Nat Hazards
DOI 10.1007/s11069-016-2455-1
ORIGINAL PAPER
Modeling urban floods and drainage using SWMM
and MIKE URBAN: a case study
Deepak Singh Bisht1 • Chandranath Chatterjee1 •
Shivani Kalakoti1 • Pawan Upadhyay1 • Manaswinee Sahoo1
Ambarnil Panda1
•
Received: 1 July 2015 / Accepted: 2 July 2016
Springer Science+Business Media Dordrecht 2016
Abstract To avoid the nuisance of frequent flooding during rainy season, designing an
efficient stormwater drainage system has become the need of the hour for present world
engineers and urban planners. The present case study deals with providing a solution to
stormwater management problem in an urbanized area. Mann–Kendall and Sen’s slope
tests are used to perform the trend analysis of rainfall events using daily rainfall data
(1956–2012), while the L-moments-based frequency analysis method is employed to
estimate the design storm for a small urbanized area in West Bengal, India, using daily
annual maximum rainfall (1975–2013). SWMM (Storm Water Management Model) and
MIKE URBAN models are used to design an efficient drainage system for the study area.
Two-dimensional (2D) MIKE URBAN model is primarily used to overcome the limitation
of one-dimensional (1D) SWMM in simulating flood extent and flood inundation. Model
simulation results from MIKE URBAN are shown for an extreme rainfall event of July 29,
2013. A multi-purpose detention pond is also designed for groundwater recharge and
attenuating the peak of outflow hydrograph at the downstream end during high-intensity
rainfall. This study provides an insight into the importance of 2D model to deal with
location-specific flooding problems.
Keywords SWMM MIKE URBAN Trend analysis Design storm L-moments Drainage system design Urban flood modeling
& Deepak Singh Bisht
dsbisht.ae@gmail.com
1
Agricultural and Food Engineering Department, Indian Institute of Technology Kharagpur,
Kharagpur, India
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1 Introduction
More than half of the world population currently lives in urban areas, and over 500 cities
shelter more than one million people worldwide (United Nations 2010). Prevention of
flooding caused by unpredictable high-intensity rainfall events in urban areas due to lack of
adequate drainage systems has become an important issue as risk of flooding is increased
due to combined effect of urbanization and climate change (Duan et al. 2016). Urban
planners from all over the world are facing a great amount of challenge because of
increased trend of urban flooding in recent days. In recent days, India is facing flooding at
frequent intervals. Total geographical area stated to be flood prone is 48 million ha out of
329 million ha, which makes India highly vulnerable to floods. On an average, India loses
1600 human lives and 18.05 billion INR each year due to various losses, such as damage to
agriculture, houses and other public utilities, caused by flood (National Disaster Management Guidelines: Management of Floods 2008). Extreme rainfall events adversely
affect the residents and infrastructure. Mumbai flood in July 2005 provides an insight into
the alarming situation of poor stormwater management in Indian cities. A rainfall of
944 mm depth, occured in 24 hours, caused more than 400 deaths in addition to financial
losses to the tune of £1.2 billion (The Guardian 2014). Climatic extremities coupled with
haphazard human intervention and inadequate planning to handle high storm events led to
Uttarakhand flood in July 2013 causing 580 deaths and over 5400 people missing in the
aftermath of flood, loss of 9200 cattle and complete damage to 3320 houses (India:
Uttarakhand Disaster June 2013). Heavy flooding due to unseasonal rainfall submerged
Kashmir twice in a short span of 6 months, September 2014–March 2015, causing over
200 deaths alone in September 2014. Improper drainage system coupled with unchecked
and ill-planned urbanization makes the region even more vulnerable to such disasters (The
Times of India 2015; The Hindu 2015).
Issue of urban flooding is addressed by many researchers in recent years, and some of
the work can be referred in Gupta 2007; Ranger et al. 2011; Tingsanchali 2012; Liu and
Cheng 2014. In India, an increased trend of urban flood disasters has been observed over
the past several years, and major cities notably Guwahati in 2010, Delhi in 2010, 2009,
2003 and 2002, Jamshedpur in 2008, Kolkata in 2007, Surat in 2006, Mumbai in 2005,
Chennai in 2004, Ahmedabad in 2001 and Hyderabad in 2000 were found to be severely
affected due to urban flooding during high-intensity rainfall events. Contrary to rural
flooding, urban flooding may lead to 1.8 to 8 times increased flood peaks and up to 6 times
increased flood volumes during high-intensity rainfall events as urbanization alters the
catchment characteristics by modifying the land use/land cover (National Disaster Management Guidelines: Management of Urban Flooding 2010). Land use modifications
associated with urbanization include removal of vegetation, replacement of pervious areas
with impervious surfaces causing increased peak of runoff hydrograph and volumes, and
less opportunity for infiltration. Development of infrastructure to fulfill the needs of
growing population and their societal demand, and the simplification of the stormwater
drainage network system (such as straightening and lining of drains) also result in faster
runoff response, reduced recession times and shorter times of concentration (Goonetilleke
et al. 2005; Marsalek et al. 2006; Teemusk and Mander 2011; Fletcher et al. 2013).
Impervious surfaces directly connected to receiving waters via hydraulically efficient
drainage paths in urbanized watersheds result in higher peak flows and higher rates of
storm recession. Change in land use/land cover and flow pathways can reduce losses in the
rainfall–runoff process eventually causing the decline in groundwater recharge (Rose and
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Peters 2001). Change in natural landscapes due to sprawling urban areas is increasing flood
risk condition and finding stormwater as a ‘‘nuisance’’ to the residents.
