Floodplain Mapping: HEC-RAS & ArcGIS Case Study

Arab J Sci Eng (2016) 41:1375–1390
DOI 10.1007/s13369-015-1915-3
RESEARCH ARTICLE - CIVIL ENGINEERING
Floodplain Mapping Using HEC-RAS and ArcGIS: A Case Study
of Kabul River
Muhammad Shahzad Khattak1 · Faizan Anwar1 · Tariq Usman Saeed2 ·
Mohammed Sharif3 · Khurram Sheraz1 · Anwaar Ahmed4
Received: 31 May 2014 / Accepted: 13 October 2015 / Published online: 6 November 2015
© King Fahd University of Petroleum & Minerals 2015
Abstract This paper describes the application of HECRAS model to the development of floodplain maps for the
part of Kabul river that lies in Pakistan. The intent is to
assist policy makers and planners in the development of flood
mitigation measures for the Khyber Pakhtunkhwa Province,
which experienced unprecedented floods in July/August
2010 exposing the vulnerability of the province to this natural
catastrophe. Owing to its reasonable accuracy and free availability, shuttle radar topography mission digital elevation
model was chosen for the extraction of geometrical data for
the river. Conventional flood frequency analysis, involving
log-normal, Gumbel’s, and log-Pearson type III (LP3) distributions, was used to calculate extreme flows with different
return periods. Using Kolmogorov–Smirnov (KS) test, LP3
was found to be the best distribution for the Kabul River. The
peak floods from frequency analysis were input into HECRAS model to find the corresponding flood levels expected
along river reaches extending through Warsak dam to Attock.
Results obtained with HEC-RAS model were used in combination with ArcGIS to prepare floodplain maps for different
return periods. Through floodplain maps, areas that are vulnerable to flooding hazards have been identified. Analysis
B
Tariq Usman Saeed
[email protected]
Muhammad Shahzad Khattak
[email protected]
1
Department of Agricultural Engineering, University of
Engineering and Technology, Peshawar, Pakistan
2
Lyles School of Civil Engineering, Purdue University,
West Lafayette, IN, 47907, USA
3
Department of Civil Engineering, Jazan University, Jazan,
Kingdom of Saudi Arabia
4
School of Civil and Environmental Engineering, National
University of Sciences and Technology, Islamabad, Pakistan
of floodplain maps indicated that more than 400 % area is
likely to be inundated as compared to the normal flow of the
river. Most of the area found to be vulnerable to flooding
is currently used for agriculture. Comparison of simulation
of 2010 flood with the image of the flood taken by MODIS
clearly shows a close agreement between the two.
Keywords
Kabul
Flood · HEC-RAS · Frequency · Modeling ·
1 Introduction
Floods are caused by extreme hydrometeorological actions
while their evolution depends on geomorphologic agents,
such as permeability and soil stability, vegetation cover,
and the geometric characteristics of the river basins. Urban
expansion and consolidation, changing demographic features
within floodplains, changes in flood regime as a result of
climate change, and human intervention in the ecological
system are the major factors that lead to increased exposure
of communities to flood risk [1]. The occurrence of extreme
precipitation is a major impact of climate change; this leads
to increase in the magnitude and frequency of extreme events
such as droughts and floods [2]. Increase in the total amount,
frequency and intensity of precipitation will affect the timing
and magnitude of runoff, but its decrease will cause droughtlike situations [3]. It is expected that future climatic shifts
would cause a great variation in the water accessibility in
different regions. As a result, almost every facet of human
life including agricultural productivity, wildlife and fish management, energy use, industrial and municipal water supply,
and flood control would be affected [4]. As a consequence of
climate change, the vulnerability of communities to floods
has increased in most parts of the world, including Southeast
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Asia. Khan [5] found an increase in the frequency and magnitude of the occurrence of disastrous floods in South Asia
in the past 2–3 decades.
Estimation of flood hazard can be carried out using methods of varying complexity depending on data, resources, and
time availability [6]. The first step in the flood hazard estimation is to compute discharge for return periods of interest
at any given location. To accomplish this step, frequency
analysis of annual maximum discharge data may be used,
although several other methods are available to estimate flood
peaks corresponding to a given return period. Flood frequency analyses involve computing statistical information
such as mean values, standard deviations, and skewness of
the given annual maximum discharges. Frequency distribution is generated from this statistical data; this distribution
gives us the likelihood of various discharges as a function
of recurrence interval. Flood frequency distributions are presented in many different ways according to the equations used
to conduct the statistical analyses. The Gumbel’s or extreme
value type 1 (EV1) distribution, the log-normal (LN) distribution, and the log-Pearson type III distribution (LP3) are
standard flood frequency distributions used by US Federal
agencies such as Federal Emergency Management Agency
(FEMA) and US Geological Survey (USGS) to calculate
flood recurrences. Each distribution can be used to estimate
design floods with their own advantages and disadvantages.
According to the US Water Advisory Committee on Water
Data [7], the recommended distribution for flood frequency
analysis is the log-Pearson type III distribution. The second
step involves the “translation” of discharges into water levels
using either the rating curves or the hydrodynamic models.
