Question:
You are given the full text of a scientific research paper.

    Summarize the paper's key **research findings** in the style of a policy brief. Your response should:
    - Focus on the major empirical or model-based results
    - Present findings that are policy-relevant and supported by the study
    - Use concise, non-technical language with essential technical terms as needed
    - Avoid quoting directly or summarizing minor details

    Write a short, clear paragraph appropriate for the “Scientific Research Findings” section of a policy brief.

    Scientific Research Paper:
    Nature Cities | Volume 1 | October 2024 | 654–664
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nature cities
Article
https://doi.org/10.1038/s44284-024-00116-7
Connectivity in urbanscapes can cause
unintended flood impacts from
stormwater systems
Vinh Ngoc Tran
  1, Valeriy Y. Ivanov
  1
, Weichen Huang1, Kevin Murphy
  1,
Fariborz Daneshvar
  1, Jeff H. Bednar2, G. Aaron Alexander
  3, Jongho Kim4 &
Daniel B. Wright
  3
Urban flooding is intensifying worldwide, presenting growing challenges to
urban communities. We posit that most of the flood management solutions
currently employed are local in nature and fail to account for ways in which
the space–time connectivity of floods is exacerbated by built infrastructure.
We examine the 2014 flood in Southeast Michigan to identify key factors
contributing to urban flooding and explore the implications of design
choices on inundation. Findings reveal that stormwater infrastructure
that neglects flood spatial connectivity can be ineffective in mitigating
floods, leading to inundation even in the absence of local rainfall. Different
configurations of network connections—including interfaces with natural
channels—can significantly impact upstream surcharge, overflowing
manholes and inundation conditions. These results emphasize the need to
consider interconnectedness of flood processes in urban watershed systems
to mitigate limitations inherent in the design of flood control and warning
systems, to enhance urban flood resilience.
Climate change is increasing the likelihood of flooding1,2, with often
disastrous impacts, particularly in urban areas3,4. Flood-related global
economic losses reached a staggering $651 billion (US dollars) between
2000 and 20195. The United States has been particularly impacted, with
floods causing 1,782 fatalities and damages exceeding $102 billion,
affecting 99% of US counties since 20006–8. The growing impacts of
flood events across the United States and globally will continue to
rise, with losses projected to soar by a factor of 20 by the end of the
twenty-first century9. These increasing impacts are driven not only
by the increasing severity of extreme rainstorms, but also by rapid
urbanization10, booming population densities and the dramatic expan-
sion of highly connected transportation and infrastructure networks in
flood-prone areas11,12. Rapid urbanization has substantially altered the
natural water cycle through the proliferation of impervious surfaces,
inhibiting rainfall infiltration and resulting in elevated surface runoff13.
The disruption of natural drainage pathways has rendered urban areas
increasingly susceptible to inundation. Consequently, numerous major
cities globally have witnessed a rise in the frequency and severity of
flood events, as urbanization outpaces upgrades to flood mitigation
infrastructure (often referred to as stormwater systems)10.
Stormwater systems play an essential role in cities across the
world (Supplementary Fig. 1). Stemming from advances in science
and technology over past decades, a new generation of flood mitiga-
tion infrastructures, such as green-blue infrastructure (GBI) systems,
are perceived as the future of urban flood management, designed to
replace traditional ‘gray’ approaches14. GBI refers to an integrated
approach that combines ‘green’ infrastructure solutions, such as per-
meable pavements, rain gardens and green roofs, with ‘blue’ infra-
structure elements (or low-impact development), such as constructed
wetlands, detention ponds and restored floodplains. Despite the
Received: 5 December 2023
Accepted: 25 July 2024
Published online: 27 August 2024
 Check for updates
1Department of Civil and Environmental Engineering, University of Michigan, Ann Arbor, MI, USA. 2Macomb County Public Works Commissioner
Candice S. Miller, Clinton Township, MI, USA. 3Department of Civil and Environmental Engineering, University of Wisconsin-Madison, Madison, WI, USA.
4School of Civil and Environmental Engineering, University of Ulsan, Ulsan, South Korea.
 e-mail: ivanov@umich.edu

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reflect the ways in which drainage system interconnectedness
can create vulnerabilities through the aggregate ‘network effects’
of localized engineering solutions. Additionally, modeling tools
employed in engineering design practices are outdated and lack the
capability to describe the coupled dynamics between flows over the
land surface, through natural and artificial surface channels, and
through sewer systems. Similar deficiencies in design practices can
be found worldwide28–33, and exist despite the availability of more
modern modeling tools that are capable of performing sophisticated
simulations. These practices have impacted scientific research focus-
ing on the optimization of drainage systems, as most studies have relied
solely on simplified one-dimensional (1D) models that simulate water
conveyance within sewer systems, while neglecting interactions with
surface water (see Group 3 in Supplementary Table 1, which contains a
relevant literature review). Such model limitations may result in poorly
informed design of drainage systems and may compromise urban flood
resilience and prevention capacities4,34,35.
State-of-the-science urban flood modeling can accurately simulate
the motion of flood waves over the surface and within stormwater infra-
structure, while accounting for the complexities of surface conditions
(vegetation, roads, bridges, buildings and so on)36–38. Augmented by the
growing volumes of high-resolution geospatial data for urbanscapes,
it is now possible to explore flooding behaviors in urban environments
with far fewer simplifying assumptions. As pointed out already, previous
studies have primarily focused on model developments of specific
physical processes37 and have thus lacked comprehensive insights
regarding the connectivity of floods in urban domains as shaped by
natural drainage elements and gray infrastructure. Further research
is required to advance such understanding. By investigating flood
propagation through a complex urban environment with multiple
process interactions, our study provides novel insights into emer-
gent flood behaviors shaped by both natural and man-made drainage
systems. The knowledge gained and capabilities demonstrated in this
research could help to provide sustainable and resilient approaches
to addressing the intensification of global urban flood hazards.
Specifically, we investigate the following. First, what is the
nature of the complexity of flood connectivity in urban landscapes?
Second, can sewer systems exacerbate urban floods? We hypothesize
that (1) in certain conditions, connectivity via stormwater infrastruc-
ture can lead to flooding in a particular location, even in the absence
apparent merits of GBI (for example, eco-sustainability and climate-
change resilience)15,16, they are costly, require a longer implementation
period, and necessitate continuous maintenance to ensure efficient
drainage and retention capabilities, with a continued reliance on gray
infrastructure17,18. Therefore, gray infrastructure continues to be the
primary approach for mitigating urban flooding17,19,20.
The repeated damages caused by urban flooding have raised ques-
tions regarding the efficacy of costly drainage systems, whether gray
or GBI. For example, the United States spends $7–10 billion dollars
annually for the construction and renovation of stormwater systems,
with larger expenditures needed over the long term21. With the gene
rally poor state of aging infrastructure in the United States22, even
moderate rainfall events (a return period of ~10 years)23,24 can exceed
the capacity of drainage systems8. Although current inadequacies stem
partly from outdated stormwater design standards25,26, we posit that it is
also due to an insufficient understanding of ‘flood connectivity’ within
urban landscapes, defined as a complex interplay of flooding mecha-
nisms (Fig. 1) and drainage pathways within urban settings, arising
from the spatiotemporal distribution of hydraulic head (the term used
to describe the summed potential, kinetic and pressure energies of
stormwater flows). This flood connectivity encompasses the interac-
tions and interdependencies among runoff, surface and subterranean
sewer and drainage systems, rivers and other natural and built water
bodies, and infrastructure components (for example, buildings and
roadways) that can, by design or in an unintended way, convey storm-
water during floods. Although numerous studies have been conducted
in recent decades focusing on understanding urban flooding (Supple-
mentary Table 1 provides a detailed literature review), most previous
studies have focused on a few specific, isolated physical phenomena,
such as assessing the impact of infrastructure and rainfall on flooding,
or case-study-specific aspects of water exchange between drainage
systems and surface flows. Therefore, a comprehensive exploration
of flood connectivity in complex urban areas and a holistic synthesis
of process interactions remain to be explored.
Insufficient appreciation of flood connectivity can be demon-
strated by outdated and oversimplified stormwater system design
guidelines22,27. For example, the American Society of Civil Engineers
(ASCE) stormwater design guidelines were last revised in 2006
(GUIDE2006 hereafter)28. While offering an extensive set of drainage
network design criteria, the GUIDE2006 guidelines do not adequately
Pipe
River
Urban watershed of interest
Nearby watershed
Past climate median water level
Future/current median water level
Vadose zone
Flooding
Manhole
Flow direction
Outfall
Groundwater
[FC3]
[FC1]
[FC2]
Fig. 1 | Illustration of key urban flooding concepts. A cross-section of an
urban watershed, depicting residential areas (buildings), green spaces (trees),
an open channel (river cross-section) and an underground stormwater system
(manholes, pipes and outfalls). Outfall discharging into the river connects the
urban stormwater network with a larger drainage system. The arrows indicate
potential flow directions of water. Black arrows indicate surface flow directions,
blue arrows show flows from the ground surface into the sewer system and out
of the outfall locations (for example, open channels), and red arrows depict
backwater occurrences when open channel flows reverse into the drainage
system through outfalls, potentially causing surcharging at manholes. The blue
wavy line represents the normal water level (historical conditions), and the red
wavy line the current or future river water level due to the influence of climate
change and urbanization. FC1 represents flooding due to high water levels
in the river, causing overflow and inundation of the surrounding floodplain
(river-induced or ‘fluvial’ flooding). FC2 represents flooding caused by intense
rainfall and insufficient natural or engineered drainage capacity of the area
(rainfall-induced or ‘pluvial’ flooding). FC3 represents flooding caused by the
hydraulic connection of stormwater infrastructure with stream channels and
other drainage basins. FC3 flooding can be caused by water accumulation due to
either fluvial or pluvial flooding or backup flow from the river through drainage
pipes, leading to surcharge at the manholes (infrastructure-induced flooding).
FC, flooding concept.

