TITLE:
Modeling of Discharge Distribution in Bend of Ganga River at Varanasi
AUTHORS:
Manvendra Singh Chauhan, Prabhat Kumar Singh Dikshit, Shyam Bihari Dwivedi
KEYWORDS:
ADCP, Discharge, Linear Regression, River, RMS
JOURNAL NAME:
Computational Water, Energy, and Environmental Engineering,
Vol.4 No.3,
June
9,
2015
ABSTRACT: Dynamics of
river behavior plays a great role in meandering, sediment transporting, scouring,
etc. of river at bend, which solely depends on hydraulics properties such as horizontal
and vertical stress, spatial and temporal variation of discharge. Therefore understanding
of discharge distribution of river Ganga is essential to apprehend the behavior
of river cross section at bend particularly. The measurement of discharge is not
very simple as there is no instrument that can measure the discharge directly, but
velocity measurement at a section can be made. Velocity distribution at
different cross sections at a time is also not easy with single measurement
with the help of any instrument and method, so it required repetitions of the
measurement. Velocity near the end of bank, top and bottom layer of natural streams
is difficult to be measured, yet velocity distribution at these regions plays important
role in characterizing the behavior of river. This paper deals with the new advanced
discharge measurement technique and measured discharge data has been used for
modelling at river bend. To carry out the distribution of discharge and velocity
with depth in river Ganga, the length of river in study area was distributed into
14 different cross sections, M-1 to M-14, measured downstream to upstream and the
measurement was done by using of ADCP (Acoustic Doppler Current Profiler). At each
cross section, profiles were measured independently by an ADCP and data acquired
from ADCP were further used for the regression modeling. A multiple linear regression
model was developed, which showed a high correlation among the discharge, depth
and velocity parameters with the root mean square error (R2) value of
0.8624.