Rating curves are based on the relationship between the historical discharges and the corresponding water levels at any
given location. These curves are developed using the flows
observed over the range of water levels at a given location.
Frequently this range does not cover the entire range of flows
that can occur. Where flows occur outside of the gauged
range, levels are estimated using a direct extrapolation of the
rating equation. Also, hydrodynamic models can be used to
convert discharges to water levels at any given location if the
cross-sectional details are supplied as an input to the model.
The final step is to determine the extent of areas likely to be
inundated for discharges corresponding to different return
periods. This step can be accomplished using an appropriate
hydraulic model.
Numerous flood estimation studies have been carried out
in several parts of the world using different techniques. Ouma
and Tateishi [8] describe an integrated model based on geographic information system (GIS) and analytical hierarchy
process (AHP) for the assessment of urban flood vulnerability
for the Eldoret Municipality in Kenya. Deng [9] used weather
research and forecasting (WRF) model to examine the effect
of model resolution and parameterization on the simulation
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of flood-causing events that happened in Jeddah city. The
authors found that the occurrence of extreme events is caused
by mesoscale convective systems linked with strong moisture
convergence. During the last few years, several flood estimation studies have been carried out in the Kingdom of Saudia
Arabia (for example, [10–13]). Dawood [11] presented a
comparison of curve number and rational methods, in addition to four regression models for the Makkah metropolitan
area in Saudi Arabia. Based on the results of their study,
these authors recommended the curve number method as an
optimum method for flood estimation in Makkah.
One of the most widely used models used by hydraulic
engineers in the channel flow analysis and floodplain delineation is HEC-RAS [14]. It is a one-dimensional steady flow
hydraulic model developed by the US Army Corps of Engineers; it has wide acceptance among government agencies.
With HEC-RAS, it is possible to simulate water surface profiles for gradually varied steady flow and the effects of various
obstructions such as culverts, bridges, structures in the overbank region and weirs. Several applications of HEC-RAS
model to determine the extent of inundation resulting from
a given flood have been reported [15–17]. Abdelbasset [18]
applied HEC-RAS model to calculate water surface profiles
corresponding to selected flood events downstream of Al
Wahda Dam in Sebou basin in Northern Morocco. However, numerical instability issues during unsteady analyses,
mainly in steep and/or highly dynamic streams and rivers,
may be found. Various tools for the analysis and visualization of temporal and spatial data come from Geoinformatics
[19]. Software often used in combination with HEC-RAS
is ArcGIS. It comprises a set of tools, utilities, and procedures for working with geospatial data. The basic inputs to
HEC-RAS include the river discharge, channel, floodplain
geometry, and channel resistance.
In Pakistan, floods are considered to be a major natural
calamity. The Indus River and its tributaries have been a
source of a long history of flooding in the country (Fig. 1).
The floods of 1928, 1929, 1955, 1957, 1959, 1973, 1976,
1988, 1992, 1995, 1996, and 1997 verify their devastating
nature and their adverse impact on infrastructure in Pakistan
[20].
Pakistan experienced very heavy rainfall in mid-July 2010
that remained until September 2010. This resulted in the
unprecedented floods affecting the entire length of the country. The 2010 floods were the worst in the history of Pakistan
[21,22]. A total of 1985 fatalities and 2946 injuries were
reported; around 20,251,550 people, 132,000 km2 area, and
1,894,530 houses were affected; 78 out of 141 districts
and 2.1 million hectares of cultivated land were damaged; a large number of livestock was perished; 515 health
and 10,436 educational facilities were either destroyed or
severely damaged. The overall recovery and reconstruction
cost as estimated by the World Bank and Asian Develop-
Arab J Sci Eng (2016) 41:1375–1390
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Fig. 1 Location map of the Indus Basin (Kabul River located at the north-west)
ment Bank is of the order of US $ 8.74 to US$ 10.85 billion
[23]. Several studies have been carried out in Pakistan to
assess the impact of floods. Khan [24] estimated a maximum instantaneous flow of 8205 m3 s−1 having a return
period of 1000 years for Kabul River at Nowshera bridge
gauging station using Gumbel’s distribution. Hussain [25]
conducted a study to determine the extent of flooded area in
the Peshawar Valley (Peshawar, Charsadda, and Nowshera
districts) of the KP province of Pakistan using Landsat-7
satellite imagery. Kwak [26] estimated the number of people affected and the damage caused by the 2010 flooding
using Advanced Land Observing Satellite (ALOS) images
and predicted the future risks for the Kabul River. Sayama
[27] applied the rainfall–runoff–inundation (RRI) model to
simulate 2010 rainfall–runoff–inundation in the Kabul River
basin. Ushiyama [28] applied Lagged ensemble rainfall–
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runoff–inundation (RRI) forecasting to the devastating flood
of 2010 in the Kabul River basin. Downscaled forecasts made
by Ushiyama [28] predicted rainfall amounts with lead times
of 1 day and 3-day reasonably well.