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of ‘local’ precipitation, and (2) improper positioning of stormwater
drainage outlets and curb inlets (called manholes hereafter) can
exacerbate flooding.
Results
Flood of 2014 in Southeast Michigan
We present a case study that demonstrates how engineering design
infrastructure interacts with runoff drainage processes at different
spatial scales of origin, including local scales, typical of engineering
design, and the larger scales that connect upstream and downstream
drainage conditions. The study was carried out in relation to a highly
urbanized watershed in Southeast Michigan that has multiple con-
ventional elements of gray infrastructure designed to deliver storm
runoff to nearby channels. We designed the study by focusing on a
single extreme storm on 11 August 2014 (referred to as STORM2014
hereafter) that struck Southeast Michigan and caused catastrophic
flooding. The storm was termed a 1-in-100-year event, with rainfall
totaling between four and six inches in four hours39. The economic
costs of the storm were substantial, with a total of over $1.1 billion dam-
ages to over 118,000 homeowners and businesses40, translating to the
largest flood-related disaster that the Federal Emergency Management
Agency (FEMA) identified during 2014. The area was declared a federal
disaster zone in that September40. The study focuses on the Warren
area, which is situated within the confines of the inner-ring Detroit
suburb of Madison Heights and has an area of ~8.8 km2 (Fig. 2a,b). This
region is considered to be a part of the Red Run watershed, dissected
by a network of diverse drainage elements such as open channels (for
example, Bear Creek; Fig. 2b and Supplementary Fig. 2), culverts, under-
ground stormwater conveyance systems, and their outlets (outfalls;
Fig. 2b,c). In contrast to this drainage complexity, the National Flood
Hazard Layer from FEMA (Fig. 2b) shows 1% or 0.2% annual probability
floodplains mainly confined to the vicinity of Bear Creek41. Nonetheless,
throughout the STORM2014 event, the majority of the Warren area,
comprising residential neighborhoods and major roadways, experi-
enced profound inundation, as depicted in the simulation results in
Fig. 2d. Specifically, 58% of the domain area was affected by severe
flooding, with water levels exceeding 0.5 m reported by eyewitnesses
in the area of the General Motors production facility (Supplementary
Fig. 2b). Because the area is typical of urban watersheds connected to
a larger drainage system, this domain was selected for an analysis of
42° 30' N
42° 35' N
83° 10' W
83° 05' W
83° W
a
Esri, HERE, Garmin, NGA, USGS, NPS
 2 mi
 5 km
MI
b
c
Outfall 1
Outfall 2
Red Run watershed
Warren area
Building footprint
1% Annual chance flood
0.2% Annual chance flood
River/open channel
Culvert
Pipe
Inlet
Outlet
Manhole
Outfall 1
Outfall 2
d    HR-Integrated
0
0.4
0.8
1.2
1.6
2.0
e    NR-Integrated
0
0.4
0.8
1.2
1.6
2.0
f    HR-Controlled
0
0.4
0.8
1.2
1.6
2.0
g    ∆h (d–f)
–0.2
–0.1
0
0.1
0.2
Fig. 2 | Flood maps of Warren, Michigan for the STORM2014 (11 August 2014)
event. a,b, The Warren area identified by the dashed line in b is situated in the
Red Run watershed (a) in southeastern Michigan. The orange and green dots in
a indicate the inlet and outlet locations of the watershed, respectively, with the
inlet being the discharge point of the George W. Kuhn Retention Treatment Basin.
The Warren area shown in b includes buildings (gray polygons), open channels
(for example, Bear Creek), culverts (dark blue lines), underground pipes
(green lines), manholes (brown dots) and two outfalls (green and red squares).
c, Photographs of the outfalls. The blue and orange areas in b represent the
regions at risk of flooding with a 1% and 0.2% annual chance flood, respectively,
provided by FEMA. d, Simulation results for the maximum inundation depth
of the flood event using STORM2014, where the land-cover conditions and
culvert systems closely reproduce actual drainage conditions, allowing for both
drainage and backwater phenomena at the outfalls (referred to as the ‘integrated’
outfall condition). The rainfall is uniformly distributed across the entire Red
Run watershed in this scenario (referred to as homogeneous rain, HR). e, The
maximum inundation depth results for a simulation configuration that mimics
that of d, but with rain occurring only outside the Warren area (referred to as
no-rain, NR). f, The maximum inundation depth results using HR, but assuming
that all water collected by storm sewers discharges into the open channel; that
is, the backwater effect in drainage pipes is not enabled (referred to as the HR
‘controlled’ outfall). g, The difference of the flood depths in d and f. The locations
of surcharging manholes in d–f are shown with brown dots. The values on the
color bars in d–g are expressed in meters.

