Article Type: Research Article

Accepted Article
Urban Flood Estimation and Evaluation of the Performance of an Urban Drainage

System in a Semi-Arid Urban Area Using SWMM

Received 10/08/2016, Revised 05/27/2018, Accepted Jun 12 2018

Ali Moafi Rabori1, Reza Ghazavi2*

1
PhD student, Department of Watershed Management, Faculty of Natural Resources,

University of Kashan, Iran.

2
Associate Professor, Department of Watershed Management, Faculty of Natural Resources,

University of Kashan, Iran.

*
Associate Professor, Department of Watershed Management, Faculty of Natural Resources,

University of Kashan, Iran; e-mail: ghazavi@kashanu.ac.ir.

ABSTRACT: Estimation of urban runoff peak and volume is a fundamental step in

determining the transferring capacity of urban drainage systems. The main aim of this study

was to present an application of the Storm Water Management Model (SWMM) in order to

estimate urban flooding of a semi-arid area (Zanjan city in the northwest of Iran). The

performance of an urban drainage system in the study area was also investigated. According

to the results, SWMM is an effective tool for urban flood estimation in a semi-arid area. In

this study, urban peak flow was simulated via a calibrated model with acceptable accuracy.
This article has been accepted for publication and undergone full peer review but has not
been through the copyediting, typesetting, pagination and proofreading process, which may
lead to differences between this version and the Version of Record. Please cite this article as
doi: 10.1002/wer.1083
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 Based on the results of the model simulation, the capacity of the main canals in the study area

is sufficient for peak runoff transferring for a design storm with 50 year return periods,
Accepted Article
without retrofitting. Whereas, based on local observation and model results, localized and

surface flooding can be observed in some urban areas.

KEYWORDS: flood, design storm, storm water, drainage system, urban runoff

management, SWMM.

Introduction

It is expected that some 70% of the world’s population will live in urban areas in 2050

(UN, 2008). Urbanization has serious effects on the quantity and quality of urban runoff. In

urban areas, natural streams have changed in the artificial drainage network (Antrop, 2004;

Haase, 2009) and the natural and rural area has changed to impervious surfaces.

Consequently, watershed infiltration, groundwater recharge, and evapotranspiration should

decrease (Klocking and Haberlandt, 2002; Rose and Peters, 2001), whereas, the volume and

peak flow of stream water should increase. The increased volume and peak flow of storm

water discharges may cause problems, such as flooding and erosion (Dietz and Clausen,

2008; Schoonover et al., 2006; Wang et al., 2005). Some researchers suggest that urban

development will reduce groundwater recharge via reduction in permeable areas (Brett et al.,

2005; Collin and Melloul, 2003; Schoonover et al., 2006), whereas, some others indicate that

urbanization will increase groundwater recharge because of urban water infrastructure

leakage and reduction in evapotranspiration (Howard, 2002; Lerner, 2002). In urban areas

there is also a variety of pollutant sources. Thus, urban storm water may in some cases be a

significant source of water pollution to receiving waters (Huong and Pathirana, 2013; Pyke et

al., 2011; White and Greer, 2006).

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 Proper management of water resources in urban areas requires consistent prediction of

water quality and quantity. Information on precipitation, evaporation, infiltration, runoff, and
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water quality is needed for such prediction. Rainfall regime in arid and semi-arid areas is

characterized by low, irregular, and unpredictable precipitation (Fengxiang, 2007; Ghazavi et

al., 2010). Consequently, storm water in an urban area has a random nature and short

duration. In such areas, hydrological data collection is difficult because of the high spatial

variability of catchment factors, long dry period between two continual rain storms, and the

high spatial and temporal variations of rainfall and storm water runoff (Osborn and Hickok,

1968; Osborn et al., 1979).

Estimation of peak and volume of runoff is of fundamental importance in order to

determine the flood magnitude, and thus design urban runoff management structures in terms

of dimensions, storage, and transfer capacity, or the treatment rate for treatment/storage

facilities (Chen and Adams, 2007). Also, measurements and monitoring are useful in defining

models and enhancing design procedures to improve the efficiency of systems for water

treatment, but because of the high temporal variation of rainfall and runoff, hydrological data

collection in arid and semi-arid areas may be expensive and time-consuming. Therefore, it is

difficult to collect a functional database of runoff quality and quantity, especially in arid and

semi-arid areas, where the monitoring network is characterized by a lack of discharge gauged

stations and low frequency of precipitation (Gorgoglione et al., 2016).