Given the fury of the floods that Pakistan faces almost
each year, there is an urgent need to develop flood mitigation measures in the region. Non-structural flood mitigation
steps such as flood forecasting, early warning systems, and
awareness programs among the communities likely can be
very effective in reducing direct and indirect damages caused
by floods [29]. Flood hazard assessment using hydrological models and geographic information system (GIS) can be
a valuable tool for the study of historical events. Furthermore, digital flood risk maps for important rivers in Pakistan
are not available for any historical events. Therefore, the
major focus of the present paper is on the application of
HEC-RAS, in combination with ArcGIS, to the development of floodplain maps for the part of Kabul river that
lies in Pakistan. The aim is to reduce the exposure of the
Khyber Pakhtunwa Province, which experienced unprecedented floods in July/August 2010, to future floods. The
implementation of the flood management program based on
floodplain maps shall contribute toward a significant reduction in damages to the inhabitants of Peshawar, Charsadda,
and Nowshera districts of the Khyber Pakhtunwa Province.
The results of the research presented herein have the potential to assist planners and policy makers to develop effective
flood management strategies in the region.
2 Description of Study Area
The Rivers Indus and Kabul are the two major tributaries
passing through the KP province of Pakistan (Fig. 2). The
Indus River is one of the world’s longest rivers, having a
length of 2900 km and a catchment area of approximately
1.16 million km2 [30]. The Kabul River originates at the
base of the Unai Pass in Afghanistan and flows eastward
toward Kabul city while enters Pakistan through the hills of
Mohmand Agency where its flow is first gauged at Warsak
dam. The River has a length of 700 km and a catchment
area up to Warsak dam of 8068 km2 . The Kabul River passes
through Nowshera and then meets the Indus River at Attock.
The river is joined by the Swat River near Charsadda, which
drains the Swat and Dir districts—the latter through its tributary Panjkora River
The Kabul River basin has complex geologic conditions.
Sedimentary limestone and shale are predominant in most of
the lower part, whereas the headwaters of the main tributaries
rise among very complicated sets of igneous and metamorphic rocks. The drainage area comprises furrowed mountains
varying in heights from 300 m above sea level (masl) to more
than 6000 m asl with a steady general rise from South to North
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[31]. Hydrometeorology in the basin is defined by highly
complex biophysical environments produced by interaction
between geology, terrain, and meteorology. The winter precipitation in the upper basin is mostly observed as snow. The
rainfall is mostly concentrated in the middle and lower part
of the basin. Several complex mechanisms are responsible
for flow generation in Kabul River. It is comprised of contributions from rain, snow, and glaciers; the contribution of
each component keeps on varying with the time of the year.
During July to September, the river flow is augmented by
monsoonal rainfall producing relatively higher discharges in
the river. Most often the higher discharges are seen in the
month of July and August, and these are mainly due to heavy
rain in the middle and lower part of the basin. Glacier-melt
contribution is the highest during the months of July and
August. Glacier-melt contribution from the upper part of the
basin combined with rainfall in the lower part is the most
likely cause of flooding in the region. Only the part of Kabul
River that lies in the KP province of Pakistan is considered in
this study. Of the three branches of the Kabul River, namely
Naguman, Shah Alam, and Haji Zai, only the latter branch
is used for floodplain mapping. These branches are shown in
Fig. 2.
3 Data Used
Digital elevation models (DEM) for the entire globe, covering
all of the countries of the world, are available from CSI (Consortium for spatial information) Web site. These data were
derived using shuttle radar topography mission (SRTM)—an
international effort that generated the most complete highresolution digital topographic database of Earth [32]. SRTM
consisted of a specially modified radar system that stayed flying on board the Space Shuttle Endeavor during an 11-day
mission in February of 2000. Several studies (for example,
[33–36]) have employed SRTM data. Owing to its reasonable accuracy and free-of-cost availability, a 90 m × 90 m
DEM, which is available from shuttle radar topography mission (Fig. 3), has been used to delineate the boundary of the
watershed, to define sub-catchment boundaries and a stream
network and to analyze the drainage patterns of the terrain.
Using the SRTM-DEM, the river and floodplain geometry
was obtained. Colored images from Google Earth were used
to classify the land uses, and then theses classes are used for
the estimation of Manning’s n values, which were needed by
HEC-RAS for performing hydraulic computations.
The hydrometeorological data used in this study were
obtained from the Water and Power Development Authority
(WAPDA), Pakistan. Discharge data for an adequate number
of years are available at two gauging stations: Warsak dam
and Nowshera. The maximum instantaneous discharge data
at Warsak dam were available from 1965 to 1970, and from
Arab J Sci Eng (2016) 41:1375–1390
1379
Fig. 2 Kabul River and its
branches in Khyber
Pakhtunkhwa province, Pakistan
2005 to 2010. At Warsak dam, the daily mean discharge was
available, without any gaps, for the period 1965–2010. Since
the maximum instantaneous discharge data at Warsak dam
were not available for the entire period of record, the missing values were generated by developing a linear regression
model. Twelve pairs of maximum daily and maximum instantaneous discharge data at Warsak dam were used to develop
the linear regression model. The derived linear relationship
between the maximum instantaneous and maximum daily
discharge is given by Eq. (1) and is shown graphically in
Fig. 4. The correlation coefficient (r) between the maximum
instantaneous and daily maximum flow was found to be 0.94.