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the interactions of urban flooding mechanisms. To replicate historical
flood events, we gathered an extensive array of data of high accuracy
and spatial resolution that influence the flood response in this area,
including topography, land use/land cover, road networks, buildings,
stormwater systems and open channels. Notably, we also conducted
field surveys to validate and refine the data, particularly regarding the
locations of drainage outfalls, where strong interconnections exist
between the drainage system, open channels and ground surfaces.
Further details on the specific data used and the model configurations
are provided in the Methods.
No local rain, but flooding
The design of the stormwater system in GUIDE2006 is based on rainfall
scenarios that represent different levels of precipitation that can cause
flooding if the stormwater infrastructure is unable to accommodate the
influx of runoff quickly enough. In other words, GUIDE2006 assumes
that rainfall occurrence is a prerequisite for flooding. However, we show
that even in the absence of rainfall in local runoff source areas, runoff
from heavy rain outside their boundaries can still cause inundation
due to the spatial hydraulic connectivity facilitated by the stormwater
infrastructure. The results in Fig. 2e highlight that a substantial portion
of the studied area is susceptible to flooding: water exceeds 0.1 m in
depth over ~75% of the area, with 27% having a depth of over 0.5 m. Such
an outcome indicates how the spatial connectivity of flood processes
can alter local perception of flood vulnerability conditions. Specifically,
scenarios with intense precipitation only in neighboring areas must
be considered when assessing flood risks and planning mitigation for
a region of interest, even in the absence of rainfall occurring locally
within that area.
One source of flooding in this ‘no local rain’ situation can be
attributed to the increased water levels in open channels causing the
well-known phenomenon of fluvial flooding (Fig. 1, FC1). Under this
mechanism, the water levels exceed the confines of the riverbanks,
and streamflow spills into floodplain areas. Additionally, simulated
results show that runoff from the surrounding land areas (where rain-
fall occurs) can flow along roadways into the Warren area, leading to a
localized pluvial flooding (denoted by FC2 in Fig. 1). A third likely cause
identified is related to backwater surcharge through the stormwater
pipe network and the catch basins in source areas. Specifically, extreme
water levels within open channels induce backwater flow at stormwater
outfalls, causing a substantial surge of reverse flow into topographically
upstream source areas of the drainage system, leading to surcharge at
manholes and flooding in areas that did not experience rainfall. This
flooding mechanism reflects concept FC3—infrastructure-induced
flooding that can impact areas distant from open channels.
Indeed, the results of explicit modeling of flood connectivity pre-
sent evidence that stormwater networks design with a ‘local mindset’
may fail severely. Not only can they become inadequate to accomplish
their designated task to drain stormwater, but they can even reverse
stormwater systems’ work and aggravate flooding due to sewer sur-
charge in residential areas that have not experienced rainfall (Fig. 2e).
The spatial connectivity of flooded urbanscape may thus lead to the
hydraulic head distribution expansion of inundation into regions
that are distant from a river or do not experience extreme rainfall.
No backwater flow into outfalls, but heavier local flooding
The location, invert elevation and size of outfalls, as well as their associ-
ated sewer pipes, play a major role in determining the drainage capacity
of a stormwater collection system. As stipulated in GUIDE2006, the
entire volume of generated runoff within the system is assumed to
flow out freely from outfalls. Although this permits an assessment of
the maximum drainage capacity, such an assumption may underesti-
mate the influence of flood connectivity at outfalls, for example, the
distribution of hydraulic head in open channels or storage tanks. If the
hydraulic head at the outfall section generated by the passing flood
wave in the channel exceeds the hydraulic head in the stormwater sys-
tem, then backwater flow commences and channel water will flow into
the sewer system, impeding stormwater drainage and possibly even
causing surcharge at upstream locations. The lack of consideration
of flood connectivity in complex urbanscapes may lead to incorrect
assessments of system drainage capacity and therefore an inaccurate
representation of possible inundation levels at sites requiring flood
protection.
We examined how accounting for flood connectivity or lack
thereof may impact flood levels. Specifically, the former case (referred
to as ‘Integrated’ outfalls) accounted for the hydraulic coupling of water
levels in open channels at the outfalls, mimicking the real-world flood
condition to the best of our abilities. In the latter case (‘Controlled’),
all stormwater at the outfalls is assumed to discharge freely into the
receiving channel of Bear Creek; that is, the outflow is controlled to
have the rate of a pipe with a free overall downstream condition, not
impeded by the presence of the receiving water body. Comprehensive
hydrodynamic modeling of these two distinct scenarios of outfall
functioning reveals peculiar results. Notably, with the outfalls hydrauli-
cally disconnected (the ‘Controlled’ case) from the rest of the flooded
watershed, the resultant flooding is more extreme than that of the
integrated outfall case, which facilitated backwater effects (Fig. 2d,f).
Specifically, in areas near the outfalls at the Bear Creek channel, inun-
dation levels for the ‘Controlled’ case exceeded the ‘Integrated’ case
by over 0.1 m (Figs. 2g and 3a–c). This result can be explained by the
considerable drainage from the ‘Controlled’ sewer system discharging
into the open channel: water levels at the outfall sites thus tend to be
higher and cause more severe flooding in nearby areas. In contrast,
in the more realistic ‘Integrated’ case, if the water level in the channel
exceeds the hydraulic head of the stormwater system at the outfall
location, backwater flow will occur and water from the open channel
will flow into the sewer system. This results in lower water levels in near
outfall areas experiencing backwater, leading to reduced flooding in
the ‘Integrated’ case. Conversely, with large discharges entering the
sewer system, upstream areas (such as in the southern region) can
be impacted when the sewer’s rainwater intake capacity is reduced,
potentially increasing flooding due to slower drainage.
Neglecting the connectivity between a sewer system and adjacent
flooded areas and consequent backwater effects can overestimate
flood levels. This can potentially result in poor design of the number,
positioning and dimensions of manholes, pipes and outfalls. Larger
sewers and outfall dimensions can facilitate rapid drainage, yet they
can also allow rapid backflow. As shown in Fig. 3d,e, a large outfall (with
a diameter of 3.8 m) can discharge high flows (up to 40.9 m3 s−1) in the
‘Controlled’ case, whereas the backflow into the sewer can be up to
63.5 m3 s−1 in the ‘Integrated’ case. The smaller outfall (with the diameter
of 0.3 m) had lower discharge and backflow (up to 9.7 and 16.7 m3 s−1 for
the controlled and integrated cases, respectively).
It is still far from being standard engineering practice to realisti-
cally simulate flooding with coupled overland- and pipe-flow models,
such as the one used in this study (results shown in Fig. 2). Instead,
engineers mainly rely on simplified stormwater models (for example,
1D channel flow models coupled with conceptual/lumped rainfall-
runoff modules28,30–33,42,43. In such formulations, the total discharge
from outfalls is assumed to entirely ‘disappear’ (or become accommo-
dated by a stormwater storage such as pond or tank) rather than flow
into open channels. Water levels in open channels (at outfall locations
or elsewhere) will thus remain unaffected and no backwater effect
can occur. The assumption that stormwater discharge vanishes after
discharging through outfall can clearly have a strong impact on flood
level assessments, and can misjudge drainage system performance.
Specifically, inundation levels are broadly underestimated across the
study area (especially near open channels) by up to 20 cm, as compared
to the more real-world ‘HR-Integrated’ case (Supplementary Fig. 3a,b).
These estimates indicate that even assumptions for outfalls can lead to

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significantly different assessments of stormwater system performance.
This highlights that the lack of appreciation of flood connectivity may
result in poorly informed stormwater system designs.
More overflowed manholes, but lower surcharge amounts
Different outfall configurations should produce distinct differences
in the surcharge in catch basins. In principle, when the sewer system
simultaneously receives a substantial influx of water from both the
ground surface through manholes in source areas and from the open
channel through outfalls, this can induce higher surcharge and a larger
number of surcharging manholes in low-lying areas (that is, where low
hydraulic head is expected). The case study results only partially align
with this conjecture, showing that accounting for backwater effects (the
‘Integrated’ case) results in surcharging in a slightly larger number of
manholes (27 versus 25), as depicted in Fig. 2d,f. Figure 3f,g indicates that
the surcharge rates in manholes near the outfalls are comparatively lower
as compared to the ‘Controlled’ case (for example, manholes 1 and 2). The
differences in the surcharge rates were negligible for manholes further
from the outfalls or at higher elevations (manholes 3, 4 and 5; Fig. 3h–j).
The higher surcharge rates at manholes (for example, manhole 1,
Fig. 3f near outfall 2) in the ‘Controlled’ case are attributed to the
higher water volumes ‘injected’ into the flooded area from a nearby
draining outfall. Such an ‘injection’ leads to larger volumes of water
inflow into the adjacent manholes, for example, manhole 1 and nearby
manholes (Fig. 2f) and, if paired with a limited drainage capacity of the
outfall, (for example, outfall 2, which has a small 0.8-m diameter), can
exacerbate the surcharge (Fig. 3f,g). These results demonstrate how
outfall configuration/drainage capacity can impact an assessment of
local flooding conditions. This underscores the complexity and vital
importance of flood connectivity for understanding the performance
of stormwater infrastructure. Ignoring flood connectivity between land
surfaces, infrastructure and open channels in urban areas can lead to
an incomplete understanding of how water moves through a complex
urban flooding environment. Failing to account for this interconnect-
edness misinforms engineering design and flood mitigation strategies.
Discussion
Existing evaluations of urban flooding have so far neglected to account
for the interactions of complex mechanisms associated with flooding
caused by human-engineered stormwater networks22,23,37,38,42,44,45. Our
results demonstrate that the interaction of flooding types—facilitated
by the connectivity created by human-engineered infrastructure—is an
important aspect of urban flood dynamics. Ignoring such a connectiv-
ity may lead to intensified urban floods, especially in regions where
river-induced floods meet stormwater infrastructure (Fig. 2d,f), even
when rainfall does not occur over the area served by the infrastruc-
ture. To expand the applicability of the study findings to a broader
range of flooding cases, we conducted simulations for the 14 largest
events (including STORM2014; Supplementary Fig. 4) over the past
15 years in the Warren study area (rainfall return periods ≥1 year) with
outfalls that have hydraulic connection with the receiving channel,
thereby mimicking real-world conditions. The simulation maximum
flooded areas are shown in Fig. 4a. Even for the annual flood (that is, a
return period of 1 year), the total area flooded to a depth of over 0.5 m
comprises up to 7% of the total area of the Warren area (excluding the
area of open channels). With the increase in rainfall intensity (shown in
a
Outfall 1
Outfall 2
Manhole 1
Manhole 2
Manhole 3
Manhole 4
Manhole 5
b    Outfall 1
5
10
15
20
180
182
184
186
188
Elevation (m)
c    Outfall 2
5
10
15
20
180
182
184
186
188
Outfall elevation
0.2% annual chance flood
1% annual chance flood
2% annual chance flood
10% annual chance flood
HR-Integrated
HR-Controlled
d    Outfall 1
5
10
15
20
–100
–50
0
50
Discharge (m3 s−1)
e    Outfall 2
5
10
15
20
–100
–50
0
50
f    Manhole 1
5
10
15
20
–5
0
5
10
15
Discharge (m3 s−1)
Discharge (m3 s−1)
g    Manhole 2
5
10
15
20
–5
0
5
10
15
h    Manhole 3
5
10
15
20
–5
0
5
10
15
i    Manhole 4
5
10
15
20
–5
0
5
10
15
j    Manhole 5
5
10
15
20
–5
0
5
10
15
Hour
Hour
Fig. 3 | Simulated water levels and flow rates at the outfalls and surcharged
manholes for STORM2014. a, Map depicting the locations of outfalls and select
manholes for the results shown in b–j. b,c, Simulated water surface elevations
at two outfall locations (see legend). The gray region represents the invert
(pipe bottom-to-top) elevation range of the outfalls. Colored lines represent
the floodwater levels at probabilities ranging from 10% to 0.2% annual chance
flood as provided by FEMA. d,e, Outflow (positive values) or inflow (negative
values) rates at the outfalls for the HR-Integrated and HR-Controlled scenarios.
f–j, Overflow (positive) and drainage (negative) rates for five manholes, indicated
by circles in a.