Urban flood simulation through modeling is useful in understanding drainage system

problems and estimating the extent of surcharging or flooding. Different models were

developed for urban runoff management, urban planning, urban design, and development.

The Environmental Protection Agency Storm Water Management model EPA-SWMM

(Huber and Dickinson, 1992) is one of the urban watershed models which is effective in

simulating the rainfall–runoff process for urban storm water. A combination of SWMM and

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 digital elevation model (DEM) within a GIS platform was used for simulating urban

watershed runoff (Gouri and Srinivas, 2015; Gumbo et al., 2002; Lei et al., 2015; Satyaji Rao
Accepted Article
and Ramana, 2015; Smith et al., 2006).

Because of population growth, urban areas have been developed during the last few

decades. In arid and semi-arid regions, immigration from rural to urban areas increased as a

result of water shortage. In these regions, surface runoff is an important component of the

hydrological cycle (Rejani et al., 2008; Thomas et al., 2009). But only a few studies have

attempted to estimate and evaluate the intensity and quantity of urban runoff in such regions.

The main aim of this study was to present an application of SWMM in order to estimate

urban flooding in a semi-arid area (Zanjan city in the northwest of Iran). The performance of

an urban drainage system in the study area was also investigated.

Methodology

Site Description. Zanjan City watershed is located in the center of Zanjan province,

in the northwest of Iran (latitude 363826″ and 364220″N, longitude 482629″ and 4835

02″E). Based on historical evidence, Zanjan city has had a sewerage network for 300 years.

Urban surface water was collected and conveyed via wells or bars to four underground main

canals and transferred toward outlets. These systems were located in the center of the city and

destroyed as a result of urban development 30 years ago. The city experienced rapid

development and population expansion from 1956 to 2012. The urban drainage system of the

Zanjan city watershed has been designed and developed in recent years. Artificial canals play

an important role in flood-routing during storm events. Flow direction is from north to south

of the urban area and ends in the Zanjanrood River (Figure 1). An earth dam named

Gavazang was built in the north of the city and limits upstream surface water and floods. The

total area of the study watershed is about 39 km2, in which 70 to 80 % are impermeable

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 surfaces in the form of buildings, roads, footpaths, and sports facilities. The morphology of

the study city is typically foothills and piedmont plain. Altitude in the study area ranges
Accepted Article
between 1590 m above mean sea level in the southern plain to 1773 m at the northern

mountain. The area has a mean annual rainfall of 290 mm. The main part of the rainfall

occurs in the autumn and spring seasons.

SWMM was calibrated and applied to computation of flows in the storm drainage

system, surcharged flow at conduits, and to simulate the inundation of the urban drainage

system in Zanjan city watershed. SWMM is a dynamic rainfall–runoff simulation model used

for single event or long-term (continuous) simulation of runoff quantity and quality from

primarily urban areas. SWMM was developed under the support of the U.S. EPA (Huber and

Dickinson, 1992).

In this study, flood conditions in the study area were simulated using SWMM

(Version 5). The whole study area was divided into 16 subwatersheds. Each subwatershed

considers a junction and its storm drainage conduits to the outlet area (Hsu et al., 2000).

According to the routing portion of SWMM, this runoff transports through a system of pipes,

channels, storage/treatment devices, pumps, and regulators (Gironás et al., 2009). Thus, no

surface runoff routing is considered.

Subwatershed Division. The study urban drainage system was identified based on

the land use map, topographic map (1/2000), building blocks, and flow direction in curbs,

gutters, and main canals. The primary map was accurate via land survey and flow direction

control in canals. The basic data for each subwatershed, such as average slope, perimeter,

area, and width were derived via this division. The subwatershed map and some properties of

subwatersheds are shown in Figure 1 and Table 1.

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 Outline of the Urban Drainage System. In order to identify the subwatershed

boundaries, canal-network, and link-node; flow direction in all curbs, gutters, and main
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canals were controlled via land survey. Junctions were determined anywhere that quick

changes occurred in a conduit characteristic (change in depth, width, bed slope, roughness

coefficient, and shape) or when a tributary canal was connected to the main canals. The

properties of the urban drainage network (surface and bottom elevation, maximum water

depth of junctions, length, shape, diameter, and slope of the storm drainage conduits) were

extracted via related maps and direct survey measurement.

