FLOODPLAIN HAZARD MAPPING AND ASSESSMENT OF RIVER KABUL USING HEC-RAS 2D MODEL

e-ISSN: 2582-5208
International Research Journal of Modernization in Engineering Technology and Science
( Peer-Reviewed, Open Access, Fully Refereed International Journal )
Volume:03/Issue:09/September-2021 Impact Factor- 6.752 www.irjmets.com
www.irjmets.com @International Research Journal of Modernization in Engineering, Technology and Science
[841]
FLOODPLAIN HAZARD MAPPING AND ASSESSMENT OF RIVER KABUL
USING HEC-RAS 2D MODEL
Bahram Khan*1, Dr. Asif Khan*2
*1,2Department Of Civil Engineering, UET Peshawar, Peshawar, Khyber Pakhtunkhwa, Pakistan.
ABSTRACT
Flood is the most devastating environmental hazard throughout the world causing loss of precious human lives
and damage to infrastructure. They occur by unusual overflow of water over the banks of rivers or channels
thus inundating the surrounding area. The magnitude and intensity of floods depends on hydrological and
physical characteristics of the catchment and river channel. Adverse effects of these floods can be alleviated
through mapping of floodplain which is essentially the area around the channel which is likely to be flooded.
One of the methods of floodplain delineation is modeling the river flow using computer models such as the
Hydrologic Engineering Center River Analysis System (HEC-RAS). In this study the application of 2D HEC-RAS
river model is used to develop a floodplain map of river Kabul.
Keywords: HEC-RAS 2D, River Kabul, Hazard Assessment.
I. INTRODUCTION
Pakistan is the 5th most vulnerable country to climate change According to [1] report. 0.2-0.5% of deforestation
rate in Pakistan is one of the fastest in the World [2]. This deforestation rate is alarming for the imminent flood
in the future. The periodic flood caused by this phenomenon needs to mapped and its extent requires to be
measured.
Kabul River is one of the largest tributaries originating in Afghanistan and entering Pakistan via hills of
Mohmand agency as shown in Figure 1. In areas of low land downstream flash floods are observed frequently.
These floods are mostly caused by heavy monsoon rains and snow melt during summer months. It has a
catchment area of 8068 sq. km, length of 700 km and contributes around 10 -12% annual flow to Indus river. At
Attock, River Kabul meets river Indus after passing through the districts of Charsadda, Nowshera and
Peshawar.
Figure 1: Kabul river alignment

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Figure 2: August 2010 floods in kabul river
The Kabul River is often flooded during the summer months and affects people's lives and infrastructure. Figure
3 shows the worst floods occurred in August 2010 which was the worst in 80 years of history. About 1.3 million
people were directly affected in three regions with destruction of vast agricultural land as shown in Error!
Reference source not found. . The social, economic and environmental impacts were also exacerbated by
epidemics from floods.
Figure 3: August 2010 Floods in Nowshera
II. LITERATURE REVIEW
Globally, a flood is the most common and serious disaster accounting for 44% of natural disasters [1]. The
negative effects of these floods can be mitigated through the use of structural and non-structural methods.
Structural method includes the construction of flood walls, dams or reservoirs to control river flow and its
application is restricted due to financial constraints and uncertainty over flood levels. While Non-structural
method include a flood risk map and design of vulnerable areas which is feasible and effective tool to mitigate
flood damage before its occurrence [2]. Flood hazard mapping is an essential non-structural measure which can
help protect human lives and infrastructure [3]. Usually the dry land that meets the rivers, streams etc. which
often overflows during the high flow in a stream is called the floodplain. [4]. Based on available data, floodplain
can be estimated using by variety of methods [5]. The first step in floodplain estimation is to determine
discharge for return periods of interest at the point of interest. To carry out this step, frequency analysis of
annual maximum discharge may be used. Flood frequency analysis incorporates computing statistical
information such as mean, standard deviation, skewness of annual maximum discharges. Based on this
information, a frequency distribution is generated which give us the likelihood of various discharges as a
function of return period. These distributions are then presented in different ways according to the statistical
equations to conduct the statistical analyses. Government agencies such as Federal Emergency Management
Agency (FEMA) and US Geological Survey (USGS) use Gumbel, Log-normal and Log-pearson (III) distribution to
calculate flood recurrence. Each distribution can be used to estimate design floods with their own advantages
and disadvantages. HEC-RAS is one of the most widely used tool for channel flow analysis and floodplain
delineation. It needs more accurate topographic data and is efficient in modeling flows in urban areas [6]–[9].
Moreover HEC-RAS 2D model is recommended in flat, alluvial and braided water courses of floodplains [10].