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Fig. 4b for STORM2014), the area of severe flooding and the flood level
also increase (Fig. 4c). It should be noted that the drainage system
in this area is designed to handle runoff resulting from a 10-year
storm, with the assumption that all rainfall will be converted to
runoff and flow into the drainage system46. However, in reality, not all
runoff efficiently concentrates in the drains due to the retention of
some water in low-lying areas, causing localized flooding.
We also carried out simulations for all events to evaluate the
impact of the stormwater system on flooding under varying rainfall
conditions and outfall configurations, in the same fashion as was
done in the simulation configurations for STORM2014 (Supplemen-
tary Figs. 5–17). Generally, the rainfall magnitude certainly influences
the performance of the stormwater system, with fewer instances
of manhole overflow occurring under lower rainfall intensities. We
still observe the same flooding phenomenon implied by the flood
connectivity. For instance, the number of overflowing manholes in
the HR-Integrated case is higher than in the HR-Controlled case. The
case of rainfall in the watershed outside of the Warren area can still
cause flooding and overflow at several manholes in that area. Overall,
simulation results for 14 rainfall events of different magnitudes
corroborate the persistence of the analyzed flood dynamics in the
case study urban area caused by flood spatiotemporal connectivity.
Another observable outcome is the rapid onset of flooding after
rainfall. As depicted in Fig. 4c, impactful flooding with inundation
depths exceeding 0.5 m occurs within a narrow time window following
the most intense precipitation (initiating at hour 15:00), within 0.5–1 h.
This signifies that to mitigate flooding, the drainage system must not
only store substantial rainwater volumes but also drain rapidly to
minimize local water accumulation. However, the influx of water from
the surrounding areas or backwater effects at outfalls can exceed pipe
and manhole conveyance capacities and thus cause drainage ‘bottle-
necks’. Failure to adequately consider flood connectivity can lead to the
design of infrastructure (for example, massive underground storages)
that may fail to counter flooding (see results in Supplementary Text 1).
Optimization-based approaches are probably required to alleviate the
issue of such drainage ‘bottlenecks’.
The recognition of the potential hazard imposed by flooding due
to connectivity, even in the absence of rainfall in the design area, is an
important but overlooked reality of urban stormwater systems. It is
noted that most design guidelines worldwide28,30–33 rely on scenarios
with only ‘direct rainfall’ occurring in the area of interest (often uni-
form), without considering influx/outflux from/to nearby intercon-
nected areas. With flooding waters contributed by sources other
than direct rainfall, assessments of drainage capacity can be highly
uncertain. Similar to above, stormwater system design may require
optimization of its configuration.
In the United States, FEMA maps require further updates in
flood-prone areas, as already highlighted in previous research47–49.
The current perception is that FEMA’s hazard maps, which have not
been updated for over a decade, are outdated due to climate change
and land-use alterations41. Critically, we show here that FEMA maps
are further outdated because the methods (modeling and flood con-
cepts) used to generate them do not consider the complexity of flood
connectivity, especially in urbanscapes. Hence, flood risk assessments
can become particularly unreliable for urban zones where the different
flood processes interact (Fig. 1), compromising their utility. For
the specific case study considered here, the simulated water levels
during STORM2014 at the two discussed outfall locations were higher
than what FEMA maps show for the 0.2% annual flood probability
(a 500-year return period). This was also confirmed by eyewitness
accounts. This demonstrates the necessity for more accurate
models to mimic the spatial interactions of flood mechanisms across
rivers, surfaces, drainage systems and other infrastructure types.
The execution of this study is not without limitations. First, the
hydrodynamic model employed, while advanced and sophisticated, is
subject to inherent uncertainties arising from physical process simpli-
fications, parameterizations and input data quality. Second, the use of
crowdsourced data, such as images from the internet, to estimate flood
depths at a few locations introduced subjectivity and uncertainties into
the model evaluation process. This arises from the potential misalign-
ment between the timing and locations of the crowdsourced data with
the simulation configuration. Third, the study focuses on a single event,
and therefore carries the signature of the specifics of this case study.
The findings have been generalized to highlight study broad-level impli-
cations, but other urban areas with differing topographic, hydrological
and infrastructure characteristics will require similarly detailed analy-
sis to understand the impacts of flood space–time connectivity. These
limitations underscore the necessity for further research with diverse
case studies and the requirement for observations of inundation depth
data and drainage system operational data. Notwithstanding these
a
0
2
4
6
8
Flooded area (km2)
0
0.5
1.0
1.5
2.0
Inundation depth (m)
P = 1.0-year
P = 1.8-year
P = 1.9-year
P = 3.2-year
P = 4.1-year
P = 4.1-year
P = 6.1-year
P = 6.6-year
P = 7.3-year
P = 7.8-year
P = 8-year
P = 21.3-year
P = 31.9-year
P = 110.3-year
c
11 Aug, 6:00
11 Aug, 12:00
11 Aug, 18:00
12 Aug, 00:00
2014
0
2
4
6
8
Flooded area (km2)
[0–0.1] m
(0.1–0.5] m
(0.5–1] m
(1–1.5] m
(1.5–2] m
(2–5] m
b
0
10
20
Rainfall
(mm per 15 min)
Fig. 4 | Relationship between flood inundation and flooded area.
a, Relationship between the flooded Warren area and the maximum flood level
calculated for the 14 largest rainfall events for the Warren area between 2009 and
2021 (Supplementary Fig. 4). The legend indicates the rainfall frequency (return
period P) for each event, which is calculated based on the maximum rainfall
within 3 h and uses the intensity–duration–frequency (IDF) curve provided by
NOAA. b,c, The rainfall time series (b) and flooded area for six flood levels (c)
during the STORM2014 event (the return period is 110.3 years). In the legend of
c, parentheses indicate exclusion of the endpoint and square brackets indicate
inclusion of the endpoint.

