A Study On Stream Bed Hydraulic Conductivity Of Beas River In India

IDL - International Digital Library
of Technology & Research
Volume 1, Issue 4, Apr 2017 Available at: www.dbpublications.org
International e-Journal For Technology And Research-2017
A Study On Stream Bed Hydraulic Conductivity Of Beas River In
India
1
VIRENDER KUMAR SARDA,
2
MIKHIL UNNIKRISHNAN
1,2
Civil Engineering Department, NIT Hamirpur
Abstract: Hydraulic conductivity is one of the principal and most important soil hydraulic characteristics and is used in all
equations for groundwater (subsurface water) flow. The vertical hydraulic conductivity of streambed plays an important role
in river water and groundwater interaction. Determination of the vertical hydraulic conductivity of the entire riverbed has
significant importance for the study of groundwater recharge and is a necessary parameter in numerical modeling of stream-
aquifer interactions. In the present study, primary objective was to determine the variation of streambed vertical hydraulic
conductivity along Beas River. To carry out this objective, three locations along the river (A, B and C) and four transects at
each location was selected. Data was collected for two seasons i.e. winter (November-January) and summer (March-May) of
2015-2016. The spatial and temporal variation of streambed vertical hydraulic conductivity of Beas riverbed using field
standpipe permeameter test and laboratory constant head permeameter test were carried out in this study. The results
indicated that there was a wide variation of Kv values obtained from lab test and field test. The values from laboratory test
were smaller than those of field test in all locations. Across the river, values of Kv increased from river bank to the middle of
the river at all locations. Along the river, the streambed Kv values decreased from location-A to location-B. At location-C,
the Kv values were found to be higher than that at location-B. The streambed vertical hydraulic conductivity values obtained
in summer season were larger than those obtained during winter season. The statistical distribution of streambed vertical
hydraulic conductivity along the Beas River was studied using normality tests. It was also observed from the normality tests
that Kv values were not normally distributed at location A and location B, but were normally distributed at location C.
Keywords: Streambed hydraulic conductivity, Beas River, spatial and temporal variation, permeameter tests, normality test.
1. INTRODUCTION
Hydraulic properties of a streambed are major control in the
hydrologic connection between a stream and an aquifer
Chenet al. (2008). They are key parameters in the
calculation of stream flow depletion (Chen and Shu, 2006) .
Better understandings on the sensitivity of various
hydraulic properties are beneficial for model development
and application purposes (Rocha et al., 2006). Streambed
characteristics such as vertical hydraulic conductivity, bed
material, thickness, width, topography, and the curvature
influence the streambed hydraulic properties and thus water
movement (Packman et al., 2004). The application of flow
laws to engineering problems such as design of earth dams,
tailing dams, clay liner for waste management practice, and
slope subjected to rain water infiltration requires the
quantification of hydraulic properties of soil (Gallage et al.,
2013).
Modeling of a groundwater system is generally based on
solving mathematical equations containing many
parameters characterizing the system. In order to have a
reliable model, its parameter values should fit their actual
ones. Sometimes the parameters can be measured from
samples in the field or in a laboratory, or they can be
determined by specially designed pumping well tests
(Ibrahim, 2013). Accurate estimation of aquifer properties
such as hydraulic conductivity, transmissivity and
storativity are considered crucial for successful
groundwater development and management practices
(Oosterbaan and Nijland, 1994).
Hydraulic conductivity K is one of the principal and most
important soil hydraulic characteristics (parameters) and it
is an important factor in water transport in the soil and is
used in all equations for groundwater (subsurface water)
flow (Stibinger, 2014). The value of a saturated soil Ks 
represents its average hydraulic conductivity, which
depends mainly on the size, shape, and distribution of the
pores. It also depends on the soil temperature and the
viscosity and density of the water (Oosterbaan and Nijland,
1994). In some structure-less soils (sandy soils) the K value
is the same in all directions, but usually the K values varies
with flow direction. Anisotropy plays very important role
in soil hydrology. Hydraulic conductivity in vertical and
horizontal direction is marked as Kv , Kh and value in
intermediate direction is Kr . Soil layers vertical hydraulic
conductivity is very often different from horizontal
conductivity because of vertical differences in the structure,
texture and porosity (Stibinger, 2014). The vertical and
horizontal hydraulic conductivities of the streambed play
important roles in surface water and groundwater
exchanges. Therefore, determination of the streambed
anisotropy is of importance in the analysis of stream-
aquifer interactions (Cardenas and Zlotnik, 2003).