Five major effects of urbanization have been identified: (1) a larger proportion of
precipitation converted into surface runoff; (2) rainfall–runoff response of the catchment is
accelerated for a specific rainfall event resulting into steeper rising limb of the flow
hydrograph with reduced lag time and time to peak; (3) increased magnitude of peak flow
for all but the very extreme events; (4) decline in low flow due to less contributions from
groundwater storage because of less replenishment and (5) degraded water quality in
streams and rivers because of effluent discharges (Shaw 1994).To model the urban
stormwater drainage and management, the input conditions and model parameters used are
always crucial. Rainfall has been advocated as one of the most important inputs to develop
the runoff response; hence, it is very essential to make the right choice of precipitation
depth for designing any urban drainage system. With changing global climate, the rainfall
pattern has changed worldwide and more intense precipitation events have not only
become very likely over many areas in the globe, but also the number of increased rainfall
events is also increased (Meehl et al. 2000; IPCC 2001a). The frequency and the magnitude
of extreme rain events during the monsoon seasons have increased in the recent past in
India due to global climate change (Roy and Balling 2004; Goswami et al. 2006). However, considering the most extreme storm event for drainage design system would make the
system too expensive to build and operate; therefore, it is essential to make reasonable
estimates of tolerable frequencies to have a system which is technically feasible and
economically viable with least compromises to people’s lives and properties.
Many studies across the globe were conducted to determine any existing trend and
variability in extreme rainfall of specific regions. Jena et al. (2014) performed trend
analysis of different rainfall extremes using Mann–Kendall test and Sen’s slope test for
Mahanadi river basin in India. Statistical methods and tools like simple regression model,
Sen’s slope estimator and Kendall’s tau test are frequently used by many researchers to
determine the trends of extreme rainfall in various parts of the world (Karl and Knight
1998; Suppiah and Hennessy 1998; Haylock and Nicholls 2000; Roy and Balling 2004;
Partal and Kahya 2006; Wan Zin et al. 2010; Douglas and Chelsea 2011; Pal and AlTabbaa 2011a; Jain and Kumar 2012; Patra et al. 2012). In the present study, temporal
trend analysis of extreme rainfall (1956–2012) of IIT Kharagpur was performed using
Mann–Kendall test and Sen’s slope test, while the L-moment-based frequency analysis
approach (as described by Hosking and Wallis 1997; Kumar and Chatterjee 2005; 2011)
was employed to estimate the design storms of various return periods.
Burns et al. (2012) emphasized on the protection of natural hydrologic processes by
restoring natural flow regimes or pre-development hydrograph of a watershed in urban
stormwater management system. One way to restore the pre-development hydrograph can
be a provision of a detention pond at the outlet before allowing the water to go downstream. Though in many cases, a complete restoration of pre- urbanization catchment
cannot be achieved because of limitation of land for pond construction, yet a cushioned
hydrograph peak can reduce the flood risk at downstream areas to some extent.
There are many software packages available commercially and non-commercially with
varying degrees of complexities to simulate the stormwater runoff quantity and quality.
Zoppou (2001) and Elliott and Trowsdale (2007) reviewed the applicability of various
rainfall–runoff models under urban environment. Li et al. (2016) used MIKE FLOOD to
simulate the flood inundation scenarios at community level. In the present study, Storm
Water Management Model (SWMM) (Rossman 2004; Gironas et al. 2010) is used to
simulate the runoff and designing of an efficient drainage system. Since, SWMM is a one-
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dimensional model and comes with no help to visualize urban flood extent and inundation
depth simulation, we used MIKE URBAN model developed by Danish Hydrological
Institute (DHI 2007) to overcome SWMM’s limitations. Prior to the modeling exercise, we
also performed the trend analysis of rainfall using Mann–Kendall (Mann 1945; Kendall
1975) and Sen’s slope tests (Sen 1968), while L-moment-based frequency analysis method
(Hosking and Wallis 1997) is employed to estimate the design storm for the study area.
2 Study area and flooding problem
Study area in the present study is a part (southern catchment) of Indian Institute of
Technology (IIT) Kharagpur campus, West Bengal, India [Fig. 1, (Google Earth 2014,
March 30)]. This area is chosen primarily because of the occurrence of frequent surface
flooding situations during short-duration high-intensity rainfall in monsoon season. IIT
Kharagpur campus lies between 22180 and 22190 North latitudes and 87170 to 87190
East longitudes, spread in an area of 470 ha. It is situated about 120 km west of Kolkata,
West Bengal, India. IIT Kharagpur receives an average annual rainfall of 1564 mm, and
the mean annual temperature is around 26 C.
Frequently occurring extreme weather events coupled with altered natural drainage
system due to urbanization have made Indian cities vulnerable to urban flooding (Down to
Earth 2014). Increased frequency of urban flooding has been acknowledged by National
Disaster Management Authority in its report (National Disaster Management Guidelines:
Fig. 1 (Google Earth 2014, March 30) Image of IIT Kharagpur. The locations 1–5 indicated in this
figure are those for which flooding snapshots for the rainfall event of July 29 , 2013, are shown in Fig. 2.
Location 5 is the position of G-type quarters where severe flooding occurs. Stream network for the current
study area, i.e., southern region is shown in yellow lines
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Fig. 2 Snapshots of flooding at various locations 1–5 as indicated in Fig. 1. Rainfall hyetograph of July 29,
2013, is shown at 6
Management of Urban Flooding 2010). It also emphasizes to have area-specific wellplanned policies to tackle urban flooding to solve the local problems. Drainage congestion
problem with the stagnation of rainwater at different locations causes inconvenience to the
locality. Anthropogenic activities such as expanding road network, pavements, parking
areas and various construction works to satisfy the societal needs cause the change in land
use pattern which in turn obstructs the natural infiltration capacity of the soil. Improper
drainage system design coupled with the aforementioned anthropogenic activities leads to
improper management of stormwater causing flooding and frequent water logging in the
streets. Drainage network for stormwater in the campus is mostly an open drainage system
with majority of the drains being earthen in nature. The study area (southern region),
shown in Fig. 1, was specifically chosen as the local topography makes it most vulnerable
to flooding during rainy season. The drainage congestion at the outlet and backwater effect
from the canal running at the outlet during rainy season further worsens the situation. More
than 70 % of study area drains through the G-type quarters (shown in Fig. 1), hence,
bringing a huge volume of discharge. Any overflow from the drains gets accumulated
inside as the concrete boundary wall around the campus isolates it from rest of the region
allowing only designated openings to drain the water. Two extreme rainfall events,
505.8 mm on June 16, 2008, and 187.5 mm on July 29, 2013, caused prolonged water
ponding of 80–90 cm at G-type quarters. Later, rainfall caused flooding at various locations in the campus (Fig. 2), which motivated us to carry out this study.
