Available online at www.CivileJournal.org
Civil Engineering Journal
Vol. 3, No. 12, December, 2017
Calculation of the Spatial Flooding Intensity with Unit Flood
Response Method in the Tangrah Watershed, Iran
M. Gharib a, B. Motamedvaziri b*, B. Ghermezcheshmeh c, H. Ahmadi d
a
b
PhD Candidate, Department of Watershed Management, Science and Research Branch, Islamic Azad University, Tehran, Iran
Assistant Professor, Department of Watershed Management, Science and Research Branch, Islamic Azad University, Tehran, Iran ..
c
Assistant Professor, Soil Conservation and Watershed management institute, Agricultural Research, Education and Extension
Organization (AREEO), Tehran, Iran.
d
Full Professor, Department of Watershed Management, Science and Research Branch, Islamic Azad University, Tehran, Iran.
Received 10 November 2017; Accepted 28 December 2017
Abstract
Increased flooding in recent years indicates that most parts of the country are subjected to periodic and destructive flood
attacks. Therefore, the identification of high-risk areas with potential runoff production within a watershed area is one of
the most important measures in flood control and reduction of the damage caused by it. In this study, the quasi-distributional
ModClark method was employed to simulate the hydrograph of flooding, and the unit flood response method was applied
to determine the intensity of flooding of different areas of the Tangrah watershed, Iran. For this purpose, the ModClark
model was first calibrated and verified. Thereafter, the design of rainfall with 50 and 100-year return periods (Tr ) was
extracted at the Tangrah station and the design flood was calculated with the above-mentioned return periods. By
combining the curve number layers, slope, precipitation, and flow distance, homogeneous units were obtained in terms of
the flood. The effect of each homogeneous unit on the total watershed output was obtained by the removal of each unit and
implementation of the rainfall-runoff model. According to the 100-year return runoff production potential, homogeneous
units of 116 with a fi (0.54 m3/ s. km2) were identified as the most effective cell in the Tangrah watershed area, which
could be explained by the soil type, vegetation, and other physical factors of these units.
Keywords: ArcGIS; Distributed Model; Flooding Map; ModClark Model; Unit Flood Response.
1. Introduction
Flood management is carried out at four levels, these include prediction, preparation, prevention, and evaluation of
damage [1]. One of the measures to reduce the risk of flood in the lower reaches is to resort to flood-control at its source.
Therefore, it is important to identify flood-prone areas in watersheds for flood-control operations. In determining the
flooding of large watersheds, it is essential to divide the watershed into hydrological units and investigate the potential
of each unit in terms of flood participation at the outlet of the watershed. One of the methods for identifying flood-prone
areas is the unit flood response method [2]. Generally, the flood-prone changes are controlled by three factors: soil,
vegetation, and topography [3]. Several studies have been conducted on flooding. Juracek (2000) identified the priority
of flood-prone sub-basins in large watershed areas of 150 to 6600 km2 in the state of Kansas, USA. In this study, the
effect of spatial distribution of rainfall intensity was studied. The results revealed that the differentiation of flood-prone
sub-basins was very limited [4]. Saghafian et al. (2002) presented a novel method based on the development of the
concept of time-area for distributed modeling. In this method, a digital elevation model, slope, flow direction, and flow
* Corresponding author: bmvaziri@gmail.com
http://dx.doi.org/10.28991/cej-030961
This is an open access article under the CC-BY license (https://creativecommons.org/licenses/by/4.0/).
© Authors retain all copyrights.
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Civil Engineering Journal
Vol. 3, No. 12, December, 2017
density maps were used [5]. Foody et al. (2004) employed the HEC-HMS model to simulate the flood in order so as to
identify flood-sensitive areas in an area in western Egypt which led to the identification of two sensitive areas [6]. Knebl
et al. (2005) conducted a modeling of the flood streaming in the summer of 2002 using HEC-HMS and HEC-RAS
software and radar data in the San Antonio Watershed in the United States. They used the ModClark method to convert
rainfall into runoff and calibrated watershed parameters manually. The results showed that the model was a suitable tool
for regional hydrological forecasting in the basin [7].