Determination of Model Parameters. Surface area, impervious and pervious area,

average terrain slope, average width of overland path, average of subwatershed width,

percent of impervious area, depth of depression storage on impervious and pervious area,

Manning roughness coefficient, and infiltration were determined based on the SWMM user’s

manual and estimated according to the properties of the studied area. The DEM was

generated from a topographic map. The average terrain slope was derived from DEM using

ArcGIS 9.3 software. Average subwatershed width was calculated via eq 1:

28 2
- - (1)
28

where is width (m), A is the area of the subwatershed (km2), and is the compactness

coefficient. The compactness coefficient is calculated via eq 2 for subwatersheds with a

compactness coefficient greater than 1.128. Otherwise, based on the user manual of SWMM,

the hydrologic unit was divided by the average maximum overland flow length:

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 0 282 (2)
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where is the perimeter of the subwatershed (km).

The Manning roughness coefficient was obtained from McCuen et al. (1996) and

ASCE (1982) manuals. Depth of depression storage on impervious and pervious area

parameters was extracted from the values suggested by ASCE (1992). The curve number

method was selected for modeling the infiltration process. A land use map was prepared via

processing of Thematic Mapper images in IDRISI Selva and ArcGIS 9.3 software. Based on

the land use map, five classes of land use including residential area, green space, main roads,

dense rangeland, and degraded rangeland or urban flatted land, were determined. Soil texture

was derived from soil surveys of the deserts atlas in Iran and controlled with soil studies of

the Agriculture and Natural Resources Research and Education Center of Zanjan. The soil

hydrological group map was determined based on NRCS Hydrologic Soil Group Definitions

in the user manual of SWMM (Rossman, 2009).

Percent of the impervious area was estimated based on the land use map (Figure 2).

The surface area occupied by urban areas, main roads, green space, dense rangeland, and

destroyed rangeland was 82.9, 5.5, 3, 0.4, and 8.2% respectively. The design hyetographs, as

a main input of SWMM, were based on reformatted rainfall intensity–duration–frequency

(IDF) curves developed for the study area. It is supposed that, when rainfall duration is equal

to the time of concentration, maximum flood should occur. Thus, rainfall hyetographs with

rainfall duration equal to the time of concentration were created for each subwatershed. In

this study, the time of concentration for all subwatersheds was computed via the TR-55

model suggested by the Natural Resources Conservation Service (2009).

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 Rainfall hyetographs were extracted based on the method of Ghahreman and Abkhezr
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(2004). Eq 3 indicates the relationship between rainfall IDF curve parameters in Iran:

(3)

where is rainfall depth (mm) with a time increment of ″t″ and a return period of T. and

are the coefficients of rainfall duration (and for rainfall less or equal to 1 h are 0.1299 and

0.4952, respectively). , , and are coefficients of rainfall duration (for rainfall less or

equal to 2 h, is 0.4608, 0.2349 and 0.62, respectively). is hourly rainfall with a 10 year

return period. is calculated via eq 4.

(4)

(the average of the maximum daily rainfall) was calculated based on the maximum

daily rainfall from 1969 to 2015 in Zanjan station. Rainfall hyetographs of the study area for

different return periods and rainfall duration (10, 20, 30, and 40 min) were prepared using eqs

3 and 4. A design rainfall hyetograph was developed in 10 min increments for a 40 min storm

with five different return periods: 2 year, 5 year, 10 year, 20 year, and 50 year, at Zanjan

station using the alternative block method (Figure 3).

Hyetographs of each hydrological unit were prepared separately (16 hyetographs

based on Ghahreman and Abkhezr, 2004) and presented to the model. For each outlet, a

separated hydrograph was created via the SWMM model. Because of a lack of discharge

gauged equipment and low frequency of precipitation; rainfall, and runoff properties (depth,

discharge, and velocity) were measured for only 3 specific rainfall events on 2nd, 3rd, and

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 10th May, 2016, in the outlet of subwatershed number 16, to calibrate the flow portion of the

models (Di Modugno et al., 2015). For each rainfall event, changes in the runoff properties
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was measured continuously at the outlet of the selected subwatershed until related runoff was

cut off. The rainfall record and runoff data were analyzed to determine potential target storms

for calibration and verification.