3 Data used
Data required for rainfall–runoff modeling consist of rainfall, land use/land cover, topographic map or digital elevation model of the watershed, and soil type information. In the
present study, historical rainfall data of IIT Kharagpur (1956–2012) recorded in Department of Physics, IIT Kharagpur are used for trend analysis and frequency analysis. A
topographic survey map having an elevation contour interval of 20 cm with campus layout
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Fig. 3 Digital elevation model of IIT Kharagpur campus
provided by Civil Works Department (CWD), IIT Kharagpur is used to generate the digital
elevation model (Fig. 3) and subsequent watershed delineations. We used ‘‘Topo to Raster’’ tool in ArcGIS 9.3 to generate the DEM from contour lines at 10 m 9 10 m grid size.
Unlike a natural watershed that drains to a single discharge point, IIT Kharagpur
campus has multiple outlets due to the nature of the topography. Digital elevation model of
the campus was used for delineation using ArcSWAT tool at 1-ha threshold in ArcGIS 9.3.
Areas draining toward southern zone of the campus are identified, and a composite basin
covering an area of approximately 220 ha is created (Fig. 4) for subsequent study.
A Thiessen polygon-based soil map of the campus developed from the textural analysis
of soil sample collected from 56 different locations using international pipette method
(Sarkar and Haldar 2005) was used to determine soil classes (Fig. 4). These soil classes
were assigned the soil properties as given by Rawls et al. (1983) (Table 1). Impervious
area was determined from the campus layout map on GIS platform by calculating the area
covered by road, buildings and other infrastructure.
4 Trend analysis of rainfall
Increased risk of extreme rainfall and subsequent flood in West Bengal and other regions of
India is reported by Guhathakurta et al. (2010). Trend analysis of different rainfall
extremes are performed using the daily rainfall data of IIT Kharagpur campus (1956–2012)
to detect any persistent trend prior to frequency analysis. Extreme rainfalls are categorized
as follows:
1.
2.
1-day, 2-days and 3-days annual maximum rainfall.
Number of events exceeding the 95th percentile value of the period 1956–2012 for
each year.
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Fig. 4 Thiessen polygon-based soil map
Table 1 Soil parameter assigned for different types of soil (Rawls et al. 1983)
Soil type
Saturated Hydraulic
conductivity (mm/h)
Wet front suction
head (mm)
Porosity
(fraction)
Field capacity
(fraction)
Sandy loam
10.922
109.982
0.453
0.190
Sandy clay loam
1.524
219.964
0.398
0.244
Clay loam
1.016
210.058
0.464
0.310
29.972
60.960
0.437
0.105
6.604
169.926
0.501
0.284
Loamy sand
Silt loam
3.
4.
5.
Number of events exceeding the 95th percentile value of the period 1956–2012 for
each 25-year moving window. The moving windows are from 1956 to 1980,
1958–1982, 1960–1984 … 1988–2012.
Threshold extreme rainfall as the 99th percentile of 25-year moving window data.
In addition to the above extreme rainfalls, the trends of cumulative rainfall of monsoon
season, i.e., June to September, are analyzed.
Two nonparametric tests, Mann–Kendall test and Sen’s slope test (Sen 1968) are performed to detect any existing trends in rainfall data. Mann–Kendall test, a rank-based test,
is a popular method for detection of trend in hydro-climatological data. First it was used by
Mann (1945), and then, the test statistic was derived by (Kendall 1975). Both aforementioned nonparametric tests have been widely used by many researchers for detecting the
trend and quantification of the magnitude of trend in hydro-climatological data series as
explained in previous works (Lettenmaier et al. 1994; Partal and Kahya 2006; Dufek and
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Table 2 Trend analysis results of Mann–Kendall test and Sen’s slope test for extreme rainfalls at IIT
Kharagpur campus
Mann–
Kendall test
Sen’s slope test
Z
Qmed
p
Qmin
Qmax
1-day annual maximum rainfall
0.349
63.64
0.159
-0.621
0.886
2-day annual maximum rainfall
0.657
74.45
0.435
-0.576
1.506
3-day annual maximum rainfall
0.543
70.65
0.387
-0.610
1.515
Number of events in each year exceeding the 95th percentile
value
1.918
97.24
0.095
0.023
0.162
Number of events in 25-year window exceeding the 95th
percentile value
3.872
99.99
1.972
1.667
2.250
Threshold extreme rainfall (the 99th percentile) for 25-year
window
0.947
82.82
0.210
-0.050
0.380
Cumulative rainfall of monsoon season (June to September)
0.998
84.08
2.352
-1.722
6.668
Ambrizzi 2008; Wan Zin et al. 2010; Douglas and Chelsea 2011; Pal and Al-Tabbaa
2011a, b; Castellarin and Pistocchi 2012; Gocic and Trajkovic 2013; Jena et al. 2014).
Table 2 shows Mann–Kendall test in terms of the standardized Z statistics and their
corresponding probability, and Sen’s slope test in terms of median slope (Qmed) and slope
at confidence interval of 10 % significance level (Qmin and Qmax). No significant trend is
found in 1-day, 2-days and 3-days annual maximum rainfall. The number of extreme
events is calculated based on the 95th percentile value. The 95th percentile threshold value
is found to be 26.5 mm for the entire data period of 1956–2012. The number of extreme
events shows significant increasing trends in both cases, i.e., number of events in each year
and number of events in 25-year windows. Threshold value as the 99th percentile value of
each 25-year window has no significant trend. The cumulative seasonal monsoon rainfall
also does not show any significant trend.
Figure 5a and b shows trends in number of extreme events in each year and 25-year
moving window, respectively, which reveals that both cases show significant increasing
trend as slope at confidence intervals has consistent positive sign (Table 2).