Streambed vertical hydraulic conductivity plays an
important role in understanding and quantifying the stream-
aquifer interactions and stream ecosystems (Generaeux et
al., 2008, Mckenzie, 2008). Higher streambed Kv induces a
higher rate of stream depletion due to groundwater
withdrawal. Therefore, knowledge of streambed Kv is
essential to characterize hydrologic connections between a
stream and its adjacent aquifers, and is a necessary
parameter in numerical modeling of stream-aquifer
interactions (Min et al., 2012). The major goal in local
water resource management is to develop practices that
maintain adequate water levels in the streams while
allowing withdrawals for agricultural, domestic and
industrial production. The first step in this direction is
determining the spatial variation in streambed hydraulic
conductovity (Wue et al., 2015).
The Kv value of a soil profile can be highly variable from
place to place as well as at different depths (spatial
variability). Not only can different soil layers have different
hydraulic conductivities but, even within a soil layer, the

hydraulic conductivity can vary tremendously (Oosterbaan
and Nijland, 1994). Some studies have revealed that the
vertical hydraulic conductivity changes significantly along
the river cross section (perpendicular to the river flow)
(Min et al., 2012). Along the river flow (in the downstream
direction), even in a small reach (no more than hundreds of
meters), the permeability varied remarkably. Temporally
changing hydraulic conductivity has the capacity to impact
rates of ecological and biogeochemical processes (Wue et
al., 2015). The temporal variability of streambed Kv has
been studied in detail in the past decades. These studies
have shown that temporal pattern in streambed vertical
hydraulic conductivity differed from one location to another
and can be an important consideration in induced stream
infiltration (Springer et al., 1999).
In the rivers of Himachal Pradesh measurement of changes
in the elevation of the streambed surface suggests erosion
and deposition which plays an important role in causing the
spatial and temporal variability in streambed (Surian,
2002). The River Beas serves as a major source of water for
the cities and villages along its bank. It has been utilized for
irrigation purposes and harnessing hydroelectricity. Several
dams are constructed across its span like the Pong Dam,
Pandoh Dam, and Dhaulasidh dam. According to the data
collected from Satluj Jal Vidyut Nigam (SJVN) Limited,
the maximum and minimum silt deposition recorded was in
the month of July and September respectively and the
maximum and minimum discharge recorded was in the
month of July and January respectively. The difference
between the maximum and minimum value is found to be
of high magnitude resulting in appreciable changes in the
riverbed properties. This necessitates the need for detailed
study on spatial and temporal variation of hydraulic
conductivity of Beas River.
2. STUDY AREA
The study was conducted on Beas River at Tira sujanpur,
which is located in the district of Hamirpur, Himachal
Pradesh, India. The River Beas, which is a major tributary
of Indus river, originates at 32
o
2159N and 77
o
0508E
and flows for some 470 kilometers before meeting Sutlej
River in the Indian state of Punjab. The drainage basin of
Beas River is around 20,303 square kilometers large. The
average bed slope is 1 in 40 for first 120 km from its
source, which decreases to 1 in 5,000 near plains. The chief
tributaries are Bain, Banganga, Luni and Uhal.
Average flow for the Beas is 61,302 cusecs in August and
4641 cusecs in January. The river flow in summer mainly
consists of monsoonal run off combined with snow-melt
discharge. The low flow in winter is more or less constant
(Map of India, 2016).