Results and Discussion

Model Calibration. Model calibration is the process of running a model using a set

of input data and comparing the model results to actual measurements of the system. Long-

term, continuous SWMM simulation results were compared to the observed runoff properties

(Chen and Adams, 2005). The evaluation criteria of root mean square error (RMSE) were

used to verify the accuracy of the model. The RMSE values should be used to distinguish

model performance in a calibration period with that of a validation period.

Root mean square error (RMSE) for discharge is based on eq 5:

(5)

where n is the number of observations in the time series and Qs (i) and Qo (i) are the simulated

and observed discharges, respectively. The performance of the urban drainage system against

the floods with different return periods was investigated using the model setting and rainfall

design with different return periods. Some of the physical characteristics of the subwatershed

and calibrated SWMM parameters are shown in Table 2.

After model calibration, the accuracy of the model was examined using RMSE

evaluation criteria. The measured and simulated values of peak flow in one of outlets are

compared in Table 3; lower values of RMSE indicate a better fit. Because RMSE measures

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 error in estimation, the smaller the values of these measures, the closer the modeled

estimation. In an ideal condition, values of zero for RMSE would mean that the estimated
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value is the same as the measured value. Based on the results, SWMM shows a good

accuracy for urban runoff forecasting in the study area (estimated RMSE < 0.005). Figures 4

to 6 indicate comparison between simulated and measured hydrographs for three specific

rainfall events.

Surface Runoff Simulation. The simulated surface runoff, infiltration loss, total

rainfall, and final surface storage are shown in Figure 7. According to the results, with

increasing return period, the total rainfall and runoff coefficient were increased, whereas, not

obviously, increase was observed for infiltration loss and final surface storage. After

successful testing of the model, the 40 min design storm for 2, 5, 10, 20, and 50 year return

periods was considered, to check the storm water drainage network efficiency in the study

area. Maximum flow discharge for different return periods at different outlets of the urban

drainage system are displayed in Table 4.

Urban Flood Forecasting. Floods can occur whenever water surface at a node

exceeds the maximum defined depth. Capacity (the ratio of depth to full depth) is one of the

variables that should determine the potential of runoff transport by conduits in an urban

drainage system. In flow routing, when entry flow to a junction exceeds that of the transport

capacity of the system, a flood will occur. The runoff transport capacity of different outlets

with different return periods, in Zanjan city watershed, are shown in Table 5.

Surcharging into an urban area should occur when the capacity of runoff transport is

equal to, or more than, one. In the study area, no surcharge outlets in the different return

periods were observed. Also, the results show that the capacity of runoff transport in minor

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 canals is greater than main canals and in severe rainfall, these parts need more consideration.

It means that minor canals, curbs, and gutters will be flooded in severe rainfall and cause
Accepted Article
local flooding in urban areas of the study area.

In general, junction overflow and pressured pipeline flow are two indexes for urban

flood forecasting. Overflow will cause inundation around the junction area. When pipelines

are on pressure, it means that the pipeline is full of water and the neighboring junction will

overflow; thus, the pipeline may need to be enlarged. According to the results, peak flow

increased with an increasing return period, but because of the very big dimensions of the

canals, no overflow was forecasted for the main canals in the studied area. Also, no pressured

pipeline flow was predicted; according to the land survey, some of the curbs and gutters were

experiencing flooding conditions.

In this study, an urban drainage system was simulated at a large scale. In order to

determine the overflow curbs and gutters, an urban drainage system should assess modeling

at the small scale. In this study, urban peak flow was simulated via a calibrated model, with

acceptable accuracy. The simulated peak flows were on average about 9% greater than those

measured, and the adjustment in time was also moderately accurate. Ovbievo and She (1995)

and Temprano et al. (2005) obtained an error of 25% and 20%, respectively, in the values of

the peak flows simulated during a process of validation of the SWMM An underestimation in

peak runoff and approximately 10% error was reported by Naubi et al. (2017).

