18 As communities seek more
INTRODUCTION
The design of stormwater and sewer systems is based on
historical observations of precipitation and land use. These
systems require significant investments to meet challenges
imposed by rapid urbanization, evolving regulations and an
uncertain climate. As a result, runoff from urban environments
is threatening environmental health by lowering the quality of
receiving waters, including fisheries, recreational sites and
sources of drinking water. There is an unprecedented
opportunity, however, to improve urban water flow and quality
by equipping existing stormwater systems with low-cost sensors
and controllers. This will enable a new generation of intelligent
green and gray stormwater networks, which will adapt their
operation to maximize water quality benefits in response to
individual storm events and changing landscapes.
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STATIC SOLUTIONS TO A DYNAMIC PROBLEM
The vast majority of the world’s population resides in or near
urban centers, underscoring the need to sustainably manage
anthropogenic environmental impacts. Urbanization and land
development are disruptive to the hydrologic cycle since they
result in an altered, more impervious landscape, which
promotes increased runoff at the expense of infiltration and
evapotranspiration.1,2 While most cities maintain a dedicated
stormwater infrastructure, ecosystems near many postindustrial
■
© 2016 American Chemical Society
Published: May 26, 2016
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Figure 1. System-level stormwater measurement and control.
stormwater facilities.29 These technological advances have the
potential to become highly transformative, however, by
enabling stormwater infrastructure to evolve from static to
highly adaptive (Figure 1). By coupling the flow of water with
the flow of information, modern stormwater infrastructure will
adapt itself in real-time to changing storms and land uses, while
simultaneously providing a highly cost-effective solution for
cities that are otherwise forced to spend billions on stormwater
reconstruction.30
Given advances of modern sensors and data acquisition
systems, it is now feasible to monitor green and gray
infrastructure projects pre- and postconstruction to provide in
situ performance metrics. This is afforded by a significant
reduction in the cost of sensors and cloud-hosted real-time data
systems. Many commercial and open-source platforms,
specifically geared toward demands imposed by storm and
sewer applications, are now available and promising to lower
the cost of wireless sensor deployments. Water flow, stage,
precipitation and soil-moisture can now be measured
seamlessly and continuously. The development of robust and
affordable in situ water quality sensors for nutrients, metals or
bacteria is still evolving.
While new measurements will provide significant insight into
the study and management of stormwater systems, it is the
ability to directly and proactively control these systems that
presents the biggest potential impact to water quality. Low-cost,
reliable and secure actuators (e.g., valves, gates, pumps) can
now be attached to existing stormwater systems to control the
flow of water in pipes, ponds and green infrastructure.
Examples include inflatable pillows that can be used to take
advantage of underused inline storage,31 or smart outlet
structures that control water levels in response to real-time
data and weather forecasts (Figure 2).
While real-time process control in water and wastewater
treatment has been studied extensively and continues to be a
fruitful area of research,33 there is now the opportunity to
distribute these treatment ideas to the watershed scale. This
presents an exciting new paradigm: retrof itting existing stormwater inf rastructure through cost-ef fective sensors and actuators will
resilient and adaptive stormwater solutions, novel and nontraditional alternatives to new construction must be considered.
One such alternative is provided by green inf rastructure (GI),
which augments impervious urban areas with pervious solutions
such as bioswales, green roofs and rain gardens.19−21 GI is
designed to restore some ecosystem functions to preurbanization levels by capturing runoff and contaminants before they
enter the stormwater system. These solutions have experienced
a significant rise in popularity due to their promise to offer a
low impact alternative toward buffering flows and improving
runoff water quality.22 Much research remains to be conducted,
however, to test the efficacy and scalability of GI as an
alternative to gray infrastructure. To that end, more costeffective sensing solutions are required to assess the in situ
performance and improve the maintenance of GI.23,24
While stormwater systems do change (albeit slowly), their
design performance is often regarded as static due to limited
ability to adapt to changing climate and land uses. More
importantly, stormwater solutions are engineered on a site-bysite basis, with little consideration given to ensuring that local
benefits are actually adding up to achieve a collective
outcome.25 Rather than offering an alternative, a new solution
promises to augment, rather than replace, green and gray
infrastructure. This approach relies heavily on sensor and
information technology to make existing stormwater systems
more adaptive by embedding them with connectivity and
intelligence.
REAL-TIME ADAPTIVE MANAGEMENT
The past decade has witnessed significant advances and
reduction in the cost of novel sensors, wireless communications
and data platforms. In large, much of this development has
accompanied the recent boom on the Internet of Things (IoT), a
technological movement that promises to build the next
generation of interconnected and smart buildings and cities.26
The stormwater sector has been slow in its adoption of these
technologies, especially in the context of high-resolution and
real-time decision-making. Present uses of sensors range from
regulatory compliance27,28 to performance studies of individual
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and dissoved contaminants. A controlled basin in Pflugerville,
Texas, achieved 6-fold reduction in nitrate plus nitrite-nitrogen
compared to the same preretrofit dry basin (0.66 mg/L to 0.11
mg/L) by extending detention time and releasing water before
a storm to create additional storage.40 While biological uptake
likely contributed to nitrogen removal, reliable and affordable in
situ sensors for many dissolved pollutants are still needed to
fully understand the impacts of control to dissoved pollutant
removal in natural treatment systems.