Khosroshahi and Saghafian (2005) investigated the response of Damavand watershed sub-basins using the HEC-HMS
model. In this research, the contribution of each sub-basin was calculated in peak discharge of the output flood such that
no single-variable relationship exists between the flood index and other characteristics of the sub-basins, including the
curve number and ground gradient [2]. Studies by Linde et al. (2008) demonstrated that distributive models were better
than Lumped reality models in terms of showing the reality [8]. Plate (2009), in his paper, divided the hydrological
models for flood management according to scale into models based on rectangular grids, models based on sub-basins,
and models based on response units, and in terms of determining the flood hydrograph on the basis of given frequencies
referred to both in continuous and real-time modeling [9]. Paudel et al. (2009) compared the distributed and Lumped
models using radar data and HEC-HMS hydrologic software. The results of this study showed that by using the same
CN values, the ModClark method offered better results than Clark's [10]. Chidaz et al. (2009) evaluated the HEC-HMS
model in the Kasilian watershed. In this research, by using the sequential elimination method of sub-basins, the role of
all sub-basins in the outlet flood hydrograph was determined. The results showed that the study of participation of subbasins in the outlet flood was not directly related to their area [11]. Saghafian et al. (2010) studied the flooding of the
Rudzard watershed area by using the unit flood response method in sub-basins and cellular units. The results indicated
that the largest and closest sub-basins to the outlet or the furthest and smallest basins did not necessarily have the highest
and lowest impacts on the maximum flood discharge [12].
Golian et al. (2010) simulated the spatial distribution of rainfall with the Monte Carlo (MC) method and the mean
Huff pattern for all rainfall durations was imposed for the temporal distribution. For each of the MC run, the random
weight assigned to every sub-watershed follows the Probability distribution Function (pdf) of weights in historical
rainfall events. The HEC–HMS model with two different infiltration methods namely SCS–CN and Green–Ampt and
Muskingum river routing were adopted as the hydrologic model. After the calibration and validation of the model for
Madarsoo watershed in Golestan province in Northeastern Iran, the MC simulations were performed for 1,2, 6 and 12 h
durations. The outputs from the SCS–CN method exhibit only a slight increase in threshold values with respect to
duration and was not in the range of our expectations from watershed response, i.e. the rainfalls with greater durations
should be greater in depth to produce a specific peak discharge. For the Green–Ampt infiltration method, the rainfall
thresholds with 50% probability associated with the critical discharge under dry soil moisture condition were 44.5, 49.0,
64.2 and 94.6 mm for 1, 2, 6 and 12 h durations, respectively. Results for July 2001 flooding revealed that the cumulative
rainfall intersected all 10%, 50% and 90% rainfall threshold curves but for July 2005 flooding the 10% curve was only
intersected by the cumulative rainfall curve. The advantage of MC-derived rainfall threshold curves in real-time
operations is that decision-makers have the flexibility to adopt a curve more consistent with flood observations in the
region [13].
Ghavidelfar et al. (2011) compared Clark lump and ModClark distributed models in the Randan watershed in Tehran
province. The results revealed that both models accurately simulate flood hydrograph. They argued that ModClark
distributed model in the estimation of time to peak and runoff volume showed better results compared to peak discharge
[14]. Bakhtyari-kia et.al. (2012) utilized an artificial neural network to identify areas with a potential of runoff production
in the Johor River watershed in Malaysia [15]. Shafapour Tehrani et al. (2013) identified areas with a potential of runoff
production using RS and GIS Remote-Sensing techniques and two different approaches of Decision Tree and
composition of abundance and logistic regression in the Malaysian Kelantan basin. Maps of the altitude digital model,
slope form, geology, river network, power of flow index, rainfall, land use, moisture index and slope were used as spatial
data. Finally, a potential runoff production map was prepared [16]. Halwatura et al. (2013) simulated runoff in the
tropical region of Attanagalu Oya from the HEC-HMS model. Their research results demonstrated that the Snyder’s unit
hydrograph method is more accurate in comparison with the Clarke unit hydrograph method in flow simulation [17].