The climate of this river basin varies all through from very
hot summer to cold winter. The temperature varies from
38
o
C in summers to almost 0
o
C in winters. The period
from March to June is the period of continuous rise in
temperature. June is the hottest month of the year, with
mean maximum and mean minimum monthly temperatures
of the order of 36
o
C and 21
o
C respectively at Indian
Meteorological Department (IMD) station at Mandi. The
monsoon rainfall occurs mainly during July to September.
Maximum rainfall occurs in the months of July-August.
The annual average rainfall at IMD stations at Mandi and
Dharamshala are 1642.2 mm and 3035 mm respectively
(Tira Sujanpur, 2015).
2
The principal soil types found in this riverbed are sub-
mountain, brown hill, and alluvial soils. The maximum and
minimum silt deposition recorded was in the month of July
and September, with mean maximum and minimum
monthly silt deposition of the order 1079.99 ppm and 70.02
ppm respectively at Dhaulasidh dam site. The dam site is
located approximately 10 km from the downstream side of
the study area. The maximum and minimum discharge
recorded was in the month of July and January, with mean
maximum and minimum monthly discharge of the order
298.45 cumecs and 38.4 cumecs (SJVN, 2016).
In the study area (Fig. 1), three locations A, B, C were
selected over a stretch of 14 km in Beas River from Baleth
to Jangalberi. The details of these locations are given in
Table 1.
Figure 1 Map showing the study sites. In-situ tests were
performed at 3 locations (from sites A to C) between
Jangalberi and Bhaleth [Map of India, 2016].
Table 1 Details of different sites of location A, B and C
Location details Distance Distance Width
from between u/s of
river and d/s (m) river(m)
bank
Location- u/s Ts1 2.6
A site Ts2 19.0 223
Ts3 30.8
Ts4 42.3 821
d/s Ts1 1.3
site Ts2 13.7
135
Ts3 25.9
Ts4 43.0
B site Ts2 23.5 286
Ts3 42.1
Ts4 65.8 949
d/s Ts1 12.0
site Ts2 28.0
262
Ts3 50.0
Ts4 68.5
C site Ts2 20.1
853
Ts3 34.6
Ts4 50.6
1439
d/s Ts1 2.8
site Ts2 25.9
413
Ts3 47.5
Ts4 66.2
At each location, in-situ permeameter tests as well as
sample collection were performed at two points, upstream
(u/s) and downstream (d/s) of the location. At location-A
(Jangalberi), (u/s) and (d/s) sites were taken 850 m apart on

either side of the Mandh River joining the Beas River at A.
At location-B (Tira Sujanpur), (u/s) and (d/s) site were
taken 100 m apart on either side of the tributary Nuegal
khad joining the main river at B. At location-C (Baleth),
(u/s) site was the wider part of the river and (d/s) site was
the narrow part and they were 820 m apart. At every u/s and
d/s site, four transect points Ts1,Ts2,Ts3,Ts 4 were fixed
across the river for experimental works.
To study the spatial variation, vertical hydraulic
conductivity measurements taken at six locations along the
river and four transect at each location will be selected. To
study the temporal variation, it has also been proposed to
collect data for two seasons i.e. winter (November-January)
and summer (February-April).
A total of 48 measurements at four transect
Ts1,Ts2,Ts3,Ts4 at upstream and downstream sites of
three locations (A, B and C) in two seasons winter
(November-January) and summer (March-May) were
performed to determine the spatial and temporal variation
of streambed vertical hydraulic conductivity.
3. METHODOLOGY
3.1 Field test
3.1.1 Field standpipe permeameter test
The field standpipe permeameter test (SP) involves
inserting a pipe vertically into the streambed, filling the
pipe with river water, measuring the rate of decline of the
water level, and then calculating the vertical hydraulic
conductivity using the rate of decline (Fig. 2).
3.1.2 Sediment sampling
Once the field standpipe permeameter test was done, the
soil samples using sampler (Fig. 3) were collected from
about 20 cm distances around the standpipe sites so that
there was no significant difference in the soil
characteristics. The samples were then collected in
sampling bags and brought to the laboratory for lab test.