Q p = 1.238 Q M − 305.47
(1)
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Arab J Sci Eng (2016) 41:1375–1390
5000
-1
where
Instantaneous Disharge (m .s )
Fig. 3 SRTM-DEM of the Kabul River catchment
3
Qp = 1.238QM - 305.48
3 −1
Q p = maximum instantaneous discharge (m s
3 −1
Q M = maximum daily discharge (m s
)
)
At Nowshera, the maximum instantaneous discharge data as
well as the mean daily discharge are available for the period
1962–2010 (49 years). The values of the generated maximum
instantaneous discharges at Warsak dam are shown in Table 1.
The entire reach of the river between Warsak dam and Attock
was divided into the upper and the lower reaches. The start
and end nodes for the upper reach were Warsak dam and the
junction of Kabul and Swat Rivers at Charsadda, respectively.
The start node of the lower reach corresponds to the end
node of the upper reach, whereas the end node of the lower
reach was assumed to be at Attock. Assuming steady state
conditions, the flow data at Warsak dam were used for the
entire upper reach. The discharge at Nowshera was assumed
to flow under steady state conditions over the entire lower
reach.
4 Methodology
The objective of the present study was to develop floodplain maps for the segment of the Kabul River that lies in
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4000
2
R = 0.8817
3000
2000
1000
0
0
500
1000
1500
2000
2500
3000
3
3500
4000
4500
5000
-1
Daily Discharge (m .s )
Fig. 4 Relationship between maximum daily and maximum instantaneous discharge for the Kabul River at Warsak dam
Pakistan. The methodology employed (Fig. 5) to develop
floodplain maps comprised of the following steps: (1) flood
frequency analysis of the available observed discharge data to
obtain floods corresponding to different return periods; (2)
preparation of DEM based on SRTM data; (3) delineation
of watershed and drainage network using HEC-GeoRAS;
(4) preparation of geometric data using HEC-GeoHMS; (5)
application of HEC-RAS to a number of potential flow scenarios corresponding to different return-period floods; (6)
preparation of floodplain maps Using GIS.
To estimate water surface profiles and extent of inundation
under different flood intensities, peak discharges for different
return periods are required. Flood frequency analysis [37]
was carried out to obtain flood peaks for different return
Arab J Sci Eng (2016) 41:1375–1390
Table 1 Maximum
instantaneous discharge (Q M )
and daily maximum (Q P ) values
for Warsak dam and Nowshera
bridge gauging stations (m3 s−1 )
1381
S.No.
Year
1
1962
2
1963
3
Nowshera (Q M )
Warsak (Q M )
Warsak (Q P )
3257
NA
NA
4079
NA
NA
1964
3852
NA
NA
4
1965
6316
4276
2918
5
1966
4588
3285
3121
6
1967
5042
3681
2981
7
1968
4050
3030
2682
8
1969
3031
2271
2170
9
1970
1983
1563
1563
10
1971
2325
1618
1554
11
1972
3937
2911
2598
12
1973
3625
2780
2492
13
1974
2543
1600
1539
14
1975
4843
2537
2296
15
1976
3172
2114
1954
16
1977
2974
3279
2895
17
1978
4758
4642
3996
18
1979
3016
2497
2264
19
1980
2974
2048
1901
20
1981
3342
2402
2187
21
1982
2218
1337
1327
22
1983
2917
2250
2064
23
1984
3229
2661
2396
24
1985
2861
1500
1458
25
1986
3116
2224
2043
26
1987
2713
1976
1843
27
1988
3739
1854
1744
28
1989
3016
1579
1522
29
1990
3368
2553
2309
30
1991
4276
3239
2863
31
1992
3856
2870
2565
32
1993
3496
1838
1731
33
1994
3519
2500
2266
34
1995
4293
2162
2162
35
1996
3615
2435
2435
36
1997
3396
2126
2126
37
1998
3658
2256
2256
38
1999
2149
1432
1432
39
2000
1427
977
977
40
2001
2378
1412
1412
41
2002
2787
1692
1692
42
2003
2677
2008
2008
43
2004
2041
1634
1634
44
2005
4775
3190
3114
45
2006
4451
1398
1364
46
2007
4083
3171
3044
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Table 1 continued
S.No.