Shabanlou (2014) in a study titled “Flood Hydrograph Calculation Using Different Methods in the Karun River”
simulated flood hydrograph in Karun watershed using the SCS Model with HEC-HMS Software, and compared this
model with the ModClark model using GIS with the use of both distributed and lump mathematical models. From the
field data, the results of the distributed model are closer to the recorded hydrograph of the basin [18]. Studies by Jiang
et al. (2015) in relation to rainfall-runoff modeling, parameter estimation and sensitivity analysis in Luanhe Province,
China, showed that the distributed model has a better performance than the Lumped model [19]. Thomas and Roy (2016)
investigated the comparison of hydrograph extraction in the Bharathapuzha River Basin. According to this study, because
in most areas without statistics, the preparation and development of unit hydrograph is difficult, therefore, the
incompatibility of observation simulator hydrograph can be attributed to the large area of the watershed [20].
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Saghafian et al. (2016) investigated a coupled ModClark-curve number rainfall-runon-runoff model. In this Research,
a novel rainfall-runon-runoff mathematical model is developed via Soil Conservation Service (SCS) infiltration and
ModClark rainfall-runoff coupled models. After deriving model formulation, three different spatial patterns of curve
number (uniform, downstream increasing, and decreasing) in conjunction with various rainfall durations and intensities
were investigated under with and without runon scenarios over a V-shaped watershed. The results indicated that there
was lower surface runoff volume and peak discharge in all cases when runon was accounted for. In Particular, in regions
with low curve number, there were major differences between the hydrographs simulated by the commonly practiced
no-runon model and the presented runon model. Moreover, the runon effect in case of decreasing curve number in
downstream direction was more pronounced than that of the increasing case. However, this effect decreased with depth,
intensity, and duration of rainfall [21]. Rezaei et al. (2016) in their study "Spatial variability of flooding using unit flood
response in the Khanmirza watershed," showed that flooding potential increased from the downstream to the upstream
watershed, showing the importance of management and flood prevention and watershed management plans at the source.
In this research, the objective is to determine flooding in distributional and sub-basin forms and to compare them with
the Tangrah watershed [22].
2. Materials and Methods
2.1. Study Area
Tangrah watershed with an approximate area of 186,000 hectares is part of the Madarsoo watershed in Golestan
province in Northeastern Iran. The runoff from this watershed zone enters the Golestan dam through the Dough River
(Figure 1).
Figure 1. Position of study area (Madarsoo, Tangrah watershed and Dough river)
The average annual rainfall of this basin is 425 mm and the average annual temperature is 14.3 oC. There are many
differences between Tangrah subbasin and other subbasins located in upstream from the viewpoints of climatic
condition, plant cover, geology, physiography and geomorphology. In study area, maximum Drainage density exist in
tow sub-basins Dasht and Dasht -e- Sheikh and maximum main stream slope in Tangrah and ghizghaleh subbasins ,
respectively.
The study area has 7 stations, contain: climatologic, Normal rain gauge and Rain recorder. the Tangrah watershed
has two hydrometric stations at Dasht and Tangrah, the only active hydrometric station being the Tangrah station. The
rain-gauge station of the Golestan National Park is the only station with adequate statistics on the basin [23]. The study
area is one of the most vulnerable flooding areas in Iran. In the past two decades, the region encountered devastating
floods, e.g. in summer of 2001, 2002 and 2005 and spring of 1992 [13]. Among the factors influencing the determination
of the Tangrah watershed, Iran, for studying this Research are: the area,the morphometric and morphological diversity
of the region, the existence of an active hydrometric station in the catchment outlet, the existence of the active rexording
rain gauge station inside the basin, the flooding potential and the high runoff production potential, the existence of a
sufficient number of rainfall events with the presence of rainfall-runoff data in the area. Table 1 shows some of the
physiographic characteristics of the sub-basins of the Tangrah watershed, and Figure 2 depicts digital elevation model
of the study area.