Figure 3 Sediment sampler
3.2 Laboratory test
3.2.1 Constant head permeameter test
Laboratory determination of vertical hydraulic conductivity
was done using the constant head permeameter test. The
constant head permeameter apparatus (Fig. 4) consist of a
mould with two porous stones and collar. The porous stones
were saturated and then placed on the drainage base. About
2.5 kg of sample was filled in the mould and then
compacted to the required density. In order to saturate the
sample, water reservoir was connected to the base and
water was allowed to flow upward. The reservoir was later
disconnected from the outlet. The specimen was connected
through the top inlet to the constant head reservoir, the
bottom outlet was opened and steady state of flow was
established. The quantity of flow for a convenient time
interval was noted. Temperature of water collected was also
noted. Using Darcy’s law, the hydraulic conductivity of
sample was calculated (Sobolewski, 2005):
Figure 2 In-situ permeameter [Chen, 2002]
In the present study, a polyvinyl chloride (PVC) pipe of
inner diameter 3.8 cm and length 140 cm was used. The
tube was inserted into the streambed sediments, ensuring
that the length of the sediment column was approximately
35 cm. River water was poured carefully into the pipe
without disturbing the sediment column inside the pipe.
After the initial water head in the pipe was recorded, the
stop watch was started and the elapsed time was recorded.
The water head in the pipe was recorded according to the
set time interval. Water temperature was also noted using
thermometer. During the each test, the water depth was
measured at each test location to determine its relationship
with streambed hydraulic conductivity. Using the water
head records at given time intervals, the values of Kv were
calculated from modified Hvorslev solution (Chen, 2002):
Kv 
Lv
ln
h1
(1)
t1  t2 h2
Where, LV = length of the sediment column in the pipe (m);
h1 = initial hydraulic head (m); h2 = final hydraulic head
(m); t1 = initial time at h1 (day) and t2 = final time at h2
(day).
3
K  QL (2)
Ah
Where Q = Flow rate (m
3
/day); L = length of sediment
column; A = area (m
2
); h = head (height of the water).
Figure 4 Constant head permeameter apparatus
3.3 Statistical analysis
The streambed vertical hydraulic conductivity values
obtained from the field permeameter tests were analyzed
statistically by normality tests to check whether the values
are distributed normally along the river. Normality tests are
used to determine if a data set is well-modeled by a normal
distribution and to compute how likely it is for a random
variable underlying the data set to be normally distributed
(Normailty test, 2016).
Statistical analysis of present data was done using the
normality tests by histogram plots and normality test

methods. The normality test methods such as Jarque–Bera
(J–B), Lilliefors, and Shapiro–Wilk (S–W) tests were used,
at the level of significance 0.05. Lilliefors test is an
adaption of the Kolmogorov– Smirnov (K–S) test. The S–
W test has requirements of the sample size N (7 ≤ N ≤
2,000), while Lilliefors tests are preferable to apply for a
large sample size N ( N ≥ 2,000). The J–B test is not good at
distributions with short tails. Lilliefors tests are also less
powerful than the S–W test (Oztuna et al., 2006).
3.3.1 Histogram
The simplest and perhaps the oldest graphical display for
one-dimensional data is the histogram, which divides the
range of the data into bins and plots bars corresponding to
each bin, the height of each bar reflecting the number of
data points in the corresponding bin. The histogram
graphically summarizes the distribution of a data set such as
the center of the data, spread of the data, skewness of the
data, presence of outliers, and presence of multiple modes
in the data (Oztuna et al., 2006). In the present study
histogram represents graphically the frequency distribution
of field Kv values at each location. Each location (location
A, B and C) comprises of sixteen streambed Kv values
(upstream and downstream values) of two seasons.