Conclusions

Because of land use changes arising from urbanization, natural streams have been

converted to the artificial drainage network. This process can lead to urban flooding in rainy

seasons. In this study, surface runoff in Zanjan city was evaluated via SWMM for different

return period design precipitation. According to the results, the SWMM model has

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 considerable predictive capability for runoff evaluation in semi-arid regions when it is

correctly calibrated via measured data. Modeling results showed that the main urban drainage
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network of the Zanjan city watershed has an acceptable capacity for transmitting the runoff

and flood waters in the studied urban area. Additionally, during rainfall in the studied areas,

no inundation problem exists at the large-scale, however, several places with inundation

problems were identified during the survey. The large amount of building materials and

garbage that have been dumped in the canals could cause this kind of inundation.

Maintenance and cleaning of paths and streambeds should increase the capacity of the main

canals and decrease inundation problems. An efficient method for reducing the surcharge

surfaces in these regions is to raise the people's culture and knowledge about urban runoff;

storm water management, maintenance and monitoring; and adequate design and proper

construction of runoff collection systems.

A high north to south gradient caused high velocity urban runoff in the drainage

system. Where curbs and gutters connect to each other floods occur. Where high intensity

rainfall causes high runoff discharge to the main canals, incorrect design of curbs and gutters,

lack of pollutant traps, and inadequate transfer capacity could cause flooding. The simulation

results of this study should be used to create the optimum design of the main canals of the

urban drainage system and prevent flood damage by redesigning and enlarging the capacities

of curbs and gutters in storm drainage systems. Also, the results of the simulation can help

investigate the possibility of urban runoff harvesting, as well as locating suitable sites to store

runoff in arid and semi-arid regions. In general, because of water shortage in arid and semi-

arid areas such as Zanjan city, applying rainwater harvesting systems and Low Impact

Development (LID) techniques such as bioretention facilities, rain gardens, vegetated

rooftops, rain barrels, and permeable pavements should decrease inundation and increase

water availability. Furthermore, a map of potential inundation can be prepared by the model

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 and flood-mitigation measures established as a part of the decision support system for the

flood control authority of minor canals, curbs, and gutters of an urban drainage system.
Accepted Article
Acknowledgments

This work was supported through the University of Kashan in Iran as a Ph.D. thesis.

The authors are grateful to the university for this generous support.

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 Table 1—Several properties of the subwatersheds and urban drainage system of Zanjan city

watershed.
Accepted Article
Canal Main Subwatershed Shape of outlet conduit Dimension of outlet
number canal area (km2) conduit
length Max. Bottom
(km) depth width
(m) (m)
1 2.83 3.7 Open rectangular canal 1.90 4
2 2.35 1.1 Closed rectangular box conduit 1 1
3 3.6 4.6 Closed rectangular box conduit 2.5 4
4 0.24 0.3 Open rectangular canal 2 4
5 0.15 0.3 Open rectangular canal 1 1
6 1.12 0.9 Closed rectangular box conduit 1.5 2
7 4.3 4.9 Closed rectangular box conduit 2.5 5.5
8 4 4.5 Closed rectangular box conduit 2.2 4
9 0.03 0.2 Closed rectangular box conduit 0.8 0.8
10 1.46 1.5 Standard circular pipe 1.5 -
11 1.02 1.6 Standard circular pipe 1.4 -
12 0.16 0.4 Open rectangular canal 2 2
13 0.19 0.1 Classic Louisville semi-elliptic 1.5 -
sewer shape
14 0.14 0.3 Closed rectangular box conduit 1 1
15 5.05 6.1 Closed rectangular box conduit 2 4
16 2.72 7.9 Open rectangular canal 2 4

Table 2—Several physical characteristics of the subwatershed and calibrated SWMM

parameters.

Parameter Value Parameter Value

Average area of subwatersheds (ha) 60.24 Percentage area with no depression storage 19.1
Average width of subwatersheds 437 Average slope of subwatersheds 12
(m)
Manning’s coefficient for 0.011 Impervious area depression storage, mm 0.8
impervious area
Manning’s coefficient for pervious 0.2 Pervious area depression storage, mm 3
area

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 Table 3—Calibration of the model results with peak flow.

Rainfall–runoff events Observed peak flow (m3/s) Simulated peak flow (m3/s) RMSE
Accepted Article
02 May 2016 0.088 0.09 0.005

03 May 2016 0.018 0.02 0.003

10 May 2016 0.022 0.02 0.001

Table 4—Maximum flow discharge (m3/s) of different outlets at different return periods.