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Vol. 3, No. 12, December, 2017
Table 1. Area and perimeter of sub-basins of Tangrah watershed
Mean slope (percent)
perimeter (km)
area (percent)
Area ) km2 (
Subbasin
18
17
19
24
15
50
242
54
143
63
15
147
664
40
8
24
7
1
20
100
752
146
453
125
12
372
1860
Nardin
Dasht-e-Sheikh
Cheshmehkhan
Ghizghaleh
Dasht
Tangrah
Total
Figure 2. Elevation Digital Model Map of Tangrah Watershed
2.2. Research Method
The required information for this study is a topographic map of 1: 25000, a CN map (Figure 3), hyetograph and
hydrographs related to each rainfall event during a long-term period. The hyetograph and hydrograph of the 38-year
statistical period were prepared from the Golestan Regional Water Authority and the Water Research Institute. After
preparing the DEM (Digital Elevation Model) map, all the stages of the watershed model were prepared, including the
flow direction, flow accumulation, and the river and the sub-basin boundary map, using the GIS.
Figure 3. Map of curve number of Tangrah Watershed in moderate humidity
In this research, the inputs of the rainfall-runoff model were extracted and then calibrated and validated. In the next
step, in order to determine the flooding of homogeneous units and sub-basins with unit flood response method, sequential
removal and replacement of these units and simulation of flood hydrographs for designed rainfall were carried out. Then
the effect of each homogeneous unit and sub-basin on the total output hydrograph in the watershed was calculated. The
total number of simulations by the model is equal to the total homogeneous units.
In this method, the effective precipitation in each cell with a delay time is proportional to the length of the movement
of that cell to the watershed outlet. The time of travel of each cell to the outlet of the watershed is proposed by relation
(1) [24].
��
=
�
∗
��
(1)
��
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Vol. 3, No. 12, December, 2017
Where Tcell is the travel time from each cell to the watershed outlet, Tc is the watershed time of concentration, Lcell
the distance between each cell to the watershed outlet, and �� the maximum length of the water flow in the watershed.
At the end, the obtained hydrograph is processed according to the relationship in the linear reservoir.
�
=
∗
(2)
�
In which, � is the reservoir at time t, Ot the output of the reservoir at time t, and K is the Clark reserve coefficient.
In this study, software based on the ModClark method and developed by Alvankar et al. (2006) in the Visual Basic
environment has been used [25]. By examining the available data, seven rainfall events with accessible rainfall-runoff
information were selected. For calibration and validation of the incident model, the events were classified into two
categories and five events were selected for calibration and two events for validation. At the time of the occurrence of
each flood, by using the daily precipitation recorded at the rain gauge stations inside and around the Tangrah watershed,
the spatial distribution of the storms was extracted using an Inverse Distance Weighted (IDW) method in the GIS
environment. The time distribution of the storms was also determined using the rain gauge data recorded at the Golestan
National Park's rain gauge station. The time of concentration of the Tangrah watershed was calculated using the Bransby
Williams method (recommended for basins larger than 50 square miles). The reserve coefficient was used by a graphical
method [26] and as a preliminary estimation in the calibration step. The watershed curve number was also calibrated
using a coefficient. The steps in implementing the Clark model are presented in Figure 4.
Flood
Grid cell Data
Hyetograph Data
Input
Tc=?
Input
CN
Coe=?
(Ia/S)
Manually Change Cn Coe.
Gridded
Hyetograph
Calculation of
Excess Rainfall
for Each Cell
Input
Hydrograph
Time Step
∆T=?