Threshold rainfall value at the 95th percentile of 25-year moving window data and
cumulative rainfall of monsoon season (June to September) from 1956 to 2012 do not show
any significant trend using Sen’s slope test (not shown here). No significant trend was
found for 1-day, 2-days and 3-days annual maximum rainfall for the period 1956–2012
(Fig. 5c, only shown for 1-day annual maximum rainfall) at 10 % significance level.
Thus, the results of trend analysis show that the number of extreme events is significantly
increasing, though extreme rainfall values do not show any significant increasing trend. As
the 1-day annual maximum rainfall values as well as the cumulative monsoon season rainfall
do not have any significant trends, they may be safely used to perform frequency analysis.
5 Frequency analysis of extreme rainfall events using L-moments
approach
Extreme events like floods and severe storms often affect the hydrologic system. Frequency of occurrence of extreme events can be determined by using probability distributions. In order to satisfy the condition of independent and identical distribution of
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Fig. 5 a Sen’s slope line with confidence interval at 10 % significance level for number of extreme events
in each year exceeding the 95th percentile value for the period 1956–2012. b Sen’s slope line with
confidence interval at 10 % significance level for number of extreme events in different 25-year moving
windows exceeding the 95th percentile value for the period 1956–2012. c Sen’s slope line with confidence
interval at 10 % significance level for 1-day annual maximum rainfall for the period 1956–2012
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random variables or say, rainfall events in this case, only the daily rainfall of highest
magnitude of each year (1975–2013) was selected for frequency analysis.
In the present study, at-site frequency analysis of extreme rainfall events is performed
using L-moments approach. L-moments approach is a recent development within statistics
(Hosking 1990) and found to be superior to other methods used in similar studies carried
out in the past (Hosking and Wallis 1997). For more details of L-moments-based frequency
analysis, readers may refer to Hosking and Wallis (1997), Kumar and Chatterjee (2005).
Various distributions namely generalized logistic distribution (GLO), generalized extremevalue distribution (GEV), generalized normal (lognormal) distribution (GNO), Pearson
type III distribution (PE3), generalized Pareto distribution (GPA) and Wakeby distribution
(WAK) are used. In this study, the L-moment ratio diagram and Zdist-statistic are used as
goodness-of-fit measures for identifying the robust frequency distribution.
Frequency analysis was performed excluding the extreme event of year 2008
(505.8 mm) to test whether this extreme event is an outlier or not. The values of L-coefficient of variation (s2), L-skewness (s3) and L-kurtosis (s4) are found to be 0.2436,
0.1995 and 0.0680, respectively. The L-moment ratio diagram, based on approximations
provided by Hosking (1990), is used to identify the suitable rainfall frequency distribution
(Fig. 6), and GPA distribution was found to be closest to the point defined by the values of
L-skewness, i.e., s3 = 0.1995 and L-Kurtosis, i.e., s4 = 0.0680. Further, absolute Zdiststatistic is also found to be the lowest for GPA distribution, i.e., 0.23 (Table 3). Therefore,
the GPA distribution is identified as the best suited distribution for our dataset from
L-moment ratio diagram as well as Zdist-statistic test.
The location (u), scale (a) and shape (k) parameters of the GPA distribution are found to
be 0.431, 0.759 and 0.335, respectively.
Fig. 6 L-moment ratio diagram for IIT Kharagpur gauging site for various distributions (excluding extreme
event)
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Table 3 Zdist-statistic for various distributions for IIT Kharagpur gauging site (excluding
extreme event)
Zdist-statistic
Sr. no.
Distribution
1
Generalized logistic (GLO)
2.62
2
Generalized extreme value (GEV)
1.89
3
Generalized normal (GNO)
1.73
4
Pearson type III (PE3)
1.38
5
Generalized pareto (GPA)
0.23
Inverse form of the GPA distribution for a return period T and k = 0 can be expressed
as,
k xðF Þ ¼ u þ a 1 1=T
=k
k 6¼ 0
ð1Þ
n
o
xðF Þ ¼ u a ln 1 1=T
k¼0
ð2Þ
Subsequently, T year return period rainfall for 1 day can be calculated as RT ¼ R xðF Þ
where R (i.e., 139.41 mm) is the average daily maximum rainfall of the chosen period (i.e.,
38 years) and x(F) is the growth factor. Estimated daily maximum rainfall for different
return periods excluding extreme event are shown in Table 4.
Magnitude of 10,000-year return period rainfall is found to be 362 mm (Table 4) which
is significantly lesser than that of 24-h maximum rainfall observed in 2008, and hence, this
event is treated as an outlier while performing frequency analysis to estimate the design
storms for design purpose.
Arisz and Burrell (2006) state that ‘‘for economic reasons the hydraulic capacity of the
minor drainage system is limited (generally the hydraulic capacity is designed to convey a
flow with a return period of between 5 and 10 years), and during extreme events there will
be overflow in the street and roadway system.’’ As the present study is carried out to design
the surface drainage system, 5- and 10-year return period rainfall was estimated using
L-moments-based frequency analysis excluding extreme event of 2008 were used. We also
compared our design estimates of 24-hour rainfall of 5- and 10-year return periods with
IMD isopluvial maps of 24-hour rainfall of respective return periods (IMD 2007). Our
estimated values are in good agreement with IMD estimates, as 5- (and 10)-year return
Table 4 Growth factors of GPA
distribution for different return
periods and corresponding rainfall at IIT Kharagpur gauging site
Return period (year)
Growth factor
Rainfall (mm)
2
0.901
126
5
1.376
192
10
1.650
230
25
1.927
269
50
2.087
291
100
2.214
309
200
2.314
323
500
2.416
337
1000
2.475
345
10,000
2.595
362
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period rainfall from IMD map is found to be 200 (and 233) mm against our estimate of 192
(and 230) mm, respectively. IMD prepared the isopluvial maps using 146 rain gauge
stations of West Bengal and Sikkim states, whereas our estimates are derived from the
local rain gauge records of IIT Kharagpur and hence can be used with confidence.