3.3.2 Jarque–Bera (J–B) test
The Jarqua-Bera test depends on skewness and kurtosis
statistics. The null hypothesis is that the data is normally
distributed. The test is based on the test statistic value (JB)
which is calculated using the following formula (Normality
test, 2016):

S
2
EK
2 
JB  N    (3)
6 24 
 
Where S = skewness; EK = excess kurtosis. The adjusted
formulae for S and EK with small sample adjustments
are given as:
n
N
x  m
3
S 
i1
(4)N 1N  2 SD
3
Where x = data observations; m = mean; SD = standard
deviation:
n
N N 1
 x  m
4
3N 1
2
EK 
i 1
N 1N  2N  3 SD
4
N  2N  3
(5)
The critical value of J-B test at significance level of 0.05 is
5.99. If the calculated value JB is found to be greater than
the critical value, then the null hypothesis is rejected and
data will be concluded as not normally distributed.
3.3.3 Lilliefors test
The Lilliefors corrected Kolmogorov-Smirnov KS  Test
compares the cumulative distribution of data to the
expected cumulative normal distribution. This test is
different from the KS  test because the population
parameters that are unknown are estimated, while the
statistic is the same. The table values of the two tests are
different, which results in different decisions. The test
statistics associated with Lilliefors test is given as (Abdi
and Molin, 2007):
4
L  Maxf Zi  cZi , f Zi  pZi1 (6)
Where f Zi = frequency associated with score Zi which is
the proportion of score smaller or equal to its value; pZi 
= probability associated with this score if it comes from a
standard normal distribution with a mean of 0 and a
standard deviation of 1.
pZi 
Zi 1 1
Zi
2 
(7) exp  
2  2 
Zi 
xi  m
(8)
S
N
 m
2
 xi
S
2
 i1 (9)
N 1
If the calculated value of L is found to be greater than the
Lcritical value, the null hypothesis is rejected (Abdi and
Molin, 2007).
3.3.4 Shapiro-Wilk (S-W) test
The Shapiro-Wilk Test (S-W) has become the preferred test
of normality because of its good power properties as
compared to a wide range of alternative tests (Shapiro-Wilk
(S-W) test, 2016). The SW test depends on the correlation
between given data and their corresponding normal scores.
A significant W statistic causes the researcher to reject the
assumption that the distribution is normal. The shapiro-wilk
test statistics is given by:
W 
b
2
(10)
SS
N
SS  xi  m
2
(11)
i1
m
b
a
i 
x
N 1i
 x
i  (12)
i1
Where ai = weight for sample size N . corresponding to the calculated W is found. If the p 
value is less than 0.05, and then the null hypothesis is
rejected (Mendis and Pala, 2003).
3.3.5 Box plot
A box plot provides an excellent visual summary of many
important aspects of a distribution. Box plots display
batches of data (McGill et al., 1978). It is a graphical
rendition of statistical data based on the minimum, first
quartile, median, third quartile, and maximum. The term
"box plot" comes from the fact that the graph looks like a
rectangle with lines extending from the top and bottom
(Box plot, 2016). Box plots provide basic information about
a distribution and are good at portraying extreme values and
are especially good at showing differences between
distributions (McGill et al., 1978).
The values of streambed at four transect points across the
river calculated for upstream and downstream of three
locations along the river during two seasons i.e. winter
(November-January) and summer (March-May) using field
and laboratory tests were plotted against distance of each
transect from the bank in order to analyze the variation of
p  value

Kv . Figures 5 is a typical of such graphs for winter season considerably less sediment transport and deposition from
at location C upstream. the tributary to the main river, thus resulting in higher Kv
during summer.
Figure 5 Variation of Kv across the river section at
Location-C u/s site (winter season)
From these it was noted that there was a wide variation of
streambed vertical hydraulic conductivity obtained from
field and laboratory test. The Kv values from laboratory
test were smaller than those of field test in all locations. The
variation of Kv obtained from field and lab tests can
be due to the disturbance in the structure of the sample
taken for the lab test by sediment sampling. In the case of
the field test, the sample inside the pipe was less disturbed
than the sample collected for lab tests. It was also observed
that up to a distance of 30 meters, there was not much
variation in field and laboratory Kv values. Beyond 30
meters, high variation of was observed and this may be due
to higher variation in riverbed profile.