Calculation of Tc For
Each Cell
Producing Table of
Time & Pe For each cell
& for each Accu Rainfall
Classification of
excess Rainfall For
each ∆T
Calculation Discharge in
Each Time Step Q=Pe/ ∆T
Hydrograph
Calculation
No
Input Clark Coe. K=? &
Hydrograph Base
Time=?
Peak and Shape of Calculated
Hydrograph is Near to
Recorded Hydrograph
Yes
End of Program
Figure 4. Flowchart of methods in the ModClark method
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Vol. 3, No. 12, December, 2017
After calibration and validation of the model, design storm of the Golestan National Park station plan was first
extracted to determine the flood-prone areas. Based on the intensity-duration-frequency curve (IDF) of the Golestan
National Park station, the rainfall intensity for the return period of 50 to 100 years was estimated in terms of the time
duration of concentration. The spatial distribution of design rainfall was done from the correlation between the height
and depth of the annual rainfall. Then, in order to determine the flooding, each slope map, CN, flow length, and the
average annual precipitation of the watershed were divided into four classes. After integrating the maps, the
homogeneous units were coded in the GIS environment. In order to determine the effect of each homogeneous unit on
the flood discharge, and to prioritize them in terms of flooding based on the unit flood response method, first, the output
flood hydrograph for the rainfall was calculated at the return periods of 50 and 100 years with the participation of all
watersheds. Then, the simulation was repeated with the successive deletion of each homogeneous unit and their effect
on the water outlet of the entire watershed was determined at the simulation stage and the participation rate of each cell
and sub-basin on the output peak discharge. In order to evaluate the effect of each cell on the output peak discharge, the
flooding index (�� ) presented in Equation 3, was used [12].
�� =
∆ �
��
(3)
In which �� is the flooding index of the sub-basin, im (m3.s-1.km-2), ∆ � is the change of the output peak flow of the
watershed zone determined by removing the sub-basin im (m2.s-1 ) and Ai is the area of the sub-basin im (km2).
Finally, to determine the distribution of fi with rainfall height, slope percentage, curve number and distance of each
unit from the output, each of the values of the following equations were standardized after extracting the values for each
unit from the GIS software.
��� =
� =
� =
� =
−
�� −
−
�� −
� −�
� �� − �
−
�� −
(4)
�
(5)
�
(6)
�
(7)
�
In these relationships, ��� is the standardized value of CN (between -1 and +1); CN, is the curve number of each unit;
, is the average curve number of all units;
�� and
� , respectively are the maximum and minimum number
of curves in units; � , is the standard value of rainfall; R, is the rainfall of each unit; , is the average rainfall of all
units;
�� and
� are respectively the maximum and minimum rainfall of units; FD, is the distance from the
output; � , is the average distance from the basin outlet; � �� and � � are respectively the highest and the lowest
distance of units from the basin outlet; S is the slope of each unit; , is the mean slope of all units; and �� and �
arethe maximum and minimum slope among units, respectively.
3. Results
Table 2 shows the parameters calibrated by the ModClark method. Thus, the ratio of initial losses decreased from 0.2
in the calibration phase to 0.15 and the coefficient of curve number increased by 10%. This indicates that the initial
losses in the basin are low, but the calculated CN values are less than the values in reality. The time of concentration
and the reserve coefficient parameters did not change much compared to the initial values. The results of the simulated
hydrographs using the ModClark model in selected events and the results of the model evaluation in estimating the flood
hydrograph characteristics are given in Figure 5 and Table 3, respectively. This model simulates the maximum discharge
very well, and it was only just less than accurate on May 25, 2003 with an efficiency coefficient of 0.81. The model
verification was done with the evaluation of two dates which shows that the modeling had good accuracy.