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limitations, this Article presents a novel contribution by accentuat-
ing the importance of considering flood connectivity in urban flood
mitigation strategies and the potential shortcomings of traditional
stormwater management approaches that neglect the interconnected-
ness of flood processes.
In summary, urban flooding is a result of complex interactions
between precipitation, terrain and land use, surface hydrology and
human infrastructure systems. Disentangling the complex interac-
tions that flood connectivity produces in urbanscapes is imperative for
improved urban flood resilience and mitigation. By explicitly analyzing
such interactions for a case study with documented flooding issues,
this work emphasizes that it is vital to distinguish the distinct mecha-
nisms of fluvial, pluvial and infrastructure-induced flooding, so that
better engineering solutions can be developed. Holistic perspectives
encompassing the complete connectivity between all facets of the
urban flooding system, rather than compartmentalized viewpoints,
need to be prioritized as cities strive to enhance their flood resilience
and sustainability.
Methods
Case study and dataset
The main study area, Warren, encompasses the urban domain of the Red
Run watershed located in southeast Michigan (Fig. 2a,b). This is a com-
plex urban area with a substantial proportion of impervious surfaces.
The land-use cover obtained from the National Land Cover Database
2019 (NLCD, https://www.mrlc.gov) indicates a predominant presence
of developed areas with buildings, driveways, pavements and parking,
with an impervious surface ratio reaching 94.48% (Supplementary
Fig. 18). The terrain in this region is relatively flat, with a northward
slope adjacent Bear Creek. According to FEMA reports, some Warren
areas fall within the 100- and 500-year flood zones. To ensure a
realistic simulation, a substantial amount of digital and field obser
vational data were collected to set up the model accurately.
The overall simulation domain encompasses the entire Red Run
watershed in which the Warren region is nested (Fig. 2a). This was done
to ensure the modeling of spatial flood processes, such as surface
inflows into the Warren area from other sub-basins and the simulation
of flood waves in the Red Run River and its tributaries and their impact
on the Warren area. High-resolution elevation data (0.6-m resolution)
were used to set up the overland flow model (Lidar Point Cloud
data kindly provided by the USGS, https://apps.nationalmap.gov/
downloader/) (Supplementary Fig. 19a). Land-use and land-cover data
with a 30-m resolution obtained from the NLCD were used to estimate
roughness parameters for the model. Similarly, impervious surface
data with a 30-m resolution, also derived from the NLCD, were used
to estimate infiltration parameters. It is worth noting that other areas
within the Red Run watershed also exhibited high impervious surface
ratios, ranging mostly between 47% and 95%.
Infrastructures (buildings and stormwater systems) play a crucial
role in surface flow routing and water drainage of the urbanized
areas. The most recent building footprint data were obtained from the
Southeast Michigan Council of Governments (https://maps.semcog.
org/BuildingFootprints), encompassing a total of 2,128 buildings
within the Warren area (Fig. 2b and Supplementary Fig. 20). Data on
the stormwater system, including manholes, outfalls and pipes, were
collected and compiled in collaboration with Macomb and Oakland
counties. A total of 21 culverts, 3,417 pipes, 3,393 manholes and
75 outfalls were identified and incorporated into the simulation for
the entire Red Run watershed (Fig. 2a,b and Supplementary Fig. 2).
The rainfall data were extracted from the Detroit City Airport
weather station, which is part of the Automated Surface Observing
Systems network. In addition to the STORM2014 used primarily in
our analysis, data for 13 other major rainfall events from 2009 to 2021
were also collected to simulate and assess the impact of variability in
rainfall conditions on flooding. All rainfall events were collected over
a duration of 24 h, with rainfall measurements taken at 1-min intervals
and processed into 15-min intervals (Supplementary Fig. 4). Flow data
at the inlet location (Fig. 2a) of the Red Run watershed were provided
by the Clinton River Watershed Council upon request.
Field support for correcting outfall characteristics
Outfalls play a crucial role in the stormwater system, as they are strate-
gically designed to receive and discharge the water volume collected
by the manholes, minimizing the risk of flooding. To ensure the closest
possible simulation to reality, field observations were conducted on 21
January 2023 to refine the design, location and functionality of the two
important outfalls in the Warren area (Fig. 2b,c). Both outfalls are situ-
ated in the northern part of Warren, with their discharge gates flowing
into Bear Creek. These are open-type outfalls that allow water from Bear
Creek to flow inside the stormwater pipes if the hydraulic head in the
channel is higher than the hydraulic head at the outfall locations. The
field measurements included the width of the outfalls, invert and crown
elevations, and the bankfull width of Bear Creek around the outfall area.
Urban flood modeling
A coupled model integrating complex hydrologic, hydraulic and
morphologic processes, which has been verified in previous case
studies, was used in the research as the high-fidelity urban flood
model50,51. The hydrology module, the TIN-based real time integrated
basin simulator (tRIBS), can simulate various hydrological processes
such as canopy interception, evapotranspiration from the bare soil and
canopy, vertical and lateral moisture fluxes in the subsurface, and diverse
runoff generation mechanisms (for example, saturation excess, infiltra-
tion excess, perched subsurface stormflow and groundwater exfiltra-
tion) with appropriate inputs of meteorological data, topography,
land-use and soil-type data. Taking into account these hydrologic
processes enabled the model to simulate the hydrodynamics of
overland flow (overland flow model, OFM52), relying on physically
modeled wave velocities within a domain of arbitrary geometric con-
figuration. The OFM model solves the full form of the 2D Saint-Venant
equations (that is, the shallow water equations), which are derived
by depth-integrating the Navier–Stokes equations. The governing
equations consist of a continuity equation and two momentum
equations for two perpendicular horizontal directions:
∂U
∂t + ∂E
∂x + ∂G
∂y = S
where U is vector of flow variables, E and G are the flux terms in the
x and y directions, respectively, and S is the source vector:
U =
⎡⎢⎢⎢
⎣
h
uh
vh
⎤⎥⎥⎥
⎦
, E =
⎡
⎢
⎢
⎢
⎣
uh
u2h +
1
2 gh2
uvh
⎤
⎥
⎥
⎥
⎦
, G =
⎡
⎢
⎢
⎢
⎣
vh
uvh
v2h +
1
2 gh2
⎤
⎥
⎥
⎥
⎦
,
S =
⎡
⎢
⎢
⎢
⎢
⎣
i
−gh
∂zb
∂x −CDu√u2 + v2
−gh
∂zb
∂y −CDv√u2 + v2
⎤
⎥
⎥
⎥
⎥
⎦
where h represents flow depth, u, v are the flow velocities in the
x- and y-axis directions in the Cartesian system of coordinates, g is
the gravitational acceleration constant, i is the net runoff production
rate, zb is the bed elevation and CD = gn2h−1/3 is the surface drag
coefficient, where n is the Manning roughness coefficient. Detailed
descriptions of the numerical solution with the finite-volume method
are available in ref. 52.
The solution appears to be feasible for many scenarios involving
overland flow; however, due to numerical considerations, it is necessary