Outlets Return period (years)

T=2 T=5 T = 10 T = 20 T = 50
1 0.027 0.116 0.201 0.295 0.484
2 0.027 0.107 0.177 0.249 0.348
3 0.120 0.506 0.866 1.255 1.802
4 0.011 0.043 0.068 0.092 0.129
5 0.008 0.026 0.043 0.061 0.089
6 0.020 0.073 0.123 0.177 0.258
7 0.063 0.334 0.607 0.914 1.,374
8 0.071 0.283 0.495 0.735 1.102
9 0.009 0.032 0.050 0.066 0.093
10 0.029 0.106 0.181 0.261 0.381
11 0.036 0.126 0.211 0.302 0.440
12 0.014 0.047 0.078 0.110 0.156
13 0.014 0.017 0.028 0.039 0.054
14 0.018 0.061 0.092 0.126 0.177
15 0.068 0.285 0.503 0.749 1.129
16 0.160 0.654 1.140 1.656 2.423

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 Table 5—Capacity of runoff transport in different outlets at different return periods in Zanjan

city watershed.
Accepted Article
Outlets Return period
T=2 T=5 T = 10 T = 20 T = 50
1 0 0.01 0.02 0.02 0.03
2 0.04 0.08 0.12 0.15 0.19
3 0.01 0.03 0.04 0.04 0.06
4 0 0.01 0.01 0.01 0.02
5 0.02 0.03 0.05 0.06 0.07
6 0.01 0.02 0.03 0.04 0.05
7 0.01 0.03 0.05 0.05 0.08
8 0.02 0.04 0.05 0.07 0.09
9 0.02 0.04 0.05 0.06 0.07
10 0.06 0.1 0.13 0.16 0.19
11 0.05 0.09 0.12 0.14 0.17
12 0.01 0.01 0.02 0.02 0.03
13 0.01 0.01 0.02 0.02 0.03
14 0.02 0.03 0.04 0.05 0.06
15 0.02 0.04 0.06 0.08 0.10
16 0.02 0.04 0.05 0.06 0.08

Figure captions

Figure 1—Location of Zanjan city watershed in Iran; and subwatersheds and main canals of

urban drainage system in Zanjan city watershed.

Figure 2—Land use map of the study area.

Figure 3—40 min design hyetograph developed for Zanjan station, using the alternative block

method at different return periods.

Figure 4—Calibration outfall hydrograph for rainfall–runoff event on 2nd May, 2016.

Figure 5—Calibration outfall hydrograph for rainfall–runoff event on 3rd May, 2016.

Figure 6—Calibration outfall hydrograph for rainfall–runoff event on 10th May, 2016.

Figure 7—Simulated surface runoff at different return periods design rainfall.

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Accepted Article

Figure 1.

Figure 2.

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 T=2
Accepted Article
8.0
T=5
Rainfall intensity (mm/min)

7.0
T=10
6.0
T=20
5.0 T=50
4.0
3.0
2.0
1.0
0.0
10 15 20 25 30 35 40
Rainfall duration (min)

Figure 3.

0.1
0.09
0.08
Discharge (m3/s)

0.07
0.06
0.05
0.04
0.03
0.02
0.01
0
0:00 4:48 9:36 14:24 19:12 0:00 4:48
Time (h)
simulation observation

Figure 4.

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 0.025
Discharge (m3/s)
0.02
0.015
Accepted Article
0.01
0.005
0
0:00 4:48 9:36 14:24 19:12 0:00 4:48
Time (h)
simulation observation

Figure 5.

0.025
Discharge (m3/s)

0.02
0.015
0.01
0.005
0
0:00 1:12 2:24 3:36 4:48
Time (h)

simulation observation

Figure 6.

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 3
Total rainfall
Surface runoff 2.417
2.5
Accepted Article
Infiltration loss
2.017
2 Final surface storage
1.717
Value (mm)

1.554
1.5 1.4
1.196

0.933 0.929
1
0.649 0.602 0.609 0.615 0.625
0.592

0.5
0.239 0.212 0.249
0.152 0.183
0.104

0
2 5 10 20 50
Return period

Figure 7

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