Table 2. Hydrological parameters of watershed before and after calibration
Cal ibr at io n p ar a me te r s
Cal ibr ate d v al ue
Init i al v al ue
Cu rv e Nu mb er C o e ffi ci e nt
1 .1
1
T im e o f C o n c en tra t io n ( hou r )
2 5 .9 3
2 5 .9 3
Ini tia l l o sse s Ra t i o
0 .1 5
0 .2
Re se rv e C o e ffic i e nt (h ou r)
2 3 .2
2 5 .9 3
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Vol. 3, No. 12, December, 2017
2.5
2
6
Q (cms)
4
8
20
40
60
80
100
2
0
Rain
0.5
observed
1
simulated
1.5
1.5
2
1
2.5
0.5
10
0
2006.04.08
3
0
Rain
Observed
Simulated
Rain (mm)
Q (cms)
1999.04.08
16
14
12
10
8
6
4
2
0
3
0
120
Rain (mm)
Civil Engineering Journal
3.5
0
30
Time (hr)
60
90
120
150
Time (hr)
Figure 5. A sample observation hydrograph and simulation using the ModClark model in the validation step
In the validation step, the accuracy of the model was obtained with the Root Mean Square Error (RMSE), Efficiency
Coefficient (EC) and coefficient of determination (R2) which were equal to 1.55, 0.8 and 0.84, respectively. The accuracy
criteria of the model represent the model's ability to simulate the hydrograph and are consistent with the results presented
by Paudel et al. (2009), Saghafian et al. (2010), Ghavidelfar et al. (2011), Rajabi and Shabanlou (2012) [27], and Jiang
(2015).
Table 3. ModClark Model Assessment Results for Estimating Flood Hydrograph Specifications
Relative error (percent) of
hydrograph factors
Time
Peak
Base
to
discharge
time
peak
0.03
38.89
38.46
0.05
14.71
17.02
0.02
21.21
25.53
0.23
38.1
30.95
0.09
4.34
11.76
0.08
23.45
24.74
6.33
20
10.34
4.06
7.5
30.95
5.2
13.75
20.65
Accuracy criteria of peak discharge
Determination
Coefficient (R2)
0.46
0.46
0.79
0.56
0.55
0.56
0.74
0.93
0.84
Efficiency
Coefficients
(CE)
0.81
0.41
0.8
0.45
0.1
0.51
0.73
0.87
0.8
Root Mean
Square Error
)RMSE(
11.72
0.058
9.96
2.54
2.39
5.33
2.81
0.28
1.55
Date of event
stage
2003.05.25
2003.11.03
Calibration
2004.05.05
2004.09.18
2005.10.08
Average
1999.04.08
Validation
2006.04.08
Average
Figures 6 and 7 show a map of the potential runoff production categories pertaining to the return periods of 50 and
100 years, respectively. According to the runoff potential mapping in both return periods, the homogeneous unit of 116
with a fi (m3/s.km2) was 0.43 (at the return period of 50 years) and 0.54 (m3/s.km2) (at the return period of 100 years)
was identified as the most effective unit in runoff production in this basin. According to Table 4, in the 50-year return
period, very low class flooding intensity area (51.81%), and in the 100-year return period, very low class flooding
intensity area with the area of 47.24% have the highest area.
Figure 6. Map of the potential runoff production classes with the 50-year return period of Tangrah watershed
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Figure 7. Map of the potential runoff production classes with the 100-year return period of Tangrah watershed
Table 4. Percentage of different areas of potential runoff production in Tangrah watershed
Return period of 100 years
Flooding Intensity
Area (%)
Class
3.34
Very high
3.84
High
15.48
Moderate
47.24
Low
30.1
Very low
Return period of 50 years
Flooding Intensity
Area (%)
Class
0.93
Very high
2.41
High
6.14
Moderate
38.71
Low
51.81
Very low
4. Results for Sub-Basin Flooding Prioritization
The priority mapping of the sub-basins runoff potential based on the fi at the return period of 100 year is shown in
Figure 8. The results of the prioritization of sub-basin flooding showed that the sub-basin of Ghizghaleh, with fi 0.2
(m3/s.km2) was in the class of very high in the map of runoff production and Cheshmehkhan and Dasht-e-Sheikh subbasins with fi 0.04 (m3/s.km2) were on the class of very low potential for runoff production. According to Table 5, the
results of peak flow values and the fi of each sub-basin showed that greater area and less distance from basin outlet did
not necessarily have more effect on the outflow, but factors such as land use and hydrologic group of sub-basin soils
affect peak output discharge and flooding which is consistent with the results of the research by Saghafian et al. (2006).