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to constrain the time step of the time-explicit finite-volume method
by the mesh’s smallest cell area, thus presenting a distinct compu
tational problem. For instance, in high-resolution applications, the
typical time step is of the order of ∼𝒪𝒪 (10−1) s, with a cell size of ∼𝒪𝒪
(101) m. Alternately, by using a time-implicit numerical scheme for
solving the shallow water equations, limitations on time-stepping are
fewer and the overall number of solution steps is reduced, generating
a potential computational benefit. However, this is limited to no greater
than one order of magnitude due to the increased need of iterations
within each time step to handle transient flow situations52.
The EXTRAN module of the Storm Water Management Model53 is
fully coupled with tRIBS-OFM for simulating a stormwater drainage
system. EXTRAN is a 1D dynamic sewer network model based on 1D
Saint-Venant equations. Water transport in conduits and nodes is
calculated based on the continuity and momentum equations, written
respectively as
∂A
∂t + ∂q
∂l = 0,
∂q
∂t +
∂(q2/A)
∂l
+ gC ∂H
∂l + gCSl + gAel = 0
where l is the distance along the conduit, C is the cross-sectional
area of the conduit, A denotes the cross-sectional area of the conduit,
q and H are the flow rate and hydraulic head, respectively, of water in
the conduit at time t, Sl denotes the friction slope of the conduit and
el is the local energy loss per unit length of conduit.
Surface water calculated from the OFM can move into the sewer
network through manholes; conversely, if the flow of the pipe network
exceeds its drainage volume, a reverse (surcharge) flow occurs and
becomes the source term for surface water. Drainage refers to the flow
of surface water to the manhole, and surcharge refers to the reverse
flow of the flow from the manhole to the surface. These bidirectional
discharges, drainage and surcharge, can be calculated from the weir
or orifice equations by comparing the hydraulic head at the manhole,
the ground elevation, and the water depth of the surface water. Specifi-
cally, if the hydraulic head at the manhole (H) is lower than the ground
elevation (zb), that is, H < zb, drainage, qd can be computed with a weir
equation:
qd = cwwwh√2gh
where cw is the weir discharge coefficient for the manholes, and ww
is the width of the weir crest. Conversely, if the hydraulic head at
the manhole rises and is greater than the topographic elevation of
the ground surface (but the hydraulic head at the manhole is still less
than the surface water level), that is, H > zb, drainage can be calculated
by the orifice formula:
qd = coAm√2g (h + zb −H)
where co is the orifice discharge coefficient for the manholes, and Am
is the area of the manhole mouth. Lastly, if the hydraulic head at the
manhole exceeds the surface water level, that is, H > (h + zb), the
surcharge, qsur, can be calculated according to the orifice formula:
qsur = coAm√2g (H −h −zb)
Mesh generation
This research necessitates sophisticated mesh generation to ensure
the most accurate and robust simulation outcomes. The intricate pro-
cess of mesh generation that considers the complex geometry of the
urban environment is summarized visually in Supplementary Fig. 21.
Primarily, the Hydrology Analysis Tools of the ESRI ArcGIS Package
are used to delineate the watershed area (183.6 km2) starting with the
high resolution of 0.6 m. Subsequently, the ArcGIS tool ‘Raster2TIN’
is used to construct a triangulated irregular network (TIN) from the
designated watershed boundary. The TIN is capable of representing
the terrain’s geometrical structure using substantially fewer nodes
than the original raster-based landscape representation.
In this study we focused on the Warren area (Supplementary Fig. 19b),
and thus divided it into cells of a considerably smaller size than
those outside the region. We established minimum and maximum
grid resolution sizes of 1 and 100 m2, respectively, inside the area
of Warren, and in the outer parts, the maximum cell size could
not exceed 5,000 m2. This refinement of the resolution helped
reduce the number of nodes and generated cells, thus alleviating the
computational burden. Note that the Warren area does not correspond
to a headwater catchment—that is, surface flows and subsurface pipe
flows can enter this rectangle-shaped domain from all of its sides.
We incorporated building footprints into the TIN to account
for the impact of buildings on flood wave propagation through the
urbanscape. Initially, the building footprints were simplified using the
‘Simplify Polygon’ tool in ArcGIS, ensuring that the minimum length of
each footprint polygon edge was 5 m. These simplified footprints were
then merged with the TIN to identify the triangle cells representing
buildings, and the edges coinciding with the footprint polygons were
designated as walls. Supplementary Fig. 21 provides an illustration
of the process for the Warren region to demonstrate this procedure.
The simplification of building footprints was necessary for two main
reasons: (1) to prevent the generation of very small triangles (this
constraint was imposed to maintain an appropriate time step in the
finite-volume method implemented in OFM for simulating overland
flow) and (2) to reduce the number of cells in the overall mesh repre-
senting the watershed, thereby reducing the computational burden
of the numerical simulation.
Overall, the TIN resulted in 116,215 mesh nodes and 232,292 triangle
cells (Supplementary Fig. 19b). The smallest cell area is 1.3 m2, and the
average cell size generated for the Warren area is ~47 m2. It is worth
noting that, even with this modestly sized TIN, the simulation time for a
24-h rainfall event requires up to 240 h of wall-clock time to complete.
It should be noted that in our study, technologies to speed up model
runtime, such as parallelization or the use of surrogate modeling54,
were not employed. However, when such computationally expensive
models need to be used in applications such as design optimization
and control/operation of stormwater systems, as well as real-time
forecasting, such technologies are particularly necessary to provide
simulation/forecast results in a timely manner.
All of the land cover is developed area, with the impervious surface
ranging from 47% to 95%. In the Warren area, the impervious surface
accounts for up to 94.48%. The land use and impervious surface infor-
mation was downloaded from NLCD 2019 (https://www.mrlc.gov/).
Experimental design
The research comprised two modeling experiments to address two
research questions. First, we evaluated two scenarios of the spatial
distribution of rainfall in causing flooding within the Warren area. For
this purpose, STORM2014 precipitation data were configured as a grid
with a resolution of 500 m × 500 m using rainfall data obtained from
the Detroit City Airport rain gauge. This gridded product was used as
model input in the first scenario, ‘HR-Integrated’ (results are presented
in Fig. 2d). The drainage system was designed to closely resemble real-
world conditions, allowing for water exchange between the inside and
outside of the Warren drainage system based on the dynamically chang-
ing distribution of hydraulic head over the surface and below ground
in storm sewers. In the second scenario, the gridded rainfall data for
the Warren area were set to 0 mm for the duration of the STORM2014
event (that is, 24 h). This scenario was named ‘NR-Integrated’ (Fig. 2e).
In the second experiment, we explored the functioning of two
stormwater sewer outfalls (Fig. 2b,c) using three different model
configurations. The first set-up represents the most realistic design