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III. MODELING AND ANALYSIS
Pre-processing of data for this study is performed using ArcMap and Microsoft Excel. Flow chart in Error!
Reference source not found. below shows methodology steps adopted in this study.
Figure 4: Methodology steps adopted
The topography Kabul river basin is obtained in the form of Digital Elevation Model (DEM) from Shuttle Radar
Topography Mission (SRTM) with 30-meter resolution from website (http://earthexplorer.usgs.gov). Sinks
were filled and the terrain was hydro corrected for bathymetric data from Google Earth. The actual topography
with channel is created using cross section points and depth from Google earth as shown in Error! Reference
source not found. below.
Figure 5: (A) Original terrain from DEM, (B) New Terrain with Channel. (C) Combined Terrain Model

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Peak discharges for return periods of 50 and 100 years is obtained using flood frequency analysis based on
Gumbel probability distribution on annual maximum series of 30 years as shown in the Error! Reference
source not found. below. Corresponding synthetic hydrographs in Error! Reference source not found. had
been prepared by employing observed flood-2010 hydrograph. This hydrograph was used as upstream
boundary condition in floodplain model.
Figure 6: Peak flow versus return period based on Gumbel extreme value theorem
Table 1: Peak flow values from Frequency analysis
Return period (T)
Peak discharge (Q)
(cusecs)
Peak discharge (Q)
(cumecs)
5 149044.72 4219.84
10 176551.76 4998.63
25 211306.99 5982.64
50 237090.41 6712.64
100 262683.44 7437.25
Historical Peak (2010)
(T=54)
239100 6769.54
Figure 7: 50 and 100-year synthetic Flow Hydrographs
0
50000
100000
150000
200000
250000
300000
1 10 100
Discharge
(Cusecs)
Return Period (Years)
Gumbel distribution vs Observed discharge
Observed Gumbel Distribution
0
50000
100000
150000
200000
250000
300000
0 48 96 144 192 240 288 336 384 432 480
Discharge
(cusecs)
Time (hrs)
Historical Flow Hydrographs
100-years
50-years

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Sentinel level IIA imagery with 10-meter resolution was used for Land use and landcover classification with
four classes of landcover. Roughness coefficients to the flow are assigned to two-dimensional mesh in HEC-RAS
Ras mas represented in Error! Reference source not found. and Error! Reference source not found..
Figure 8: Landcover types
Table 2: Coefficients for different land use (Ven.Te. Chow)
Land cover Manning’s coefficient (n)
Water surface 0.001
Developed/urban 0.0648
Vegetation 0.037
Barren land 0.0113
IV. RESULTS AND DISCUSSION
HEC-RAS v5.0.3 had been used to delineate floodplain which is displayed in RAS Mapper. The vulnerable areas
in watershed for both return period had been calculated by importing inundation extents to ArcMap. Total
floodplain area for flood with 50 year return period is 301.5 sq.km. and that for 100 years is 327 sq.km. while
maximum inundation areas including agriculture, urban or village and barren land for a flood with return
period of 50 and 100 years is 270 sq.km and 295 sq.km respectively as shown in Error! Reference source not
found. Below.
Figure 9: Inundated area

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Model output is shown in Error! Reference source not found. which shows inundated areas in blue. The most
affected land cover type is vegetation as evident in the Error! Reference source not found.. The three districts
are rich in wheat, maize, vegetables and sugarcane. As agriculture vegetation is the primary source of livelihood
of the area under study, future flood occurrences might be more frequent and severe.
Figure 10: Impact of floods on different land cover
Kabul river flow through administrative districts of Nowshera, Charsadda, Peshawar. For assessment of
vulnerability three distinct type of areas has been identified which includes agricultural or vegetation, urban or
developed, and undeveloped or barren land. Vegetation comprise plantations such as cultivatable agricultural
and forest etc. Residential villages and manmade infrastructures are in developed land category while land
other than above two is barren land includes beaches, rocks and dry land soil.
In district of Peshawar, agriculture land is the most affected. A flood with return period of 100 years is
devastating in Peshawar. Agriculture land of 63.75 sq.km, developed urban land of 7.44 sq.km and undeveloped
barren land of 8.28 sq.km. is affected. Moreover it can be seen that there is a minute difference between the
amounts of area of both flood occurrences
Table 3: Floods in Peshawar region (sq.km.)
Return
period
50-year
Agriculture Developed Undeveloped
50-year 63.58 7.37 8.15
100-year 63.75 7.44 8.28
Figure 11: Affected landcover in Peshawar region
0
50
100
150
200
Area
(sq.km)
Land Affected
Landcover affected
50-year
100-year
0
10
20
30
40
50
60
70
Area
(sq.km)
Land Affected
District Peshawar
50-year
100-year