However, we used 10-year return period design storm for drainage design subsequently
to avoid very frequent flooding of the streets and other campus utility areas.
6 Time distribution curve for 24-h rainfall
Time series of 24-h rainfall distributed in hourly basis is developed using the time distribution coefficient given by Central Water Commission (CWC 1994). Twenty-four hours
maximum rainfall of 5- and 10-year return periods were multiplied with the distribution
coefficients, and obtained values were distributed in 24-h duration as shown in Fig. 7,
assuming the maximum depth of rainfall occurring at fourth hour to avoid any trend (either
continuous increment or decrement in rainfall intensity) in the time series. To further
elaborate, CWC report does not specify any specific rainfall distribution pattern and gives
the fraction of rainfall that may occur in first, second, third … 24th hour in descending
order. We arranged it to have maximum depth of rainfall at fourth hour. Such an
assumption is made as it is highly probable that before a high-intensity rainfall occurs there
may be a few low-intensity events saturating the upper soil layer to accelerate the subsequent runoff during a high-intensity event.
7 SWMM (Storm Water Management Model) setup
SWMM simulates the runoff quantity generated from a subcatchment during simulation
period along with the discharge, and flow depth in conduits either open channels or closed
pipes. Apart from simulating the quantitative aspects of stormwater, it is also capable of
simulating the runoff quality. This model was primarily developed for urban areas and is
Fig. 7 Hourly distributed rainfall of 5- and 10-year return periods
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capable of modeling water quantity and quality through the catchment for single event or
long-term (continuous) simulation. In order to account for the infiltration losses, it has
three different schemes to choose from, i.e., Green–Ampt model, Horton Model and curve
number scheme, while the flow routing can be carried out using steady wave routing,
kinematic wave routing and dynamic wave routing. In the present study, Green–Ampt
model for infiltration losses and dynamic wave routing for flow routing have been used.
For more details on SWMM, reader may refer to Rossman (2004). An inflow can be
assigned to the nodes as the inflow boundary condition. However, in the present study, we
carried out the simulation considering no antecedent rainfall events. Therefore, we did not
assign any inflow at any of the nodes.
7.1 Subcatchment discretization
In SWMM, subcatchments are hydrologic units of land whose topography and drainage
system dispose the direct surface runoff to a single discharge point. These discharge points
are designated as nodes and carry the runoff through links to another node which can be a
junction of two or more than two conduits or discharge point of any other subcatchment.
Unlike a natural watershed, in urban watershed, the overland flow is often intercepted by
the drains which carry the water to the designated outlet. This phenomenon of interception
of surface runoff by drains should be taken into account while discretizing subwatersheds
in SWMM. Figure 8 shows the watershed identification and subcatchment discretization in
SWMM model setup for simulating the rainfall–runoff response of the catchment.
7.2 Determination of subcatchment properties
Properties associated with a subcatchment in SWMM are: area, width, % slope, %
imperviousness, soil properties, Manning’s n for impervious and pervious surfaces for
overland flow, depth of depression storage in pervious and impervious area and %
impervious area with no depression storage. Area of the subcatchment discretized in the
present study ranges from 0.092 to 6.65 ha.
7.2.1 Width
Time of concentration and the shape of hydrograph in SWMM simulation depend upon the
assigned width of the subcatchment, which is defined by ratioing the watershed area to the
length of the overland flow. Since the length of overland flow is difficult to determine
accurately, subcatchment width parameter is subjected to calibration to obtain a good match
between observed and simulated hydrographs. In the present study, the width of the subcatchments is determined taking the square root of the subcatchments area, assuming each
subcatchment as a square-shaped region. As the shapes of the discretized subcatchments are
not ideally square, the width parameter is subjected to change during the calibration process.
7.2.2 Slope
‘‘3D Analyst Tools’’ in ArcGIS 9.3 was used for generating slope map from the digital
elevation model of the campus as shown in Fig. 9. For estimating the area-weighted mean
slope of the subcatchments, first the slope map (raster) was converted to point (vector)
using ‘‘Conversion Tools,’’ and then this point shapefile was converted to Polygon
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Fig. 8 a Watershed identification and b subcatchment discretization
shapefile using ‘‘Create Sample Plots’’ option of ‘‘Sampling Tools’’ available in ‘‘Hawths
Tools’’ (Beyer 2004). The area-weighted mean slope of each subcatchment was determined
using ‘‘Polygon In Polygon Analysis’’ option under ‘‘Analysis Tools’’ available in
‘‘Hawths Tools’’ by overlaying the discretized subcatchments on the slope raster.
7.2.3 Gross impervious cover
Gross percent impervious area was derived from the campus layout map. Area covered by
building footprints, road, pavements and other existing infrastructures was measured in
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Fig. 9 Slope map of IIT Kharagpur campus
ArcGIS 9.3. To incorporate the missing impervious features in the map, an additional 10 %
of subcatchment area was added to percent imperviousness.
7.2.4 Soil properties
The rate of infiltration is a function of soil properties in the drainage area, ground slopes
and ground cover. The Green–Ampt equation for infiltration has physically based
parameters that can be estimated based on soil characteristics. The soil parameters used in
this method are: wetting front suction head, saturated hydraulic conductivity and the initial
moisture deficit (i.e., the difference of porosity and field capacity). These parameter values
(Table 1) are assigned as per the soil classes present in the subcatchments.
7.2.5 Depression storage
In SWMM, overland flow takes place when the initial abstraction losses in the form of
depression storage are satisfied. These depression storages are subjected to depletion due to
infiltration and evaporation losses. As the present study considers an event-based simulation, evaporation phenomenon is not taken into consideration. Typical values of depth of
depression storage for pervious and impervious area are taken from the literature (ASCE
1992). For simplicity, it was assumed that the depth of depression storage is homogeneous
in nature in all the subcatchments.