It may also be noted that, at all locations along the river,
values of Kv increased from river bank to the middle of
the river. The center of the river usually has higher flow
velocity than the sides of the channel. A larger value may
occur in the channel sediments where the flow velocity is
generally higher, since fine-grained sediments can be
washed away by higher flows and they may deposit again in
the area with lower flow velocity. This may lead to higher
seepage towards middle of the river. Greater water depth
can also result in coarser sediments which can lead to
higher streambed Kv
Figure 6 is a typical plot showing variation of Kv at location
A (d/s) for summer season (March-May).
Figure 6 Variation of Kv across the river section at
Location-A d/s site (summer season)
From these figures it was noted that the variation in Kv in
summer season was the same as that observed during winter
season. However, the streambed vertical hydraulic
conductivity values obtained in summer season were larger
than those obtained during winter season. This may be due
to the lesser discharge during summer which leads to
5
4 STATISTICAL ANALYSIS
4.1 Normality test
Histograms representing graphically the frequency
distribution of field Kv values of each location (A, B and C)
in a 15-km reach of the Beas River and comprising of
sixteen streambed Kv values (upstream and downstream
values of two seasons) were plotted. The population was
taken as sixteen Kv values ((4 u/s transect points + 4 d/s
transect point) × two seasons = 16). Their corresponding
frequency and normal probability were found and the plots
were drawn with streambed Kv values along abscissa and,
frequency and normal probability along ordinate. Figure 7
is one such typical plot for location A.
Figure 7 Histogram plot of streambed hydraulic
conductivity at location-A
Normality tests by these histogram plots showed that
streambed values were not normally distributed at location
A and location B but were normally distributed at location
C. At location A and B, the streambed values were
positively skewed as per the histogram plots.
The normality test methods such as Jarque–Bera (J–B),
Lilliefors, and Shapiro–Wilk (S–W) tests were also carried
out and the results obtained from these tests are shown in
the Table 2.
Table 2 Results of normality tests for location A, B and C
Location Jarque– Lilliefors Shapiro-Wilk
Bera (J–B) Test (S-W) Test
Test
Location-A Yes No No
Location-B No No No
Location-C No Yes No
According to the J-B test, the values at location-A were
found to be normally distributed. But, the histogram plot
showed that these values were skewed. The reason behind
this is the unsuitability of J-B test for small size data.
Usually J-B test is employed for large size samples. For
small samples the decision rule can be viewed as
approximate. According to the Lilliefors test, location C
values were found to be normally distributed. The
histogram plot also showed the same result. S-W test
showed that none of the data is normally distributed.

It could be seen that Lilliefors test for normality gave same
results as the histogram plot results. So, Lilliefors normality
test is suitable for these streambed data. In general,
streambed Kv values were found not to be normally
distributed in location A and B. The reason may be due to
the effect of tributaries at these locations.
4.2 Box plot
Box plot of streambed values of three test locations
(location A, B and C) between Jangalberi and Baleth of
Beas River is shown in Fig. 8. In the box plot, box indicates
the upper and lower quartile (75th and 25th percentile
value), the solid horizontal line inside the box indicates the
median value, and vertical line extends from the top of the
box indicate the maximum value, and another vertical line
extends from the bottom of the box indicate the minimum
value. The 25th and 75th percentile values are the values at
one-fourth and three-fourth positions of the total
population. The 25th percentile values for location A, B and
C are 12.324 m/day, 3.11 m/day and 6.372 m/day
respectively. The 75th percentile values for location A, B
and C are 39.74 m/day, 12.21m/day and 26.4 m/day
respectively.
i. There was a wide variation of Kv values obtained from
lab test and field test. The Kv values from
laboratory test were smaller than those of field test in
all locations in both the seasons. The variation of
obtained from field and lab tests can be due to the
disturbance in the structure of the sample taken for the
lab test by sediment sampling.