Year
Nowshera (Q M )
Warsak (Q M )
Warsak (Q P )
47
2008
3483
1689
1635
48
2009
3141
2062
1960
49
2010
12707
4324
3825
Q M at Warsak from S.No. 10 to 43 is calculated from the developed regression equation, while the
remaining is observed value. The text NA stands for not available data
Fig. 5 Methodology flowchart
for HEC-RAS modeling
periods at Warsak dam and Nowshera bridge. Three commonly used frequency distribution functions for estimation
of extreme floods, namely log-Pearson type III distribution
[38], Gumbel’s or extreme value distribution [39,40], and
log-normal distribution [41,42], were used for frequency
analysis. The detailed description of each of the methods
is given in Chow [37]. To determine the best fit distribution for the estimation of flood peaks, Kolmogorov–Smirnov
(KS) test having a confidence interval of 95 % was used. The
procedure of KS test is given in McCuen [43]. The maximum instantaneous observed discharge data of Nowshera
and observed and generated data at Warsak dam were used
for flood frequency analysis (Table 1). The flood peaks for
different return periods were obtained using LP3 and used as
an input to HEC-RAS model.
HEC-Geo HMS was used to extract basin characteristics,
including the river centerline, from DEM. The flow and geometric data such as the bank stations, cross sections, and flow
pathlines were prepared using HEC-GeoRAS, and later used
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as an input to HEC-RAS. The files required in preparing the
geometric data were DEM and georeferenced natural color
images of the study area. Google Earth was used to acquire
the natural color images, and visual inspection method was
used to estimate the Manning’s n values. Based upon the type
of land cover, the entire area was classified into four groups
as given in Table 2. For each group of land cover, separate
polygons were drawn, and values of Manning’s roughness
coefficient “n” were determined for each polygon using the
method suggested by McCuen [44] and Chow [37]. Phillips
[45] recommended Manning’s n values of 0.15 each for both
single- and double-story dwellings. In this study, n value of
0.15 was used.
After assigning n values to each land class and completing
all geometric data requirements, RAS GIS Import File was
imported into HEC-RAS. This procedure allows the geometric data transfer from ArcGIS into HEC-RAS. The next step
was to supply the steady flow data along with boundary conditions to the steady flow editor. Two stations were selected
Arab J Sci Eng (2016) 41:1375–1390
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Table 2 Land use classes and their estimated Manning’s n values
Table 3 Maximum instantaneous discharge for different return periods
by LN, Gumbel’s, and LP3 distribution (m3 s−1 ) for Warsak
S. No.
Land use
Manning’s ‘n’
1.
Channel
0.0305
2.
Urban
0.15
3.
Hilly
0.045
LN
3538
4605
4.
Cultivated
0.035
Gumbel’s
3482
4534
LP3
3512
4448
4821
for flow inputs; first station was at some distance downstream
of Warsak dam, and the second at the junction of the Kabul
and Swat Rivers. The peak flows are input into HEC-RAS to
find the expected flood levels along river reaches extending
through populated areas of the basin. Flow inputs were supplied for 10-, 50-, 100-, 200-, 500-, and 1000-year flood along
with maximum instantaneous discharge value of 2010 flood
in Kabul River. Upon completion of data inputs, the HECRAS model was executed to perform a detailed analysis of
the flow. A detailed report of the analysis showing the depth
of flow, discharge at each cross section, and other details was
generated by the model. After removing all errors, the results
were exported to ArcGIS in the form of RAS GIS Export File.
The RAS GIS Export File was imported into ArcGIS after
generating water surface and floodplain delineation, and a
raw floodplain map was obtained. This raw map only showed
the extents of flood and some islands/reservoirs, so these
were removed and a smooth water surface was obtained.
The MODIS satellite images of 2010 Kabul River flood captured on July 31, 2010, has been analyzed by the UNITAR
Operational Satellite Applications Programme (UNOSAT).
This image was used to assess the flood extent and damages
over a wide area in Peshwar Valley. A comparison was made
between the flood extent simulated by our model and that
shown by the MODIS satellite image.
5 Results and Discussion
The analysis of the extent of the area inundated under
different return-period floods is driven by the peak flows
obtained from frequency analysis conducted using the maximum instantaneous discharge available at Warsak dam and
Nowshera bridge. The maximum instantaneous discharges
at Warsak dam and Nowshera bridge for different periods
were obtained using LN, Gumbel’s, and LP3 distributions,
and are shown in Tables 3 and 4, respectively. A comparison of these discharge values at Warsak dam and Nowshera
obtained using different distributions is presented in Figs. 6
and 7, respectively. As can be seen from Fig. 6, the predicted
peak flood, using LN distribution, at Warsak dam is higher
than that predicted by the Gumbel’s and LP3 distribution. The
smallest values were produced using the LP3 distribution. At
Station
Warsak
Return period
10
50
100
200
500
1000
5053
5502
6099
6557
4979
5422
6006
6448
5182
5648
5993
Table 4 Maximum instantaneous discharge for different return periods
by LN, Gumbel’s, and LP3 distribution (m3 s−1 ) for Nowshera
Station
Nowshera bridge
Return period
10
50
100
200
500
1000
LN
5223
6773
7423
8073
8937
9597
Gumbel’s
5714
7788
8664
9537
10,689
11,560
LP3
5317
7845
9160
10,645
12,915
14,902
Nowshera bridge (Fig. 7), the highest values were produced
by LP3 distribution and the lowest by the LN distribution.