Real-time control of a retrofitted detention pond showed that
the removal of Escherichia coli was improved by strategically
retaining water for 24 h after a storm rather than allowing the
water to flow though the pond as originally designed.41 For the
controlled basin the outlet concentrations were an order of
magnitude lower than inlet concentrations (1940 MPN/100
mL in vs 187 MPN/100 mL out; and 3410 MPN/100 mL in vs
768 MPN/100 mL out), whereas the uncontrolled basin
showed limited removal and even increased E. coli at the outlet
(4350 MPN/100 mL in vs 8860 MPN/100 mL out; 10800
MPN/100 mL in vs 11000 MPN/100 mL out). Since
streambed concentrations of E. coli were three times higher
than in the streamwater, the primary mechanisms for removal
were attributed to sedimentation and increased exposure to
sunlight. This example also speaks to the need to be cognizant
of flow releases from controlled basins, as high outflows can
resuspend pollutants. As such, real-time control can be used to
modulate the flow rate from storage facilities to reduce
downstream erosion and pollutant loads. Such strategies
begin to place real-time control into a much broader systems
context, whereby each individual stormwater facility not only
generates local benefits, but can also be used to improve flow
and water quality at the city-scale.
Flow modulation for stream protection was demonstrated at
two pilot sites owned by Clean Water Services (CWS) in
Washington County, Oregon. In one system (sized to retain 0.2
in. of rainfall), the addition of real-time control to an existing
wet pond reduced the volume and duration of channel forming
discharges by approximately 25%. In a second facility (a dry
detention pond), the use of real-time control was used to
minimize release rates in smaller, more frequent storm events
while maintaining the ability to match predevelopment peak
flows during larger storms. This enhancement was modeled to
reduce the volume of erosive flows by nearly 60% and the
volume of wet weather discharges by nearly 70% compared to a
passive basin (Figure 3). Additionally, the use of real-time
control increased the average residence time of this facility from
1 to 19 h. In a simulation case study real-time control reduced
Figure 2. Example sensing and control devices (a) Remote valve for
basin control, (b) smart sensing manhole cover, and (c) an opensource sensor node for distributed measurement and control.32
transform its operation f rom static to adaptive, permitting it to be
instantly “redesigned” to respond to changing conditions. There is
an inherent complexity associated with control of city-scale
systems, however, as they are comprised of a variety of gray and
green solutions and driven by complex storm patterns,
hydrologic phenomena, and water quality dynamics. The
number of studies addressing real-time water quality control
is limited but promising, ranging from local- to city-scale
control.
REAL-TIME CONTROL OF INDIVIDUAL
STORMWATER FACILITIES
Many existing studies focus on the real-time control of
stormwater basins and ponds, which are some of the most
common elements in a stormwater system.34−36 Pollutant
removal in basins comprises a complex interaction between a
number of mechanisms, including sedimentation, flotation,
infiltration, biological conversion, and degradation.37 Traditionally, these facilities are designed as compromises between flood
control (detention) and water quality control (retention), with
limited ability to adapt functionality to individual storm events.
Retrofitting an existing site with a real-time control valve
permits it to serve both as a detention and retention basin, as
well as a spectrum of in-between configurations. One control
rule, for example, opens a valve to drain a pond if a storm is
forecasted, which creates additional storage for incoming
runoff. Similarly, runoff can be strategically retained after a
storm to improve settling and biological uptake. It has been
shown, for example, that by temporarily converting a detention
basin to a retention basin, the removal efficiency of total
suspended solids (TSS) increased from 39% (189 120 g inflow
vs 98 269 g outflow) to 90% (e.g., 59 807 g inflow vs 8055 g
outflow) and ammonia-nitrogen increased from 10% (101.1 g
inflow vs 79.2 g outflow) to nearly 90% (e.g., 163.5 g inflow vs
7.8 g outflow).37,38 Using data from these studied, Mushalla et
al.39 simulated that retaining water using real-time controls may
result in up to a 60% improvement in small particle removal
compared to a traditional design.
Some studies are also beginning to show that real-time
control can play a significant role in removing biological, metal
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Figure 3. Improvement achieved by retrofitting an existing basin to
reduce erosive flows.