ii. Across the river, values of Kv increased from river
bank to the middle of the river at all locations. Up to a
distance of 30 meters, there was not much variation in
Kv values. Beyond 30 meters, high variation of was
observed in all locations.
iii. Along the river, the streambed Kv values decreased
from location-A to location-B. At location-C, the Kv
values were found to be higher than that at location-B.
iv. The streambed vertical hydraulic conductivity values
obtained in summer season were larger than those
obtained during winter season.
v. Among histogram plots and normality test methods
like J-B test, Lilliefors test and S-W test, results
obtained from Lilliefors test were found to be better
compatible with histogram plots. So, Lilliefors
normality test is suitable for the present streambed
data. It has also been found that values were not
normally distributed at location A and location B, but
were normally distributed at location C.
vi. The streambed Kv values were found to be maximum
at location-A and minimum at location-B. Along the
river flow, the streambed Kv values decreased from
location-A to location-B and again increased towards
location-C. The effect of tributaries in between these
locations might have played an important role in
variation of streambed Kv values.
Figure 8 Box plot of streambed Kv values of three test
locations (from sites A to C) between Jangalberi and Baleth
of Beas River
From this plot, it can be seen that, the streambed Kv values
were found to be maximum at location-A and minimum at
location-B. Along the river flow, the streambed Kv values
decreased from location-A to location-B. The overall
reduction in values may be due to the effect of tributaries
which carry fine sediments in to the main stream. At
location-C, the Kv values were found to be higher than
that at location-B. Absence of tributaries in between
location-B and C can be the reason for the sudden increase
in Kv values.
5. CONCLUSION
The variation in the vertical hydraulic conductivity was
studied in a reach of Beas River in India. Data was
collected at the upstream and downstream of three sections
along the reach with six points on each transects for
summer and winter season. It was found that:
6
REFERENCES
1. Abdi, H. and Molin, P. (2007). “Lilliefors/Van Soest’s test of
normality”, Encyclopedia of Measurement and Statistics, pp.
540-544. http://www.utd.edu/∼herve
2. Box plot, http://whatis.techtarget.com/definition/box-plot, 09-
June-2016.
3. Cardenas, M. B. and Zlotnik, V. A. (2003). “A simple
constant-head injection test for streambed hydraulic
conductivity estimation.” Ground Water, Vol. 41, No. 6, pp.
867-871.
4. Chen, X. (2002). “Determination of Streambed Hydraulic
Conductivity Using Extended Permeameter Methods.”
Ground Water/Surface Water Interactions AWRA, Summer
Specialty Conference, July 1-3.
5. Chen, X., Burbach, M. and Cheng, C. (2008). “Electrical and
hydraulic vertical variability in channel sediments and its
effects on stream flow depletion due to groundwater
extraction.” Journal of Hydrology, 352, pp. 250– 266.
6. Chen X. H. and Shu, L. C. (2006). “Groundwater
evapotranspiration captured by seasonally pumping wells in
river valleys.” Journal of Hydrology, Vol. 318, pp. 334–347.
7. Gallage, C., Kodikara, J., and Uchimura, T. (2013).
“Laboratory measurement of hydraulic conductivity functions
of two unsaturated sandy soils during drying and wetting
Processes." Soil and Foundations, Vol. 53, No. 3, pp. 417-
430.
8. Genereux, D. P., Leahy, S., Mitasova, H., Kennedy, C. D.,
and Corbett, D. R. (2008). “Spatial and temporal variability

of streambed hydraulic conductivity in West Bear Creek,
North Carolina, USA.” Journal of Hydrology, Vol. 358, No.
3-4, pp. 332-353. doi.org/10.1016/j.jhydrol.2008.06.017.
9. Ibrahim, M. A. E. (2013). “Calibration of hydraulic
conductivities in groundwater flow models using the double
constraint method and the Kalman filter.” Department of
Hydrology and Hydraulic Engineering, Vrije Universiteit
Brussel, VUB – Hydrologie (72).