7.2.6 Subcatchment Manning’s roughness
Manning’s roughness for overland flow for impervious and pervious area is taken from the
literature (McCuen et al. 1996). To overcome the complexities involved in determination
of Manning’s roughness for varying surfaces, single value of Manning’s roughness is
assigned for impervious and pervious areas as 0.013 and 0.05, respectively.
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7.3 Conduit properties
Conduit properties consist of channel cross sections and Manning’s roughness. Channel cross
sections were measured at various locations to assign the conduit (drain) dimensions in the
model setup while the Manning’s roughness values were taken from the literature (ASCE
1982). Drainage network in the campus is mostly earthen and partly cemented/concrete/brick
lined in nature; hence, earthen drains were assigned a value of 0.03 for Manning’s roughness,
while for other drains, this value is chosen as 0.01 for simplicity.
8 Design of drainage system and multi-purpose detention pond using
SWMM
SWMM run was carried out for the southern region of the campus using a 24-h rainfall
recorded by Automatic Weather Station (AWS) at Agricultural and Food Engineering
Department in IIT Kharagpur. In the absence of time series data of flow at the outlet for
calibration of model, few point data were recorded by measuring the flow through the
drains at various locations for the rainfall received on Oct 02, 2013 (29.5 mm). These
observed point values were plotted against the model simulated flows in the links (drains)
for assessing the model accuracy (Fig. 10).
From Fig. 10, it is evident that predicted flow by model simulation is of considerable
accuracy for links 60, 33 and 34. As the model assumes a free flow through drains with no
blockage; hence, at few locations, a time lag between observed and simulated flow can be
seen. Observed and simulated values of flows in chosen links are tabulated in Table 5.
8.1 Design of drainage system using SWMM
Model runs are carried out to simulate the rainfall–runoff response of the catchment with
existing drainage network to assess the carrying capacity of drains. Based on the results
Fig. 10 Simulated and observed flows in links/drains
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Table 5 Observed and simulated flows in various links/drains
on Oct 02, 2013
Time (PM)
Link
Observed flow
(m3/s)
Simulated flow
(m3/s)
2:55
33
0.179
0.198
3:11
34
0.126
0.152
3:45
48
0.417
0.17
3:55
85
0.426
0.16
4:05
60
0.189
0.161
obtained, the new cross sections are proposed to avoid flooding and water stagnation
problem.
Figure 11a and b shows the total inflow from the system, total flooding that occurred at
various nodes and outflow simulated at the current outlet for 5- and 10-year return periods,
respectively. Model simulated peaks of total inflow, flooding and outflow with existing
drainage system are shown in Table 6.
In SWMM, runoff generated from each subcatchment from overland flow is directed to
the assigned nodes of that particular subcatchment which is further routed to another
downstream node as inflow from upstream subcatchments. Inflows from upstream subcatchments and lateral inflow from the subcatchments as a result of surface runoff added up
as total inflow in the model calculations. Excess flow in the drains due to inadequate
capacity gets lost as flooding from the nodes. SWMM sums up the flooding from various
nodes to give the total flooding in the system. Total discharge from the system excluding
flooding from the nodes is expressed as outflow.
In order to design an efficient drainage system, numbers of model runs were carried out
using different combination of drain dimensions. Drainage network was modified from its
existing conditions to the extent where it can carry a runoff generated from a rainfall of
10-year return period safely to the outlet without causing any flooding or water stagnation
in the upstream areas.
Figure 12a and b shows the water elevation profile in the drain network for 5-year return
period design storm with existing drainage system and for 10-year return period design
storm with modified drainage system, respectively. Modified drains show no flooding
through nodes for 10-year return period design storms (Fig. 12b), whereas a 5-year return
period design storm results in flooding from all the nodes (Fig. 12a) for existing drain
dimensions. We also performed a brief sensitivity analysis of SWMM for Manning’s n for
overland flow and % imperviousness. We found that the model is not very sensitive to
these parameters, provided the values are varied within a reasonable range. Since, the
SWMM does not provide the inundation depth and flood extent, MIKE URBAN model is
used for simulation of extreme rainfall event of July 29, 2013. Though the total rainfall
depth of July 29, 2013 (187.5 mm) is lesser than the estimated design storm of 10-year
return period (230 mm, Table 4), the maximum rainfall intensity of the former storm was
found to be too high (89 mm/h, see hyetograph in Fig. 2) than the later (46 mm/h, Fig. 7).
Therefore, MIKE URBAN was used to assess the performance of our designed system
against such extreme events (Sect. 9).
8.2 Design of multi-purpose detention pond
One of the objectives of the present study was to design a multi-purpose detention pond to
harvest the rain water as well as to reduce the peak discharge during extreme events.
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Fig. 11 System response for a 5-year and b 10-year return period rainfall with existing drainage network
Table 6 System response under existing drainage system for various return periods
Design storm
Peak inflow (m3/s)
Peak flooding (m3/s)
Peak outflow (m3/s)
5-year return period
8.85
6.29
2.49
10-year return period
11.29
8.51
2.76
Therefore, a seasonal rainfall analysis of the historical rainfall data of IIT Kharagpur is
performed to approximate the required capacity of the pond. Annual rainfall data of
56 years from 1957 to 2012 are used for this purpose. Analysis of the 56 years of rainfall
record revealed that on an average 75 % of the rainfall occurred in monsoon season alone,
i.e., June to September (Fig. 13). These seasonal rainfall data are used to perform a
frequency analysis using L-moments approach. Absolute Zdist-statistic is found to be
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Fig. 12 Water elevation profile: node N38 to N115 a for 5-year return period of rainfall in existing drainage
system (all nodes are getting surcharged), b for 10-year return period of rainfall in modified (design)
drainage system (no node is surcharged)
lowest for generalized logistic (GLO); therefore, it is chosen as the best suited distribution
for our dataset. Seasonal rainfall depth at 2-year return period is chosen to approximate the
depth of pond to be designed. Growth factor for 2-year return period for GLO distribution
is computed as 0.973. Therefore, 2-year return period monsoonal rainfall is the product of
average monsoonal rainfall and growth factor (i.e., 1193.95 mm 9 0.973 = 1161.7 mm).