The maximum instantaneous discharge values at Warsak dam
estimated by LP3 distribution for return periods of 10, 50,
100, 200, 500 and 1000 years were 3512, 4448, 4821, 5182,
5648, and 5993 m3 s−1 , respectively. The corresponding values for Nowshera bridge as estimated by LP3 distribution
were 5317, 7845, 9160, 10,645, 12,915, and 14,902 m3 s−1 .
Khan [24] estimated a maximum instantaneous flow of
8205 m3 s−1 having a return period of 1000-years for Kabul
River at Nowshera bridge station using Gumbel’s distribution. For each of the three distributions used in the frequency
analysis, higher discharge values were produced than those
reported by Khan [24].
Table 5 contains the results of the K–S test for Nowshera
bridge gauging stations and Warsak dam. The K–S test is a
nonparametric test that can serve as a goodness-of-fit test.
The critical value of K–S test statistic ‘D’ for Warsak dam
and the Nowshera bridge station was 0.196 and 0.194, respectively. Since the values of the test statistic for each of the three
distributions are less than the critical value, all the distributions used in this study are considered to be fit for use at both
the gauging stations. Based upon the values of K–S statics,
LP3 distribution was used for the estimation of flood peaks
for both Warsak dam and Nowshera bridge.
Maximum instantaneous flows derived from LP3 distribution for the return periods of 10, 50, 100, 200, 500, and
1000 years and 2010 floods were used as steady flow inputs
in HEC-RAS at two sections. The first section was at some
distance downstream of Warsak dam where the topography was relatively plain compared to the sections upstream,
whereas the second section was at the junction of Kabul
and Swat Rivers. A comparison of water surface profiles at
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Fig. 6 Discharges at Warsak
dam obtained using different
distributions
Fig. 7 Discharges at Nowshera
bridge obtained using different
distributions
Table 5 K–S test results for the
Warsak dam and Nowshera
bridge
Distribution
Warsak dam
Test statistic (D)
Fit
Ranking
Test statistic (D)
Fit
Ranking
LN
0.060
Yes
2
0.124
Yes
3
Gumbel’s
0.068
Yes
3
0.057
Yes
2
LP3
0.052
Yes
1
0.046
Yes
1
Warsak dam under 10-, 100-, and 1000-year return-period
floods and under 2010 flood is shown in Fig. 8. As illustrated by Fig. 8, both the 100-year and 1000-year return
periods produce higher water levels than that produced by
the 2010 flood. The water surface elevations produced at
the junction of Charsadda and Nowshera districts under 10-,
100-, and 1000-year return-period floods and under 2010
floods are shown in Fig. 9. The water surface elevations
produced by 2010 flood are higher than that produced by 100year return-period flood, thus indicating the severity of the
2010 floods. However, the levels were lower than 1000-year
return-period flood. Figure 10 shows water surface elevations
123
Nowshera bridge
near Nowshera bridge under different return-period floods.
Comparison of water surface elevations clearly indicates the
severity of the 2010 flood at the Nowshera bridge as the levels
are found to be higher than the 100-year return-period flood at
the bridge site. The Kabul River runs through steeper slopes
in the upper reaches, thereby leading to smaller depths of
flows. When the river passes through the plains (Charsadda
and Nowshera), the river becomes slower as compared to the
upstream with the higher depth of flow.
Flow profiles for different return floods and for 2010 flood
are shown in Figs. 11 and 12 for the upper and the lower reach,
respectively. As expected, at all the sections in the upper and
Arab J Sci Eng (2016) 41:1375–1390
1385
Fig. 8 Water surface elevations
at some distance downstream of
Warsak dam under different
floods
D/S Warsak Dam
.045
.0305
.035
348
Legend
WS 1000Y
WS 100Y
346
WS 2010_FL
WS 10Y
Elevation (m)
Ground
344
Bank Sta
342
340
338
200
400
600
800
1000
1200
Station (m)
Fig. 9 Water surface elevations
at the junction of Peshawar,
Charsadda and Nowshera
districts under different floods
Districts' Junction
.035
.0305
.035
306
Legend
WS 1000Y
304
WS 2010_FL
WS 100Y
302
WS 10Y
Elevation (m)
300
Ground
Bank Sta
298
296
294
292
290
288
0
2000
4000
6000
8000
10000
12000
14000
Station (m)
Fig. 10 Water surface
elevations near Nowshera bridge
under different floods
Nows hera Bridge
.15
.035
.15
.0305
.15
296
Legend
WS 1000Y
294
WS 2010_FL
WS 100Y
WS 10Y
Elevation (m)
292
Ground
Bank Sta
290
288
286
284
1000
2000
3000
4000
5000
6000
Station (m)
the lower reach, the water surface elevation under 2010 flood
is higher than that under 100-year return-period flood.
