Journal of Water Resource and Protection, 2012, 4, 847-850 Published Online October 2012 (
Calibration of Channel Roughness for Mahanadi River,
(India) Using HEC-RAS Model
Prabeer Kumar Parhi1, R. N. Sankhua2, G. P. Roy3
1Center for Water Engineering and Managemen t, Central University of Jharkhand, Ranchi, India
2National Water Academy, Pune, India
3Department of Water Resources, Bhubaneshwar, India
Received June 8, 2012; revised July 9, 2012; accepted August 10, 2012
Channel roughness is the most sensitive parameter in development of hydraulic model for flood forecasting and flood
plane mapping. Henc e, in the present study it is attempted to calibr ate the channe l roughness co ef ficient ( Manning’ s “n”
value) along the river Mahanadi, Odisha through simulation of floods using HEC-RAS. For calibration of Manning’s
“n” value the flood of year 2003 has been considered. The calibrated model, in terms of channel roughness, has been
used to simulate the flood for year 2006 in the same river reach. The performance of the calibrated and validated
HEC-RAS based model is tested using Nash and Sutcliffe efficiency. It is concluded from the simulation study that
Mannnig’s “n” value of 0.032 gives best result for Khairmal to Munduli reach of Mahanadi River.
Keywords: Hydrodynamic Model; Calibration; Simulation; Flood Hydrograph ; Validation; HEC-RAS
1. Introduction
For flood forecasting and flood plane mapping, various
hydrodynamic models, based on hydraulic routing, have
been developed and applied to different rivers in the past
using computer technology and numerical techniques.
For flood warning, the discharge and river stage were
chosen as the variables [1], which along with other hy-
draulic properties are interrelated to each other. Among
various hydraulic parameters, the channel roughness
plays very important role in the study of open channel
flow particularly in hydraulic modeling. Channel rough-
ness is a highly variable parameter which depends upon
number of factors like surface roughness, vegetation
cover, channel irregularities, channel alignment etc. [2].
The channel roughness is not a cons tant parameter and it
varies along the river depending upon variation in chan-
nel characteristic along the flow. Good number of re-
searchers including Patro et al. [3], Usul and Turan [4],
Vijay et al. [5] and Wasantha Lal A. M. [6] has cali-
brated channel roughness for different rivers for the de-
velopment of hydraulic model. Datta et al. [2] estimated
single channel roughness value for open channel flow
using optimization method, taking the boundary condi-
tion as constraints. Prafulkumar et al. [7] calibrated
channel roughness for Lower Tapi River, India using
HEC-RAS model.
In the above context, there is a need to calibrate the
channel roughness coefficient (Manning’s “n” value)
along the river Mahanadi, Odisha through simulation of
floods, using HEC-RAS. It will be pertinent to mention
that the river Mahanadi has experienced several historic
floods which have caused huge loss to life and property
in Odisha.
2. Model Description
In the present study, unsteady, gradually varied flow sim-
ulation model, which is dependent on finite difference
so lution s of th e Sain t-Venan t equations (Equations (1) and
(2)), has been used to simulate the flood in the Mahanadi
River. Here HEC-RAS has been used to perform one
dimensional hydraulic calculation for full network of
natural and constructed channels [8].
 (1)
tx x
  (2)
where A = cross-sectional area normal to the flow; Q =
discharge; g = acceleration due to gravity; H = elevation
of the water surface above a specified datum, also called
stage; So = bed slope; Sf = energy slope; t = temporal co-
ordinate and x = longitudinal coordinate. Equations (1)
and (2) are solved using the well known four-point im-
plicit box finite difference scheme [9].
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3. Study Reach
In the context of flood scenario, the Mahanadi system
can be broadly divided into two distinct reaches: 1) Up-
per Mahanadi (area upstream of Mundili barrage, inter-
cepting a catchment of 132,100 sq km) [10], which does
not have any significant flood problem; 2) Lower Maha-
nadi (area downstream of Mundili barrage, in tercepting a
catchment of 9304 sq km). The key area downstream of
Hirakud up to Munduli intercepting a catchment of
48,700 sq km is mainly responsible for flood havoc in the
deltaic area of Mahanadi. Figure 1 shows the details of
catchments of Mahanadi Basin inside and outside of
Orisssa. In the present study, river reach in the Mahanadi
system extending over a length of 200 km from Khairmal
to Munduli is con s idered for analysis.