In aforementioned model simulations, free flow condition at the outlet was assumed
neglecting any backwater effect and drainage congestion at downstream boundary.
However, the existing outlet of the catchment is a canal running outside the campus
boundary (as shown in Fig. 1) which causes backwater effect during rainy season. Furthermore, the simulation reveals the drainage congestion problem at the southern end of the
study area. While investigating, we found that all the major drains carry stormwater from
the upstream subcatchments to the outlet through the problem area. Moreover, as the
campus is secured by erecting a concrete wall along the boundary, no water leaves the area
as surface runoff. In order to provide a solution for the backwater effect from canal and
drainage congestion problem, a multi-purpose detention pond is proposed to divert the flow
from the existing outfall to a new outlet as shown later in Figs. 16a and 17a.
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Fig. 13 Seasonal rainfall analysis (1957–2012) for IIT Kharagpur campus
Primary purpose of a retention pond is to retain the runoff and act as a rainwater
harvesting structure by allowing the standing water to infiltrate for groundwater recharge,
while the detention pond is designed to reduce the peak of outflow hydrograph at the
downstream end by allowing a slower release of incoming flow by means of weirs or
orifices.
Three rectangular weirs of dimension 1 m depth and 3 m width are proposed at one side
of the pond. Depth of the pond was kept as 3 m, thus allowing approximately 2 m depth of
water to be stored before allowing any flow out of the pond. Surface area of pond is kept as
3 ha (300 m length and 100 m breadth). While simulating the outflow hydrograph at the
downstream of the pond, depth of standing water in the pond was assumed to be at same
level as of crest of the weir due to the expected previous runoff from the system as well as
direct rainfall in the pond. For 10-year return period rainfall, corresponding inflow and
outflow hydrograph from the retention cum detention pond is shown in Fig. 14.
Inflow–outflow hydrograph of the pond reveals a slower discharge and reduction in peak
of the outflow hydrograph at the outlet from 10.55 to 4.13 m3/s. This reduction of peak
discharge is very significant in the present study as all the runoff generated from the study
area is drained into a canal running outside the campus boundary. During high rainfall
events, this canal starts running at full flow; hence, discharge from the campus at higher
rate encounters the backwater effect from the canal resulting in flooding at the adjacent
upstream areas. This backwater effect can be avoided or reduced to a significant extent by
reducing the discharge at the outlet.
9 MIKE URBAN (MU) model setup
MIKE URBAN model uses SWMM engine; therefore, it has all the functionalities of
SWMM. However, the advantage of MIKE URBAN over SWMM is the capability to
simulate 2D overland flow and GIS integration unlike in SWMM. Simulation of 2D
overland flow is carried out by coupling MIKE 21 and MIKE URBAN using urban links.
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Fig. 14 Inflow–outflow hydrograph of proposed retention cum detention pond for 10-year return period
rainfall
MU MOUSE is a 1D hydrodynamic pipe flow model which incorporates backwater effects,
flow reversals, and surcharging through the solution of the complete 1D Saint–Venant
equations. MIKE 21 is a distributed 2D hydrodynamic model which numerically solves the
full 2D Saint–Venant equations on a rectangular grid. MIKE FLOOD facilitates the
dynamic interchange of flow between MIKE 21 and MU MOUSE. MIKE FLOOD (DHI
2007) coordinates the simultaneous operation of both models, and transfers flow between
the models based on the difference between water depth in MIKE 21 and the hydraulic
grade line in MIKE URBAN. This linkage takes place through pre-defined inlets and
allows for draining of the overland flow model into the storm sewer model as well as for
surcharging from the storm network model into the overland flow model.
9.1 MOUSE setup
MU MOUSE needs three categories of input data for hydrologic and hydraulic simulations,
i.e., network, catchment and boundary data. Network inputs primarily consist of nodes and
links. Node information is spatial location, dimensions and elevations of nodal structures
such as manholes, basins and outlets. These node structures are connected by links,
including pipes and channels. The main link inputs are type of link and their hydraulic
properties such as roughness and information on upstream and downstream nodes.
Catchment inputs primarily include catchment area and the node at which the catchment
drains. The catchment should drain to a pre-defined node from nodal inputs. The setup is
designed to simulate the flood inundation in the study area by the use of urban links in
MIKE FLOOD for heavy rainfall events. The setup is prepared by discretization of study
area into number of sub-catchments and then defining the hydraulic network through
nodes, links and outlets. There are three types of boundary inputs in MU MOUSE namely
catchment loads and meteorological boundary conditions, network loads and external
water levels. The parameters used here are same as in SWMM.
In order to compare the runoff flow of MU SWMM with MU MOUSE, the runoff
discharge for different rainfall events are taken from MU SWMM using Green–Ampt
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method. Then, the runoff generated file is used in the hydraulic network of MU MOUSE.
The runoff generated from each sub-catchment discharges into their corresponding outlets
(i.e., nodes).
9.2 Two-dimensional model setup
The hydraulic setup which consists of conduits and nodes requires only runoff file generated from the runoff model. This hydraulic setup (1D) is coupled with 2D model, through
urban links in MIKE FLOOD. The 2D model requires DEM which acts as a surface over
which excess water flows. The purpose of DEM is to represent land elevation data. This is
required for the estimation of flood volumes on the surface. Moreover, the resulting
inundation map represents water levels that are usually based on the DEM.
The development of MIKE 21 model in MIKE URBAN MOUSE is through ‘‘2D
Overland Flow’’ which requires DEM as an input to MIKE 21, specification of flooding
and drying depth, Manning’s number (i.e., M = 1/n, n = Manning’s coefficient) of surface
including initial conditions. Nodes are coupled to 2D surface by the use of ‘‘Couple
Nodes’’ given in ‘‘2D Overland Tool’’ in MU MOUSE. Furthermore, it is necessary to
define an equation which facilitates the exchange of flow from MU MOUSE to MIKE 21.
