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

2

VIRENDER KUMAR SARDA, 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 streamaquifer 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. pores. It also depends on the soil temperature and the viscosity and density of the water (Oosterbaan and Nijland, 1. INTRODUCTION 1994). In some structure-less soils (sandy soils) the K value Hydraulic properties of a streambed are major control in the is the same in all directions, but usually the K values varies hydrologic connection between a stream and an aquifer with flow direction. Anisotropy plays very important role Chenet al. (2008). They are key parameters in the in soil hydrology. Hydraulic conductivity in vertical and calculation of stream flow depletion (Chen and Shu, 2006) .

Better understandings on the sensitivity of various horizontal direction is marked as Kv , Kh and value in hydraulic properties are beneficial for model development intermediate direction is Kr . Soil layers vertical hydraulic and application purposes (Rocha et al., 2006). Streambed conductivity is very often different from horizontal characteristics such as vertical hydraulic conductivity, bed conductivity because of vertical differences in the structure, material, thickness, width, topography, and the curvature texture and porosity (Stibinger, 2014). The vertical and influence the streambed hydraulic properties and thus water horizontal hydraulic conductivities of the streambed play movement (Packman et al., 2004). The application of flow important roles in surface water and groundwater laws to engineering problems such as design of earth dams, exchanges. Therefore, determination of the streambed tailing dams, clay liner for waste management practice, and anisotropy is of importance in the analysis of streamslope subjected to rain water infiltration requires the aquifer interactions (Cardenas and Zlotnik, 2003). quantification of hydraulic properties of soil (Gallage et al., Streambed vertical hydraulic conductivity plays an 2013). important role in understanding and quantifying the streamModeling of a groundwater system is generally based on aquifer interactions and stream ecosystems (Generaeux et solving mathematical equations containing many al., 2008, Mckenzie, 2008). Higher streambed Kv induces a parameters characterizing the system. In order to have a reliable model, its parameter values should fit their actual higher rate of stream depletion due to groundwater ones. Sometimes the parameters can be measured from withdrawal. Therefore, knowledge of streambed Kv is samples in the field or in a laboratory, or they can be essential to characterize hydrologic connections between a determined by specially designed pumping well tests stream and its adjacent aquifers, and is a necessary (Ibrahim, 2013). Accurate estimation of aquifer properties parameter in numerical modeling of stream-aquifer such as hydraulic conductivity, transmissivity and interactions (Min et al., 2012). The major goal in local storativity are considered crucial for successful water resource management is to develop practices that groundwater development and management practices maintain adequate water levels in the streams while (Oosterbaan and Nijland, 1994). allowing withdrawals for agricultural, domestic and Hydraulic conductivity K is one of the principal and most industrial production. The first step in this direction is important soil hydraulic characteristics (parameters) and it determining the spatial variation in streambed hydraulic is an important factor in water transport in the soil and is conductovity (Wue et al., 2015). used in all equations for groundwater (subsurface water) The Kv value of a soil profile can be highly variable from flow (Stibinger, 2014). The value of a saturated soil Ks  place to place as well as at different depths (spatial represents its average hydraulic conductivity, which variability). Not only can different soil layers have different depends mainly on the size, shape, and distribution of the hydraulic conductivities but, even within a soil layer, the

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 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

o

o

of Indus river, originates at 32 2159N and 77 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

o

o

38 C in summers to almost 0 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

o

o

of the order of 36 C and 21 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 submountain, 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 Locationu/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 Locationu/s Ts1 8.0 Ts2 23.5 B site 286 Ts3 42.1 Ts4 65.8 949 d/s Ts1 12.0 site Ts2 28.0 262 Ts3 50.0 Ts4 68.5 Locationu/s Ts1 6.3 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

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 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): K  QL (2) Ah

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

t1  t2

ln

h1

(1)

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

3

Where Q = Flow rate (m /day); L = length of sediment 2 column; A = area (m ); 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

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 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):  2 2  JB  N  S  EK  (3) 6 24     Where S = skewness; EK = excess kurtosis. The adjusted formulae for S and EK with small sample adjustments are given as: n

x  m

N

i1

3

n

N N 1 EK  N 1N  2N  3

 x  m 

i 1

SD

4

3N 1

2

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):



(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.

Z 1 1 2  pZi   i Zi  (7) exp  2   2 x m Zi  i S

N

2

  xi

(8)  m

2

 i1

(9) N 1 If the calculated value of L is found to be greater than the S

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: 2 b W (10) SS N

SS 

3

S  N 1N  2 SD (4) Where x = data observations; m = mean; SD = standard deviation:

4



L  Max f Zi  cZi , f Zi  pZi1

b

xi

p  value

2

 m

i1 m

a x i

N 1i

(11)

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

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 Kv . Figures 5 is a typical of such graphs for winter season at location C upstream.

considerably less sediment transport and deposition from the tributary to the main river, thus resulting in higher Kv during summer.

4

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

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.

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

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– Bera (J–B) Test

Lilliefors Shapiro-Wilk Test (S-W) 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.

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 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,

i.

There was a wide variation of Kv values obtained from

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 ii. (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 iii. 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 iv.

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 v. respectively.

vi.

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.

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.

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.

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. 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.

REFERENCES 1.

2.

3.

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

4.

5.

6.

7.

8.

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 Box plot, http://whatis.techtarget.com/definition/box-plot, 09June-2016.

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.

Chen, X. (2002). “Determination of Streambed Hydraulic Conductivity Using Extended Permeameter Methods.”

Ground Water/Surface Water Interactions AWRA, Summer Specialty Conference, July 1-3.

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.

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.

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. 417430.

Genereux, D. P., Leahy, S., Mitasova, H., Kennedy, C. D., and Corbett, D. R. (2008). “Spatial and temporal variability

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 of streambed hydraulic conductivity in West Bear Creek, North Carolina, USA.” Journal of Hydrology, Vol. 358, No.

25.

3-4, pp. 332-353. doi.org/10.1016/j.jhydrol.2008.06.017 .

9.

10.

11.

12.

13.

14.

15.

16.

26.

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).

Map of India, http://www.indiamapped.com/rivers-inindia/beas-river/, 4- January-2016.

McGill, R., Tukey, J. W., and Larsen, J. W. (1978).

“Variations of box plots.” The American Statistician, Vol. 32, No. 1, pp. 12-16.

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.

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 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.

Normality test, https://en.wikipedia.org/wiki/Normality_test, 09-June-2016.

Oosterbaan, R. J. and Nijland, H.J. (1994). “Determining the saturated hydraulic conductivity.” International Institute for Land Reclamation and Improvement (ILRI), Publication 16,

27.

nd

17.

18.

19.

20.

21.

22.

23.

24.

2 revised edition, Wageningen, Netherlands.

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.

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).

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.

Shapiro-Wilk and related tests for normality, https://math.mit.edu/~rmd/465/shapiro.pdf, 14-March-2016.

SJVN Limited, http://sjvn.nic.in/project-details.htm?14, 10January-2016.

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/art13.html 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.

Stibinger, J. (2014). “Examples of determining the hydraulic conductivity of soils theory and applications of selected basic methods.” University Handbook on Soil Hydraulics.

7

Surian, N. (2002). “Downstream variation in grain size along an Alpine river: analysis of controls and processes.”

Geomorphology, 43, Page 137– 149.

Tira Sujanpur, https://en.wikipedia.org/wiki/Tira_Sujanpur, 24-September-2015.

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.

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