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Developed areas such as rural villages and urban infrastructure are most affected in the Nowshera region. Also
in 2010 flooding this region was the worst affected.
Figure 12: Affected landcover in Nowshera region
The results show that agricultural land is also the most affected type of land in Charsadda. With a small
population in floodplain, the village and urban land is less affected and is less vulnerable in future
Figure 13: Affected landcover in Charsadda region
V. CONCLUSION
This study was performed on 107 km section of Kabul river section stretching from Warsak dam to Khairabad
bridge. Kabul river was modeled using HEC-RAS 2D unsteady flood model. The floodplain model was calibrated
and validated for August 2010 flood event. The model was based on terrain data of SRTM of 30 meter
resolution. Peak flood discharges were calculated using gumbel extreme value distribution for 30-year record
from 1987 to 2016. Subsequently vulnerable areas were calculated using 50 and 100 year return period peak
flows. The analysis shows that inundation area increase with increase in flood return period. Flood hazard
assessment for both return period was performed to identify potential damages. Agricultural vegetation is
major land class prone to flood damage. These maps can serve as a warning during high flows in kabul river
help alleviate further damages in the study area.
0
10
20
30
40
50
60
70
Area
(sq.km)
Land Affected
District Nowshera
50-year
100-year
0
5
10
15
20
25
30
35
Area
(sq.km)
Land Affected
District Charsadda
50-year
100-year

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VI. REFERENCES
[1] “Human Cost of Disasters 2000-2019.” https://www.undrr.org/publication/human-cost-disasters-
2000-2019 (accessed Jan. 23, 2021).
[2] M. Masood and K. Takeuchi, “Assessment of flood hazard, vulnerability and risk of mid-eastern Dhaka
using DEM and 1D hydrodynamic model,” Nat. Hazards, vol. 61, no. 2, pp. 757–770, Mar. 2012, doi:
10.1007/s11069-011-0060-x.
[3] S. Grimaldi, A. Petroselli, E. Arcangeletti, and F. Nardi, “Flood mapping in ungauged basins using fully
continuous hydrologic-hydraulic modeling,” J. Hydrol., vol. 487, pp. 39–47, 2013, doi:
10.1016/j.jhydrol.2013.02.023.
[4] V. T. Chow, D. R. Maidment, and L. W. Mays, Applied hydrology (letters). 1988.
[5] H. de Moel, J. van Alphen, and J. Aerts, “Flood maps in Europe–methods, availability and use,” Nat.
Hazards Earth Syst. Sci., vol. 9, no. 2, pp. 289–301, 2009.
[6] K. E. K. Abderrezzak, A. Paquier, and E. Mignot, “Modelling flash flood propagation in urban areas using
a two-dimensional numerical model,” Nat. Hazards, vol. 50, no. 3, pp. 433–460, 2009.
[7] J. E. Schubert and B. F. Sanders, “Building treatments for urban flood inundation models and
implications for predictive skill and modeling efficiency,” Adv. Water Resour., vol. 41, pp. 49–64, 2012.
[8] USACE, “HEC-RAS River Analysis System Hydraulic Reference Manual. Version 5.0,” 2016.
[9] Z. Vojinovic and D. Tutulic, “On the use of 1D and coupled 1D-2D modelling approaches for assessment
of flood damage in urban areas,” Urban Water J., vol. 6, no. 3, pp. 183–199, Sep. 2009, doi:
10.1080/15730620802566877.
[10] D. S. Dhillon, F. A. Bombardelli, W. E. Fleenor, and K. Zamani, “On the issues associated with 2-D
modeling for flood mapping purposes,” 2014.

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FLOODPLAIN HAZARD MAPPING AND ASSESSMENT OF RIVER KABUL USING HEC-RAS 2D MODEL