MU MOUSE offers three equations for the flow exchange of water at the inlet between the
1D and 2D models which can be computed using three different methods:
(1)
(2)
(3)
Orifice equation: The flow is governed by a standard orifice Eq.
Weir equation: The flow is described through a weir Eq.
Exponential function: The flow is governed by a simple exponential function.
Out of three methods, the orifice equation is used. The orifice equation requires input as
the ‘‘Max Flow’’ and the ‘‘Inlet Area.’’ The inlet area is only used for describing the flow
exchange between the 1D model and the 2D model. The greater the cross-sectional area,
the greater is the conveyance capacity of the coupling. This parameter corresponds
physically to the surface area of the node. For a node, the inlet area used in the calculation
will be the smallest value of the specified inlet area and the inlet area calculated from the
node diameter (DHI 2011a, b).
The orifice calculation is given by:for jQUM21 j\Qmax ;
pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
ð3Þ
QUM21 ¼ signðHU HM21 Þ CD minðAm ; Ai Þ 2gjHU HM21 j
where QUM21 = Flow from MOUSE to MIKE 21 grid point, m3/s; HU = Water level in
drainage system, m; HM21 = Average water level on the ground, m; Am = Cross-sectional
area of node, m2; Ai = Cross-sectional area of inlet (not used for basins), m2 and;
CD = Discharge coefficient (typically 1); g = Acceleration due to gravity, m/s2.
10 Flood inundation modeling using MU MOUSE
The flood inundation for July 29, 2013 (hyetograph shown in Fig. 1) was simulated in
MOUSE model which is coupled with MIKE 21 in MIKE FLOOD using urban links. Flood
inundation maps are shown for southern part of IIT Kharagpur campus due to its vulnerability to frequent flooding in rainy seasons, especially the G-type quarters which lie
close to the boundary (located at 5 in Fig. 1). The G-type quarters are located at the lowest
elevation in the study area with a highly congested drainage network. Therefore, maximum
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flood extent and water depth profile, for the extreme rainfall event used, are shown for both
existing and designed drain conditions.
Figure 15a and b shows the inundation map and water depth profile at G-type quarters
for existing drainage system. The maximum water depth observed from the water depth
profile near G-type quarters is found to be 70 cm. This simulated depth is approximately
same as narrated by the local residents.
Figure 16a and b shows the maximum flood extent and water depth profile in the drains
after modifying the drainage system (i.e., with design drains as obtained from SWMM
model). These modifications were done considering the limitation of available space for
drain enlargement. Depth of water due to inundation was found to be up to 45 cm for the
chosen event (i.e., for the extreme rainfall of July 29, 2013), which infers that design drains
Fig. 15 a Maximum flood extent and b water depth profile near G-type quarters at point 1 for existing
drains for the extreme rainfall event of July 29, 2013
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Fig. 16 a Maximum flood extent, b water depth profile for designed drains near G-type quarters at point 1
for the extreme rainfall event of July 29, 2013
are not capable to handle such extreme events. In order to overcome this situation during
high rainfall events, simulations are carried out with the design drains and no boundary
wall along G-type quarters [i.e., with approximately 4.5 m of opening in existing boundary
wall near G-type quarters, shown in the blue box in Fig. 17a]. Figure 17a and b shows the
maximum flood extent and inundation depth (4 cm) with design drainage system and no
boundary wall. Inundation depth in our case reduces drastically once the boundary wall is
removed which infers that in addition to design drainage system, specific local problems
should also be taken into consideration. While a one-dimensional model (SWMM) can be
successfully used to design drainage system, two-dimensional model helps to identify
location-specific problems and facilitate alternative measures under different scenarios.
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Fig. 17 a Maximum flood extent (b) water depth profile for designed drains with no boundary wall along
G-type quarters at point 1 for the extreme rainfall event of July 29, 2013
11 Conclusions
Frequent flooding during rainy season in urban areas often causes water logging problems
which not only interfere in the day-to-day life of people, but also carries several health
hazards. Appropriate flood management solutions in urban areas must be provided to
minimize the losses due to flooding. The present study makes an effort to provide a
solution for local urban flooding by designing an efficient drainage system through
modeling. In order to tackle the nuisance of flooding, a thorough analysis of local rainfall is
carried out to assess the potential threat from extreme events during rainy season. Trend
analysis of the rainfall data at IIT Kharagpur campus reveals that the 1-day, 2-days and
3-days annual maximum rainfall as well as the cumulative monsoon season rainfall does
not show any significant trends. However, the number of extreme events (i.e., daily rainfall
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events exceeding the 95th percentile threshold value of 26.5 mm for the entire data period
of 1956–2012) occurring each year shows a significant increasing trend. Frequency analysis of the daily annual maximum rainfall of IIT Kharagpur campus is performed to
estimate the design storms. SWMM was used to design an efficient drainage system to
handle a design storm of 10-year return period safely with a provision of a multi-purpose
detention pond. This multi-purpose detention pond helps in attenuating the peak of outflow
hydrograph at the downstream and also works as a rainfall harvesting structure. During the
study, it is also observed that a significant amount of drainage problem can be solved by
providing proper connectivity in stormwater drains and keeping them clean during rainy
season. Since, SWMM model cannot be used to simulate flood extent or flood inundation
depth due to its one-dimensional nature, MIKE URBAN, a two-dimensional model, was
used for this purpose. Extreme rainfall events occurring in a short time span are supposed
to result in flooding from any drainage system irrespective of their design. In such cases
where a one-dimensional model shows flooding from nodes, a two-dimensional model can
be used to visualize the direction of overland flow and inundation depth over the surface,
and a solution can be obtained based on the specific local conditions. In our case, MIKE
URBAN was used to find the impact of boundary wall in the flooding problem in our study,
which is not possible using a simple one-dimensional model (such as SWMM). We also
found that we can design an efficient drainage system using SWMM to safely handle the
design storm of 10-year return period; however, there is no way out to deal with the
extreme rainfall events. To deal with such specific cases, appropriate design/modifications
can be made by employing a two-dimensional model.
Compliance with ethical standards
Conflict of interest None.
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