Figure 8. Flooding map of the sub-basins of Tangrah watershed with a Return Period of 100 years
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Table 5. Peak flow and 100-year flooding classes of each sub-basin
Class
Flooding index of
sub basin (m3/s.km2)
moderate
Very low
Very high
low
Very low
high
0.088
0.045
0.219
0.062
0.045
0.15
Average of flooding
index homogeneous
units
0.09
0.04
0.15
0.08
0.04
0.14
Peak discharge
(m3/s)
Area)%(
184.44
179.31
163.37
144.01
167.69
129.32
0.66
7.82
6.71
40.43
24.36
20.01
Sub basin
Dasht
Dasht-e-Sheikh
Ghizghaleh
Nardin
Cheshmehkhan
Tangrah
-0.70
0.40
0.4
0.35
0.35
f (m3/s.km2)
f (m3/s.km2)
The results of sub-basins fi showed that the intensity of sub-basin flooding was not influenced by only one factor, and
the combined effect of parameters especially CN, rainfall ratio of each sub-region, time of concentration, and distance
to output were more effective. Figures 9 and 10 show the distribution of fi with the curve number, rainfall heights,
distance from the outlet, and the slope percentage of all units in the basin.
0.30
0.25
-0.50
-0.30
0.3
0.25
0.20
0.2
0.15
0.15
0.10
0.1
0.05
0.05
0.00
-0.10
0.10
0.30
0.50
0.70
-0.7
-0.5
-0.3
Z- Rain
0
-0.1
0.1
0.3
0.5
0.7
Z-CN
-0.70
0.40
0.40
0.35
0.35
f (m3/s.km2)
f (m3/s.km2)
Figure 9. fi against rainfall height and standardized curve numbers
0.30
0.25
-0.50
-0.30
0.30
0.25
0.20
0.20
0.15
0.15
0.10
0.10
0.05
0.05
0.00
-0.10
0.10
0.30
0.50
-0.70
0.70
-0.50
-0.30
0.00
-0.10
0.10
0.30
0.50
0.70
Z- Distance from outlet
Z- Slope
Figure 10. fi versus slope percentage and distance to standardized output
In this research, the distribution of the fi with the curve number, distance to output, and slope percentage and rainfall
height of all cellular units found in the basin has been obtained (Figures 9 and 10), in which Figure 9 shows that with
increasing rainfall, the index of flooding increases. Moreover, the fi increases with an increase in the curve number, but
the distance to the outlet and the slope percentage did not follow a specific distribution (Figure 10). The results of the
distribution of the flood index indicated that only one parameter of each homogeneous unit could not determine the fi of
that unit, which is consistent with the results of Saghafian et al. (2010). The curve number and rainfall were directly
related to the flood index, but the percentage of slope and distance to the outlet did not show a specific relationship with
the fi, therefore, it can be concluded that it is more important to determine the index of flooding, rainfall, and CN.
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5. Discussion
One of the proposed methods for identifying areas with a potential for runoff production is the unit flood response
method. In this method, the processing of runoff from the design rainfall, and the determination of the effect of each of
these units on the output flood from the total basin are done by successive removal of units within the basin. According
to this method, it is possible to prioritize areas with a potential for runoff production.