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by allowing backwater to occur (water from the open channel flows
back into the sewer system through the outfall) (termed ‘Integrated’
outfalls). The second set-up assumes the current sewer system is
unaffected by water levels at the outfall locations, maximizing drainage
capacity by allowing unrestricted discharge from the stormwater
system into the open channel without backflow from Bear Creek (termed
‘Controlled’ outfalls). The third set-up precludes any water exchange
at the outfalls between the stormwater system and open channel, with
the total outflow from the system stored in an underground storage
sized to contain all runoff from the Red Run watershed under
the STORM2014 event (a volume of ~24.77 million m3) rather than
entering the open channel. We term this case ‘Water loss’, the results
of which are shown in Supplementary Fig. 3.
The Manning’s roughness coefficient is the most influential para
meter for overland and sewer flows55. For surface flow simulated with
the OFM model, we assume a spatially uniform value of 0.015, which
is in the middle of the interval for concrete surfaces (0.012–0.018)29.
Manning’s coefficients for all the conduits were set to 0.013
(with concrete as the pipe material)56.
Data mining and model evaluation
In terms of its suitability to mimic the important details of hydrodynamic
processes such as overland flow, backwater, hydraulic jump, as well as
the influence of vegetation and buildings on surface flow, the tRIBS-OFM
model has been extensively tested and validated in numerous
previous studies52,54,57. To analyze the performance of the tRIBS-OFM
model with regard to its capability to depict the interaction between the
stormwater system and surface flow, we conducted a benchmark experi-
ment to evaluate simulated surcharge in manholes, as demonstrated
in Supplementary Fig. 22. A detailed description of this case study
is provided by the United Kingdom Environment Agency58. This
evidence supports that tRIBS-OFM produces reliable results when
simulating complex hydraulic dynamics and interactions between
the stormwater system and the surface, comparable with that of
other models that used the same dataset58. The competent performance
by tRIBS-OFM makes it a great choice for studies of urban flooding.
Due to the limitations of flood data availability in the study area,
conventional model validation is not feasible. Therefore, an alternate
approach was adopted to validate the model results by collecting
flood evidence from various internet sources. The manual process of
data collection and analysis involved several steps to ensure reliability
and accuracy:
•
Keywords and search operators: an ad hoc combination of
keywords was used for searching, including ‘2014 flood’,
‘Macomb County’, ‘Oakland County’, ‘Warren MI’, ‘Bear Creek
flood’ and ‘Michigan flood disaster 2014’. These keywords were
entered into the Google search engine. From the obtained
search results, images published in reports, newspapers and
blogs were selected and evaluated for inclusion in the study.
•
Quality assessment and metadata extraction: in a subsequent
filtering step, the obtained images were visually analyzed to
determine flooded locations and water levels relative to visible
objects such as people, vehicles or traffic signs. Details about
street names or intersections were used to estimate and extract
coordinates. The extracted metadata include information such
as coordinates, flood severity, flooded location (street name)
and the website link from which the image was sourced.
•
Uncertainty assessment: estimating water levels from images
involves subjective interpretation and conjectures necessitating
an evaluation of relevant confidence intervals. Typically, there
are two approaches to estimating this uncertainty: (1) assuming
that the uncertainty is proportional to the estimated water
depth or (2) considering that the uncertainty is associated
with each reference class59. In this study, we opted for the first
approach, assuming that the standard deviation was equal to
20% of the estimated water depth. The final results for the esti
mation of water levels at six locations are described in Supple-
mentary Table 2.
The flood depth data were subsequently used to validate the model
results (Supplementary Fig. 23). One limitation of these flood depth
estimations is the lack of certain information regarding the time at
which the images were acquired. Therefore, the accuracy of such a
comparison of the model results is constrained. We thus considered
the obtained flood depths as a threshold for ‘being flooded’ and used
them to compare with the model results to determine whether the
model exceeds this threshold at any simulation time. In essence, the
model results confirm that at the locations marked as ‘flooded’, the
occurrence of flooding is consistent with the simulation results, with
relatively minor discrepancies in flood depths. For example, at loca-
tion 1, the model yields a maximum flood depth of ~0.85 m, with an
estimated data range from 0.8 to 1.2 m. Similar results can be observed
for other locations.
Fundamentally, an accurate description of what occurs in
reality requires substantial efforts, not only in terms of the model’s
capabilities but also regarding the accuracy of the data used. This
study does not aim to assert that the model used in the study can pre-
cisely depict all relevant flood dynamics during STORM2014 across the
entire study area (Red Run watershed and Warren area). Rather, we aim
to show that, in conditions with complex permutation of the storm
water infrastructure, terrain, land use and buildings, the sophisticated,
state-of-the-science model can provide insights into potential flood
phenomena that have not been examined previously.
Reporting summary
Further information on research design is available in the Nature
Portfolio Reporting Summary linked to this Article.
Data availability
The land use cover was obtained from the National Land Cover Database
2019 (https://www.mrlc.gov). High-resolution elevation data were
provided by the USGS (https://apps.nationalmap.gov/downloader/).
The building footprint data were obtained from the Southeast Michigan
Council of Governments (https://maps.semcog.org/BuildingFoot-
prints). Data on the stormwater system, including manholes, outfalls
and pipes, were collected and compiled in collaboration with Macomb
and Oakland Counties. The rainfall data were from the Automated
Surface Observing Systems network (https://www.weather.gov/asos/).
The simulation dataset (~600 gigabytes) archived on the Globus Cloud
is available upon written request to the authors. Source data are
provided with this paper.
Code availability
Data processing and analysis were performed using MATLAB 2022b
(standard version). The source code is publicly accessible via GitHub
at https://github.com/vinhngoctran/RedRun_processing.
References
1.
Donat, M. G., Lowry, A. L., Alexander, L. V., O’Gorman, P. A. &
Maher, N. More extreme precipitation in the world’s dry and wet
regions. Nat. Clim. Change 6, 508–513 (2016).
2.
Prein, A. F. et al. The future intensification of hourly precipitation
extremes. Nat. Clim. Change 7, 48–52 (2016).
3.
Scott, D. T., Gomez-Velez, J. D., Jones, C. N. & Harvey, J. W.
Floodplain inundation spectrum across the United States.
Nat. Commun. 10, 5194 (2019).
4.
Paprotny, D., Sebastian, A., Morales-Napoles, O. & Jonkman, S. N.
Trends in flood losses in Europe over the past 150 years.
Nat. Commun. 9, 1985 (2018).

Nature Cities | Volume 1 | October 2024 | 654–664
663
Article
https://doi.org/10.1038/s44284-024-00116-7
5.
UNDRR Annual Report 2021 (United Nations Office for Disaster Risk
Reduction, 2021).
6.
NWS Preliminary US Flood Fatality Statistics (National Weather
Service, 2023).
7.
Storm Events Database (NOAA National Centers for Environmental
Information, 2023).
8.
US Billion-Dollar Weather and Climate Disasters (NOAA National
Centers for Environmental Information, 2023); https://doi.org/
10.25921/stkw-7w73
9.
Hirabayashi, Y. et al. Global flood risk under climate change.
Nat. Clim. Change 3, 816–821 (2013).
10.	 Rentschler, J. et al. Global evidence of rapid urban growth in flood
zones since 1985. Nature 622, 87–92 (2023).
11.
Revi, A. et al. Urban areas. In Climate Change 2014: Impacts,
Adaptation and Vulnerability. Part A: Global and Sectoral Aspects.
Contribution of Working Group II to the Fifth Assessment Report of
the Intergovernmental Panel on Climate Change Ch. 8, 535–612
(Cambridge Univ. Press, 2014).
12.	 Nowak, D. J. & Walton, J. T. Projected urban growth (2000–2050)
and its estimated impact on the U.S. forest resource. J. Forestry
103, 383–389 (2005).
13.	 Andreadis, K. M. et al. Urbanizing the floodplain: global changes
of imperviousness in flood-prone areas. Environ. Res. Lett. 17,
104024 (2022).
14.	 Golden, H. & Hoghooghi, N. Green infrastructure and its
catchment-scale effects: an emerging science. WIREs Water 1,
e1254 (2018).
15.	 Radhakrishnan, M., Pathirana, A., Ashley, R. M., Gersonius, B. &
Zevenbergen, C. Flexible adaptation planning for water sensitive
cities. Cities 78, 87–95 (2018).
16.	 Wong, T. H. & Brown, R. R. The water sensitive city: principles for
practice. Water Sci. Technol. 60, 673–682 (2009).
17.	 From Gray to Green—Helping Communities Adopt Green
Infrastructure (EPA, 2023).
18.	 Alves, A., Vojinovic, Z., Kapelan, Z., Sanchez, A. & Gersonius, B.
Exploring trade-offs among the multiple benefits of green-blue-
grey infrastructure for urban flood mitigation. Sci. Total Environ.
703, 134980 (2020).
19.	 Wilbanks, T. et al. Climate Change and Infrastructure, Urban
Systems, and Vulnerabilities. Technical Report for the U.S.
Department of Energy in Support of the National Climate
Assessment (Springer, 2013).
20.	 Collentine, D. & Futter, M. N. Realising the potential of natural
water retention measures in catchment flood management:
trade-offs and matching interests. J. Flood Risk Manag. 11, 76–84
(2018).
21.	 EFAB Report: Evaluating Stormwater Infrastructure Funding and
Financing (EPA, 2023).
22.	 Qiao, X.-J., Kristoffersson, A. & Randrup, T. B. Challenges to
implementing urban sustainable stormwater management from
a governance perspective: a literature review. J. Clean. Prod. 196,
943–952 (2018).
23.	 Rosenzweig, B. R. et al. Pluvial flood risk and opportunities for
resilience. WIREs Water 5, e1302 (2018).
24.	 ten Veldhuis, J. A. E. How the choice of flood damage metrics
influences urban flood risk assessment. J. Flood Risk Manag. 4,
281–287 (2011).
25.	 Backhaus, A., Dam, T. & Jensen, M. B. Stormwater management
challenges as revealed through a design experiment with
professional landscape architects. Urban Water J. 9, 29–43 (2012).
26.	 Wright, D. B., Bosma, C. D. & Lopez-Cantu, T. U.S. hydrologic
design standards insufficient due to large increases in frequency
of rainfall extremes. Geophys. Res. Lett. 46, 8144–8153 (2019).
27.	 Mays, L. W. Stormwater Collection Systems Design Handbook
(McGraw-Hill, 2001).
28.	 Standard Guidelines for the Design of Urban Stormwater Systems
(ASCE, 2006).
29.	 Arcement, G. J. & Schneider, V. R. Guide for Selecting Manning’s
Roughness Coefficients for Natural Channels and Flood Plains
(US GPO, 1989).
30.	 Woods-Ballard, B. et al. The SuDS Manual (CIRIA, 2007).
31.	 Drainage Services Department. Stormwater Drainage Manual:
Planning, Design and Management (Government of the Hong
Kong Special Administrative Region, 2018).
32.	 Allen, M. D. et al. Water Sensitive Urban Design: Basic Procedures
for ‘Source Control’ of Stormwater. A Handbook for Australian
Practice (ed. Argue, J.) (Univ. South Australia, 2004).
33.	 Bradford, A. & Gharabaghi, B. Evolution of Ontario’s stormwater
management planning and design guidance. Water Quality Res. J.
39, 343–355 (2004).
34.	 Wing, O. E. J. et al. Inequitable patterns of US flood risk in the
Anthropocene. Nat. Clim. Change 12, 156–162 (2022).
35.	 Framing the Challenge of Urban Flooding in the United States
(National Academies Press, 2019).
36.	 Rosenzweig, B. R. et al. The value of urban flood modeling.
Earth’s Future 9 (2021); https://doi.org/10.1029/2020ef001739
37.	 Guo, K., Guan, M. & Yu, D. Urban surface water flood modelling—a
comprehensive review of current models and future challenges.
Hydrol. Earth Syst. Sci. 25, 2843–2860 (2021).
38.	 Forero-Ortiz, E., Martínez-Gomariz, E. & Cañas Porcuna, M.
A review of flood impact assessment approaches for
underground infrastructures in urban areas: a focus on transport
systems. Hydrol. Sci. J. 65, 1943–1955 (2020).
39.	 Carmichael, C., Danks, C. & Vatovec, C. Assigning blame:
how local narratives shape community responses to extreme
flooding events in Detroit, Michigan and Waterbury, Vermont.
Environ. Commun. 14, 300–315 (2020).
40.	 Shepardson, D. White House Approves Michigan Disaster
Declaration (Detroit News Washington Bureau, 2014).
41.	 National Flood Hazard Layer (FEMA, 2023).
42.	 Yazdanfar, Z. & Sharma, A. Urban drainage system planning and
design—challenges with climate change and urbanization:
a review. Water Sci. Technol. 72, 165–179 (2015).
43.	 Chen, S. S. et al. Designing sustainable drainage systems in
subtropical cities: challenges and opportunities. J. Clean. Prod.
280, 124418 (2021).
44.	 Schmitt, T. G. & Scheid, C. Evaluation and communication
of pluvial flood risks in urban areas. WIREs Water 7, e1401
(2020).
45.	 Prosdocimi, I., Kjeldsen, T. R. & Miller, J. D. Detection and
attribution of urbanization effect on flood extremes using
nonstationary flood-frequency models. Water Resour. Res. 51,
4244–4262 (2015).
46.	 Storm Water Management Plan (City of Warren, 2012); https://
www.cityofwarren.org/departments/engineering-division/
47.	 Mapping the Zone: Improving Flood Map Accuracy (National
Academies Press, 2009).
48.	 Kousky, C. Financing flood losses: a discussion of the national
flood insurance program. Risk Manag. Insurance Rev. 21, 11–32
(2018).
49.	 Pralle, S. Drawing lines: FEMA and the politics of mapping flood
zones. Clim. Change 152, 227–237 (2019).
50.	 Ivanov, V. Y., Vivoni, E. R., Bras, R. L. & Entekhabi, D. Catchment
hydrologic response with a fully distributed triangulated irregular
network model. Water Resour. Res. https://doi.org/10.1029/
2004wr003218 (2004).
51.	 Ivanov, V. Y., Vivoni, E. R., Bras, R. L. & Entekhabi, D. Preserving
high-resolution surface and rainfall data in operational-scale
basin hydrology: a fully-distributed physically-based approach.
J. Hydrol. 298, 80–111 (2004).