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Figure 4. Comparison of combined sewer overflows (CSOs) before and after commissioning of real-time sensing and control system in South Bend,
IN.
the required pond volume by 30−50%, compared to a passive
facility, while achieving the same level of flow-duration control
performance. Finally, based on whole lifecycle cost estimates, it
was determined that a real-time control retrofit of an existing
stormwater detention facility would be approximately three
times lower in lifecycle cost than the equivalent passive
alternative.42
KNOWLEDGE GAPS
Systems Thinking. While nascent, research on real-time
stormwater control is not limited by technology, but rather by a
much more fundamental need to understand the complex
spatiotemporal dynamics that govern water flow and quality
across large urban areas. One of the largest challenges with
existing stormwater solutions relates to their design as single
entities. This means that benefits achieved at a local scale may
often be masked or eliminated at the city scale if the
performance of an individual element is not designed in a
broader systems context.25,45 Perhaps the biggest benefit of
control relates to the ability to leverage real-time interconnection to guide the behavior of individual elements to achieve
city-scale benefits.
There is a need to build upon prior and ongoing research
efforts on best management practices (BMPs)20,29,46,47 to
understand how individual green and gray stormwater solutions
perform when stressed by varying climate, storms, and runoff
dynamics. Many studies focus on hydrologic control and
removal of solids and bacteria, but much work still remains to
be done to determine the impacts of these solutions to the
treatment of metals, nutrients and emerging contaminants. This
will require the expanded development of cheap and reliable
sensors for these pollutants. Furthermore, there is an urgent
need to fill a knowledge and measurement gap on the
interconnectedness of BMPs across various scales and runoff
dynamics (e.g., first flush vs peak flow). By improving the
understanding of stormwater networks as a function of scale, it
will then be possible to posit how very large systems (ten to a
hundred ponds, for example) should be controlled or tuned in
real-time to achieve a collective outcome.
Uncertainty. The role of uncertainty is rarely acknowledged
in the design of traditional stormwater systems, since it is
assumed that many transient system behaviors will average out
into a cumulative performance over time. The benefits of realtime control, however, are highly underpinned by uncertainties
related to weather forecasts, models, control algorithms, and
sensor measurements. Some elements of the system will always
remain unmeasured or not understood. Furthermore, many
control decisions will continue to be based on hydraulic
parameters, such as flow or residence time. Until reliable and
low-cost water quality sensors become available, water quality
control decisions will rely on statistical correlations or physical
■
SCALING UP
An insight into the scalability of real-time control is provided by
a large-scale control network that is presently deployed in
South Bend, Indiana.43 The network encompasses 100 km2 and
is comprised of 120 real-time flow and water depth sensors
(Figure 4), which share information every 5 min. The system
has been retrofitted with control valves located at nine CSO
regulators to modulate flow into the city’s interceptor line. The
control valves allow more water to enter the interceptor line
when conveyance capacity is available, while avoiding
surcharging the interceptor, which may cause surface flooding
or structural damage. The system operates by taking advantage
of excess conveyance capacity within the interceptor line, which
is driven dynamically by spatial or temporal features of specific
storms.
The distributed control strategy uses an agent-based control
scheme to optimize the water collection system, whereby each
infrastructure component trades its own storage or conveyance
capacity to other upstream assets, similar to traders in a stock
market.44 Even before the system was controlled, benefits were
achieved by means of monitoring alone. By isolating
maintenance issues in its first year of operation (2008), the
system helped the utility eliminate critical dry weather sewer
overflows, which were occurring an average of 27 times per
year. Overall, the control system reduced total sewer overflow
volumes from 2100 MGal to 400 MGal from 2006−2014
(Figure 4). Even after adjusting for total annual rainfall, a near
5-fold performance improvement (ratio of overflows to
precipitation) was achieved. While a reduction in E. coli
concentrations (443 cfu/100 mL to 234 cfu/100 mL) in the
downstream sewer locations was also observed, a more
comprehensive ecological study is warranted to study the
impacts of real-time control to E. coli removal mechanisms. It is
estimated that over one billion gallons of untreated sewer flows
were blocked from flowing into the river, suggesting that realtime control played a role in improving water quality.
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scale control and maintenance. Governance models must be
explored to facilitate cooperation and liability concerns. While
solutions to these concerns can build on successful models used
for ownership and operation of passive controls, they may
require further thought in their translation to real-time
controlled systems.