The results revealed that by using the distributional characteristics of the ModClark model, we can study the potential
of runoff production in cellular units of any small size requiring accurate inputs to the model. From the results of this
study, it can be concluded that although a sub-basin may have low flooding in the scale of prioritization of the sub-basin,
smaller units within it may have high flooding, indicating the importance of determining the flooding as a distribution
in a flood-control project.
According to the runoff production potential in the return period of 50 and 100 years, it was found that the runoff
production potential increased from the upstream to the downstream of the basin, and the homogeneous unit of 116 with
a fi 0.54 (m3/s.km2) was identified as the most efficient cell in the flooding. It must be stated that in this basin that due to
a higher rainfall, the steep slope, and a high CN, the flooding close to the outlet of the basin was higher than that of the
upstream areas, a finding different from investigations by Saghafian et al. (2010) and Rezaei et al. (2016).
In Table 5, the fi of the sub-basins and in Figure 8, the potential runoff production map in the sub-basins indicate that
the Ghizghaleh sub-basin with fi 0.21 (m3/s.km2) is the most flood-prone zone of the sub-basin and in studying the
topographic specification of the area, Ghizghaleh was found to be the steepest sub-basin. Its fi indicated that it was
located in a high-risk class and it was clear from this table that the largest and nearest sub-basin or the farthest and
smallest sub-basins did not necessarily have the most and the least effect on the maximum flood discharge in the basin
output. Factors such as land use and the hydrological group of the sub-basin soil could have a greater effect on the peak
flow and the runoff production potential. The flooding intensity of sub-basins is not influenced by one agent alone, and
the combined effect of the parameters, in particular the CN, the rainfall ratio of each sub-basin, time of concentration
and the distance to the outlet have more effective roles.
The results of this research show that the participation rate of sub-basin in output discharge of the entire basin is not
affected by area and peak flow of the sub-basin, but factors such as the spatial location of the sub-basins, distance to
output, CN coefficient, and the role of the route of the main river have a significant impact on the flooding of the subbasins. For example, the Nardin sub-basin which is ranked first in terms of area and account for about 40% of the total
area is in the fourth place in terms of participation in the flood output of the entire basin. The results of the distribution
of fi (Figuers 9 and 10) also indicated that the distance from the outlet and the slope percentage had no correlation with
fi, and it can be concluded that only the distance from the output and the percentage of the slope of each cell cannot
determine the fi of that cell, which is consistent with the results of Saghafian et al. (2010). The results of this research
show that by integrating the Geographic Information System (GIS) and hydrological models, we can study the
interactive effect of physiographic and climatic factors on the flooding potential of the watershed and with regard to a
simultaneous peak flow and role of flood routing in rivers, we can prioritize sub-basins in the most desired way, a
characteristic consistent with the results of Khosroshahi and Saghafian (2005).
6. Conclusion
Considering the high accuracy of ModClark’s distribution model, by using this hydrologic model, we can study the
interaction of physiographic and climatic factors on the potential of watersheds' runoff production. Also, the results
showed that because the use of the ModClark’s distributive model requires highly precise inputs, therefore; it is possible
to use this method in very important tasks such as determining areas with high runoff potential in very small units. The
results of this research can be used in flood-control planning for small structures and reinforcing them, land-use and
vegetation management, conservation and control programs, and rainwater harvesting projects. The results of this study
can also be used to determine the location of the installation of flood measurement and flood warning devices in flood
prone sub-basins. Since prioritization in terms of sub-basin and cellular model has the same results, it is suggested that
prioritization can be done in the case of a sub-basin unit if it is not necessary for smaller units than the sub-basin. Since
the SCS-CN method is sensitive to the depth of precipitation, it is suggested that other methods of estimation of
infiltration should also be used and their results should be investigated. It is recommended to use other distributive
models for rainfall-runoff modeling and compare with the present study and the effectiveness of the Modclark model in
more watersheds of the country should be investigated in order to determine its applicability according to different
conditions in watersheds with different climatic and geomorphologic conditions.
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