10. Map of India, http://www.indiamapped.com/rivers-in-
india/beas-river/, 4- January-2016.
11. McGill, R., Tukey, J. W., and Larsen, J. W. (1978).
“Variations of box plots.” The American Statistician, Vol. 32,
No. 1, pp. 12-16.
12. Mckenzie, C. R. (2008). “Measurements of hydraulic
conductivity using slug tests in comparison to empirical
calculations for two streams in the Pacific Northwest, USA.”
M.Sc. (Engg.) thesis, Washington State University,
Department of Biological Systems Engineering.
13. Mendes M. and Pala A. (2003). “Type I Error Rate and
Power of Three Normality Tests.” Pakistan Journal of
Information and Technology, Vol. 2, No. 2, pp. 135-139.
ISSN 1682-6027
14. Min, L., Yu, J., Liu, C., Zhu, J., and Wang, P. (2012). “The
spatial variability of streambed vertical hydraulic
conductivity in an intermittent river, northwestern China.”
Environmental Earth Science, Vol. 69, pp. 873–883.
15. Normality test, https://en.wikipedia.org/wiki/Normality_test,
09-June-2016.
16. Oosterbaan, R. J. and Nijland, H.J. (1994). “Determining the
saturated hydraulic conductivity.” International Institute for
Land Reclamation and Improvement (ILRI), Publication 16,
2
nd
revised edition, Wageningen, Netherlands.
17. Oztuna, D., Elhan, A. H., and Tuccar, E. (2006).
“Investigation of four different normality tests in terms of
type 1 error rate and power under different distributions.”
Turkish Journal of Medical Sciences, Vol. 36, No. 3, pp.
171–176.
18. Packman, Aaron I., Salehin, M., and Zaramella, M. (2004).
“Hyporheic exchange with gravel beds: Basic hydrodynamic
interactions and bed form-induced advective flows.” Journal
of Hydraulic Engineering, Vol. 130, No. 7, pp. 647-656. DOI:
10.1061/(ASCE)0733-9429(2004)130:7(647).
19. Rocha, D., Abbasi, F., and Feyen, J. (2006). “Sensitivity
analysis of soil hydraulic properties on subsurface water flow
in furrows.” Journal of Irrigation and Drainage Engineering,
ASCE, Vol. 132, No. 4.
20. Shapiro-Wilk and related tests for normality,
https://math.mit.edu/~rmd/465/shapiro.pdf, 14-March-2016.
21. SJVN Limited, http://sjvn.nic.in/project-details.htm?14, 10-
January-2016.
22. Sobolewski, M. (2005). “Various Methods of the
Measurement of the Permeability Coefficient in Soils
Possibilities and Application.” Electronic Journal of Polish
Vol. 8, No. 2. http://www.ejpau.media.pl/volume8/issue2/art-
13.html
23. Springer, A. E., Petroutson, W. D., and Semmens, B. A.
(1999). “Spatial and temporal variability of hydraulic
conductivity in active reattachment bars of Colorado River,
Grand Canyon.” Ground Water, Vol. 37, No.3, pp. 338-344.
24. Stibinger, J. (2014). “Examples of determining the hydraulic
conductivity of soils theory and applications of selected basic
methods.” University Handbook on Soil Hydraulics.
25. Surian, N. (2002). “Downstream variation in grain size along
an Alpine river: analysis of controls and processes.”
Geomorphology, 43, Page 137– 149.
26. Tira Sujanpur, https://en.wikipedia.org/wiki/Tira_Sujanpur,
24-September-2015.
27. Wu, G., Shu, L., Lu, C., Chen, X., Zhang, X., Appiah-Adjei,
E. K., and Zhu, J. (2015). “Variations of streambed vertical
hydraulic conductivity before and after a flood season.”
Hydrogeology Journal, Vol. 23, No. 7, pp. 1603-1615.
7

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