5.1 Floodplain Maps
Floodplain maps were produced using elevation, land cover
and geological, physiographic, and basin network data. Water
surface profile data were extracted from HEC-RAS through
HEC-GeoRAS and then were incorporated into a floodplain
map through GIS. Using the water surface data and DEM
created for the basin, the flooded area under different returnperiod floods was delineated. Although floodplain maps were
prepared for various return periods, namely 10, 50, 100, 200,
500, and 1000 years, but only the 100-year floodplain maps
are presented. For presenting floodplain maps, the entire
reach was divided into the upper and the lower reaches, and
separate floodplain maps were presented for each reach. Figures 13 and 14 show the areas that are likely to be inundated
123
1386
Arab J Sci Eng (2016) 41:1375–1390
Fig. 11 Water surface profile
under different floods for the
upper reach (Warsak dam to
junction of Kabul and Swat
River)
350
Legend
345
WS 1000Y
WS 2010_FL
340
WS 100Y
335
WS 10Y
Ground
330
Elevation (m)
325
320
315
310
305
D/S Warsak Dam
300
295
290
285
60
65
70
75
80
85
90
95
Main Channel Distance (km)
Fig. 12 Water surface profile
under different floods for the
lower reach (Kabul and Swat
River Junction to Kabul and
Indus River Junction)
300
Legend
WS 1000Y
WS 2010_FL
295
WS 100Y
WS 10Y
Ground
Elevation (m)
290
285
Kabul and Swat Junction
275
Districts' Junction
Nowshera Bridge
280
270
0
10
20
30
40
50
60
70
Main Channel Dis tance (km )
Table 6 Percentage area
inundated for different return
periods relative to Normal flow
Return period
Normal flow
Percentage w.r.t normal flow (%)
64.24
100
10-Year
252.00
392
50-Year
266.28
415
100-Year
270.09
420
200-Year
271.86
423
500-Year
275.26
428
1000-Year
279.02
434
under 100-year return-period flood in the upper and the lower
reach, respectively. Each map shows contour lines at 20-m
interval as smaller intervals tend to make the maps crowded
and difficult to understand. The details shown by the floodplain maps include area inundated under normal flow, flood
extent for the given return period, district boundaries, names
of different villages, towns and roads in close proximity to
floodplain, and gauging stations. The area inundated under
normal flow refers to the area occupied in between the banks
and is delineated using areal images.
It can be observed from Figs. 11 and 12 that the slope
of Kabul River in the upper reach is about 1 in 600, which
123
Area affected (km2 )
is steep as compared to the slope of the lower reach, which
is about 1 in 4200. Consequently, the inundated area and
depth of flow in the upper reach is relatively less as compared to the lower reach. The maximum affected area lies at
the junction of Peshawar, Charsadda, and Nowshera districts.
It can be seen from Table 6 that more than 400 % area is predicted to be inundated under 100-year return period. Results
of simulation under 100-year return-period flood indicate that
the most severely affected villages in Peshwar district are
Bela momandan, Banda Farid, Bunyadi Kalyay, Agra Payan
and Kudi Kalay (Fig. 13), whereas the areas most severely
affected villages in Charsadda district are Sardaryab, Mohib
Arab J Sci Eng (2016) 41:1375–1390
1387
Fig. 13 100-year floodplain map for the Kabul River in Peshawar, Charsadda, and Nowshera districts
Fig. 14 100-year floodplain map for the Kabul River in Charsadda and Nowshera districts
Banda, Paxton Gari, and Mohala Khera Khel (Fig. 14). The
villages that are most affected in Nowshera districts are Pir
Piai, Mohabat Khel, and Peer Sabaq. It can be seen from
Fig. 15 that most of the areas shown to be affected by the
simulated 100-year return-period floods were also affected by
2010 historical floods. The majority of the inundated areas
as shown in Fig. 15 are currently used for cultivation purposes, while the inundated areas in district Nowshera mostly
comprise of urban areas.
Since flood is a wave process, the satellite images showing inundated areas may relate to images taken during the
flood at different times. A MODIS satellite images of 2010
123
1388
Arab J Sci Eng (2016) 41:1375–1390
Fig. 15 Comparison between simulated and actual (MODIS image taken on July 31, 2010) flood extents
Kabul River flood captured on July 31, 2010, were used in
this study to assess the reliability of the HEC-RAS model in
simulating floods of a given magnitude. Although the time
of taking satellite image may not necessarily coincide with
the time of flood peak or maximum inundated area, but fields
visits and surveys conducted in the basin confirm that the
peak flood occurred around July 31, 2010. Therefore, a comparison of the extent of inundated areas shown by MODIS
image and that simulated by the HEC-RAS model for 2010
flood was made. Figure 15 shows the extent of inundation
shown by MODIS image and by the model. The floodplain
map prepared by using GIS and HEC-RAS for 2010 flood
matched closely to those reported by Hussain [25] and Kwak
[26] using different satellite images. The boundaries of the
inundated areas simulated by our model showed close agreement to the MODIS image of July 31, 2010, but there were
small deviations at some places. These deviations could be
attributed to the manner in which the water surface is generated by ArcGIS. The water surface produced by ArcGIS had
several islands and reservoirs, probably due to the reason that
the elevations in the DEM had an interval of 1 m, whereas
the water surface assumed continuous values of elevations.