Nature Cities | Volume 1 | October 2024 | 654–664
664
Article
https://doi.org/10.1038/s44284-024-00116-7
52.	 Kim, J., Warnock, A., Ivanov, V. Y. & Katopodes, N. D. Coupled
modeling of hydrologic and hydrodynamic processes including
overland and channel flow. Adv. Water Res. 37, 104–126 (2012).
53.	 Rossman, L. A. Storm Water Management Model User’s Manual,
Version 5.0 (US Environmental Protection Agency, 2010).
54.	 Ivanov, V. Y. et al. Breaking down the computational barriers
to real‐time urban flood forecasting. Geophys. Res. Lett. 48,
e2021GL093585 (2021).
55.	 Ozdemir, H., Sampson, C. C., de Almeida, G. A. M. & Bates, P. D.
Evaluating scale and roughness effects in urban flood modelling
using terrestrial LIDAR data. Hydrol. Earth Syst. Sci. 17, 4015–4030
(2013).
56.	 Chow, V. Open Channel Hydraulics (McGraw Hill Education,
1959).
57.	 Kim, J., Ivanov, V. Y. & Katopodes, N. D. Hydraulic resistance to
overland flow on surfaces with partially submerged vegetation.
Water Resour. Res. https://doi.org/10.1029/2012wr012047 (2012).
58.	 Néelz, S. & Pender, G. Benchmarking the Latest Generation of 2D
Hydraulic Modelling Packages (UK Environment Agency, 2013).
59.	 Songchon, C., Wright, G. & Beevers, L. The use of crowdsourced
social media data to improve flood forecasting. J. Hydrol. 622,
129703 (2023).
Acknowledgements
V.N.T. and V.Y.I. acknowledge the support of the US National Science
Foundation CMMI program award no. 2053429 and the Department
of Defense, Department of the Navy, the Office of Naval Research
award no. N00014-23-1-2735, Environmental Protection Agency
grant #2020‐2509. J. Kim was supported by the National Research
Foundation of Korea (NRF) grant funded by the Korea government
(MSIT)(NRF-2022R1A2C2008584. D.B. Wright and G.A. Alexander
acknowledge the support of the U.S. National Science Foundation
CMMI program award #2053358. We acknowledge informative
discussions with P. Seelbach, B. Kerkez, J. Bednar, G. O’Neil, C. Brown,
C. Purdy, A. Asher, M. Thomas and S. Bergt that assisted this study.
Author contributions
V.N.T. and V.Y.I. designed the study. V.N.T., W.H., K.M. and F.D. collected
the data. V.N.T. conducted the experiments. V.N.T. and V.Y.I. analyzed
the data and wrote and revised the paper with inputs from all
co-authors. All authors contributed to the interpretation of results,
writing and revision of the paper.
Competing interests
The authors declare no competing interests.
Additional information
Supplementary information The online version contains supplementary
material available at https://doi.org/10.1038/s44284-024-00116-7.
Correspondence and requests for materials should be addressed to
Valeriy Y. Ivanov.
Peer review information Nature Cities thanks Matt Bartos and the
other, anonymous, reviewer(s) for their contribution to the peer review
of this work.
Reprints and permissions information is available at
www.nature.com/reprints.
Publisher’s note Springer Nature remains neutral with regard to
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Springer Nature or its licensor (e.g. a society or other partner) holds
exclusive rights to this article under a publishing agreement with
the author(s) or other rightsholder(s); author self-archiving of the
accepted manuscript version of this article is solely governed by the
terms of such publishing agreement and applicable law.
© The Author(s), under exclusive licence to Springer Nature America,
Inc. 2024

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Corresponding author(s):
Valeriy Y. Ivanov
Last updated by author(s): Jul 10, 2024
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The land use cover was obtained from the National Land Cover Database 2019 (https://www.mrlc.gov). High-resolution elevation data was provided by the USGS,
https://apps.nationalmap.gov/downloader/). The building footprint data was obtained from the Southeast Michigan Council of Governments (https://
maps.semcog.org/BuildingFootprints). Data on the stormwater system, including manholes, outfalls, and pipes, were collected and compiled in collaboration with

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The main purpose of the study is to examine how built infrastructure exacerbates the space-time connectivity of urban floods, using
the 2014 flood in Southeast Michigan as a case study. The findings highlight the importance of considering the interconnectedness of
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    Scientific Research Findings:

Answer: Starting with analysis of unexpected flooding in southeast Michigan, USA, in 2014, we find that human-engineered infrastructure can introduce additional stormwater flow connectivity in urban environments that may increase flood risk. Specifically, flooding may worsen where river channels are connected to underground infrastructure. This has immediate implications for stormwater design, as differing assumptions about flood connectivity between river flooding and infrastructure can yield markedly different estimates of potential inundation. The ubiquitous practice of ignoring this connectivity — assessing the performance of infrastructure elements in isolation and with overly simplistic rainfall scenarios — can lead to substantial errors, which result in reduced infrastructure effectiveness. Paradoxically, design errors can reverse stormwater network functionality in the real world and lead, for instance, to flooding even in the absence of local rainfall. Although this study illuminates broader challenges in stormwater network design and flood risk management, solutions will need to be tailored to the needs and unique characteristics of individual communities.