Beyond technical challenges, the ecosystem of municipalities
and engineering firms must adapt to accommodate real-time
control within a large umbrella of green and gray infrastructure
solutions. Broader community engagement is necessary to
facilitate dissemination and adoption of real-time stormwater
control. Compliance regulators, such as state and federal
environmental protection agencies, must be highlighted as
members of this community, since many cities are wary of
innovation because of perceptions that regulators will reject
nontraditional solutions. Environmental consulting firms,
municipalities, and researchers will need to acquire nontraditional skillsets, which span electrical engineering and computer
science. To help with this effort, a major initiative is presently
underway to organize an open-source consortium and share
reference implementations on real-time stormwater control
(http://open-storm.org). While open-source options for
sensing and control are alluring due to their perceived cost,
examples of holistic open-source approaches, which integrate
environmental science, technology and engineering design,
have yet to be developed. To that end, this consortium will
serve as a hub for reference applications, standards,
architectures, sensors, hardware and algorithms, to show that
it is well within the abilities of most academic groups,
municipalities and engineering firms to begin instrumenting
and controlling stormwater infrastructure.
models. It will be important to quantify the role of the resulting
“error bars” on the performance of real-time control.
As with many controlled systems, there may be an inherent
risk to infrastructure, private property, or even human life due
to poorly designed control algorithms. Since risk relates directly
to uncertainty, reliable and consistent real-time operations can
only be achieved by exhaustively quantifying the role of
uncertainty in control operations. Furthermore, even the best
controllers and sensors may only achieve marginal benefits if
storms cannot be predicted adequately, thus calling for the need
to begin investigating the value of weather forecasts in control
operations. Many other examples can be given, but studies
exploring the role of uncertainty have yet to be conducted.
OUTLOOK AND BROADER ADOPTION
Real-time control promises to revolutionize the management of
urban water quality by providing the ability to significantly
improve the operation of existing stormwater assets. As the
community of researchers grows, there will be a need to
develop baseline performance metrics, study sites, measurement platforms, and data sets. Research on stormwater capture
and direct use (reuse) has recently increased48 due to the
potential of reclaimed stormwater to serve arid regions. In
drought-prone regions of the U.S., where stormwater direct use
is becoming one of the few viable water recovery options,
sensing and real-time control will improve stormwater
extraction compared to static or natural treatment options.
Controlling the timing and magnitude of flows and improving
removal of contaminants before they reach the plant will also
result in a reduction in resources required for treatment in
combined sewer systems.
Outfitting stormwater infrastructure with sensors and digital
control systems introduces new opportunities for efficiency and
new risks of failure. Responsible use of these systems extends
beyond deployment, requiring ongoing effort to maintain trust
in the data produced and the integrity with which control
actions are followed. As with all Best Management Practices,20
standards will be required to facilitate broader adoption of realtime control and to assess the risks introduced by the use of
information sourced from these embedded systems. Future
standards may focus around data formats, sensor requirements
or actuator specifications, and will need to ensure interoperability between various sites. Failure to recognize, plan for, and
manage the ongoing cyber security risks introduced by the
distributed installation of sensors and actuators in stormwater
infrastructure will result in new risks to public health and safety,
which may undermine trust in broader efforts to deliver the
potential benefits of these technologies.
There will be a need to address regulatory compliance,
ownership, governance, and operational jurisdictions relating to
real-time controlled systems. Unlike existing deployments of
sensor and control systems in wastewater treatment, digital
stormwater infrastructure is deployed across a watershed,
outside of buildings staffed by an operations team. A key
tension relates to jurisdiction, both in terms of who owns the
infrastructure being controlled and which software system
provides this dynamic capacity. Many cities may only wish to
try retrofitting some sites, with the plan to augment their
systems over time as they see benefits. This raises the possibility
that many software systems may operate simultaneously and
interfere with a global goal. If control systems are deployed by a
spectrum of public and private stakeholders, they should
nonetheless interoperate to provide capacity for watershed-
■
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AUTHOR INFORMATION
Corresponding Author
*E-mail: bkerkez@umich.edu.
Notes
The authors declare no competing financial interest.
Biography
Branko Kerkez (Assistant Professor) and Brandon Wong (Graduate
Student) are part of the Real-time Water System Lab in the Department
of Civil and Environmental Engineering at the University of Michigan.
Cyndee Gruden is an Associate Professor at the University of Toledo,
Department of Civil Engineering. Dr. Matt Lewis is the CTO of
Michigan Aerospace, in Ann Arbor, Michigan. Dr. Luis Montestruque
(CTO), Ruben Kertesz and Tim Braun are employed at EmNet in
South Bend Indiana. Marcus Quigley (CEO), Alex Bedig and Owen
Cadwalader are employed at OptiRTC, while Aaron Poresky works
with GeoSyntec. Carrie Pak works at Clean Water Services in Hillsboro,
Oregon.
ACKNOWLEDGMENTS
Branko Kerkez, Cyndee Gruden, Matthew Lewis and Brandon
Wong are supported by the Great Lakes Protection Fund. We
acknowledge the CWS staff, including Richard Boyle, Jadene
Stensland and Andy Braun, Doug Schuh, and Jeff Van Note, as
well as the city of South Bend, including Gary Gilot, Eric
Horvath, Patrick Henthorn, Al Greek and Jack Dillon. All data
used to generate the figures in this paper are available at http://
open-storm.org/data
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