Overall, the performance of HEC-RAS model in producing
2010 flood inundation maps is quite promising, considering
that channel resistance was based on very preliminary values and no calibrating adjustments were made. Calibration
of channel roughness could possibly improve the accuracy
of the model.
Review of literature presented in this paper clearly indicated that a large number of different techniques have been
123
employed by researchers for the simulation of flooding
events. However, due to the highly complex nature of meteorological and hydrological processes, it is not possible to
forecast extreme precipitation events that lead to catastrophic
floods. In developing countries such as Pakistan, humans are
particularly vulnerable to flooding because of high population density, absence of adequate flood control measures, and
lack of zoning regulations and emergency preparedness systems. Industrialized countries are equipped with better flood
control measures. However, instances of flood-related casualties have been reported from industrialized countries as
well because it is not possible to provide absolute protection
against floods. Moreover, global climate change has given
an impetus to studies aimed at improving flood forecasting
procedures, and reduction in damages caused by floods.
6 Conclusions
The major focus of this study was on assessing the suitability of HEC-RAS model in simulating water surface profiles
and in determining the extent of inundation under different
return-period floods for Kabul River in Pakistan. It involved
routing the flood by making use of natural channel geometry
in the sub-reaches whererin approximate geometry between
these sites and flood levels is required. Analysis carried out
using Kolmogorov–Smirnov test indicated that LP3 is the
best fit distribution for both Warsak dam and Nowshera
bridge gauging station on River Kabul. Using LP3 distribution, the values of 10-, 50-, 100-, 200-, 500-, and 1000-year
Arab J Sci Eng (2016) 41:1375–1390
return-period floods at Warsak dam were found to be 3512,
4448, 4821, 5182, 5648 and 5993 m3 s−1 , respectively. At
Nowshera, the corresponding values were 5317, 7845, 9160,
10,645, 12,915, and 14,902 m3 s−1 , respectively. Using the
HEC - RAS model, in combination of ArcGIS, an analysis
of the extent of areas likely to be inundated under different return-period floods was carried out. Analysis of results
clearly indicated that the 100-year return-period flood at
Warsak (4821 m3 s−1 ) would inundate 400 % more area than
the normal flow. The extent of inundation with 100-year
return-period flood at Nowshera (9160 m3 s−1 ) would be
around the same magnitude. Floodplain maps showed that
areas prone to flooding are mostly used for cultivation. Urban
areas such as those of Nowshera are also vulnerable to flooding, which was also evident in July/August 2010 floods. The
extent of inundation shown by the satellite image of the 2010
flood was very well reproduced when the 2010 flood was
input into the HEC-RAS model, thereby demonstrating the
capability of the model to simulate open water floods and
produce water levels at the desired locations with reasonable
accuracy.
It is clearly visualized from the floodplain maps that with
one in 100-year return-period flood, the levels of inundation
are roughly four times that due to normal flow. Therefore,
it is extremely important to provide protection to cities such
as Charsadda and Nowshera on both banks of the river primarily through raising of embankments. The reduction in the
magnitude of damages could be achieved through de-silting
at vulnerable reaches on the River. During the field visits, it
was noticed that certain sections of the marginal embankment
need repairing as the villagers have cut these at several locations and constructed passages for their cattle and tractors.
This has resulted in the weakening of embankment structures. Repair and maintenance of existing embankments at
regular intervals should be at the top of the agenda for the governmental agencies entrusted with the responsibility of river
maintenance. Another associated problem with floodplain
management in the basin is its encroachment by the people.
In recent years, there has been increased encroachment of
floodplains because of development and population pressures
in the basin. To ensure protection of people and property from
flooding, the floodplains shall remain preserved in or restored
to an undeveloped natural state. Keeping the floodplain free
from encroachment can reduce flood damages significantly
and provide recreation benefits through the development of
parks and other recreational facilities.
The results of this case study indicate that the simulation
of flood levels for a given floods can easily be performed
using the HEC-RAS —a public domain model. Application
of the model to simulate floods need not require the expensive acquisition of channel geometry data between cities.
Employing the approach presented herein would enable the
government agencies in Pakistan to achieve significant reduc-
1389
tion in flood damages in the Kabul River basin. Though
unsteady flow hydraulic models is known for being difficult
to apply, it is not surprising that water system managers and
planners are questioning about the value of their operational
use. However, many agencies in Pakistan already have familiarity with HEC-RAS models that can be effectively utilized
to improve and simplify the forecasts of areas likely to be
inundated under a given flood. As it is a quasi-hydrodynamic
model, preparing input data and their reliability are critical. The value of resistance parameter, namely Manning’s
“n”, keeps on varying with water level. This value decreases
with increasing water level as the effective relative roughness diminishes and then increases again as the flow spills
overbank, as the channel roughness is lower than floodplain
roughness [46]. Although the HEC-RAS model was found to
perform well in the absence of calibration of Manning’s n, it
is worth investigating the sensitivity of model performance
to this parameter in future works.
Acknowledgments We would like to acknowledge all the guiding
studies that helped us in finishing this research. Any use of trade, product, or firm name is for descriptive purposes only.
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