4. Geometric and Hydrologic Data
The Channel geometry, boundary conditions and channel
resistance are required for conducting flow simulation
through HEC-RAS. The cross-section data at 15 meter
intervals from Khairmal to Munduli (head of Mahanadi
Delta) extending over a length of 200 km were collected
from Department of Water Resources Odisha [11]. The
flood hydrograph at Khairmal and the friction slope of
the reach have been considered as up-stream and down-
stream boundary conditions respectively. The flood hy-
drograph at Munduli has been used for validation of the
5. Calibration and Simulation of HEC-RAS
Model for Manning’s Roughness
Coefficient “n”
The data pertaining to the floods for years 2003 has been
used for calibration of Manning’s roughness coefficient,
“n”. In the present study, effort has been made to cali-
brate Manning’s roughness coefficient for single value
using aforesaid data and, subsequently, different values
have been used to ju stify their ad equ acy for si mulation o f
flood in the study reach. Various single values used in
calibration for whole reach for floods of year 2003 are
shown in Table 1. The table, also, shows the flood year,
flow duration and name of gauging station for calibration
and validation.
24% of
70% of D/S
33760Tel Sub-basin
34% of
58% of the
U/S of th e
48700D/S of
Hirakud upto
59% of
83400U/S of
CatchmentArea (1,41,600 Sqkm.)
Figure 1. Details of catchments of Mahanadi system inside and outside of Orissa.
Table 1. Flow year, simulation duration, Manning’s “n” and gauge station used for calibration.
Flow year Simulation duration Roughness coefficient Manning’s “n”Nash and Sutcliffe efficiency Guage station used for calibration
0.04 68
0.035 70.5
0.032 76.74
0.03 70
2003 Aug-27, 00.00 hrs to
Sep-8, 09:00
0.25 67
Munduli (calibration)
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P. K. PARHI ET AL. 849
Simulation of Flow for Different Value of
Manning’s “n”
The HEC-RAS model for the Mahanadi River (Khairmal
to Munduli) has been used to simulate the flow for dif-
ferent single roughness coefficients (Manning’s “n”) for
the flood of year 2003. To arrive some optimal value for
aforementioned model, the simulated flow hydrograph
was compared with observed flow hydrograph at Mun-
duli gauging site. Nash and Sutcliffe [12] efficiency test
has been used for comparison of simulated flow hydro-
graph with the observed flow hydrograph for various
Manning’s “n”. The comparison of observed and simu-
lated flow hydrograph (calibration) at Munduli gauging
station is shown in Figure 2.
6. Performance of Calibrated Model in
Simulation of Flood for Year 2006
The calibrated HEC-RAS based model has been used to
simulate the flood for year 2006. The comparison of ob-
served and simulated flow hydrograph at Munduli gaug-
ing station is s ho wn in Figure 3.
7. Conclusions
On the basis of simulation carried out for the Mahanadi
River (Khairmal to Munduli) following findings can be
1) The most effective single Manning’s roughness co-
efficient calibrated for the reach Khairmal to Munduli of
the Mahanadi River is 0.032.
Figure 2. Observed and simulated flow hydr ograph at Munduli (calibration).
Figure 3. Observed and simulated flow hydr ogr aph at M unduli (validation).
Table 2. Flow duration, Manning’s “n” and gauge station used for validation at Munduli.
Flow year Simulation duration Roughness coefficient Manning’s “n”Nash and Sutcliffe efficiencyGuage station used for calibration
2006 Aug-30, 00:00 to Sep-4,
09:00 0.032 84.65 Munduli (validation)
Copyright © 2012 SciRes. JWARP
2) The performance of calibrated model has been veri-
fied for flood of year 2006. Close agreement (84.65%
efficiency) have been arrived between simulated and
observed flows for Munduli gauging station.
3) For flood forecasting and flood plane mapping us-
ing HEC-RAS, Manning’s roughn ess coeff icient of 0.032
may yield best result.
4) Furthermore, the calibrated Manning’s roughness
coefficient works best for high flow only, which needs to
be verified for lean flows in the f o cus reach .
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