statistical analysis of grain–size characteristics of

American Journal of Earth Sciences 2019; 6(1): 1-13

http://www.openscienceonline.com/journal/ajes

ISSN: 2381-4624 (Print); ISSN: 2381-4632 (Online)

Statistical Analysis of Grain–Size Characteristics of Streambed Sediments in River Catchments of the Lake Tana Basin, Northwest Ethiopia

Veeranarayana Balabathina1, *

, Ravi Kumar Kandula2

1Department of Geology, College of Natural & Computational Sciences, University of Gondar, Gondar, Ethiopia 2Department of Geology, College of Science & Technology, Andhra University, Waltair, India

Email address

*Corresponding author

To cite this article Veeranarayana Balabathina, Ravi Kumar Kandula. Statistical Analysis of Grain–Size Characteristics of Streambed Sediments in River

Catchments of the Lake Tana Basin, Northwest Ethiopia. American Journal of Earth Sciences. Vol. 6, No. 1, 2019, pp. 1-13.

Received: December 1, 2018; Accepted: December 19, 2018; Published: March 6, 2019

Abstract

This paper presents a statistical analysis of grain-size distributions in the samples of bed-material load sediments from the

Megech, Reb, Gumara, Gilgel Abbay, and Tana West rivers in the drainage basin area of lake Tana. In this study thirty one

streambed sediment samples were collected from these five river’s catchment areas. Grain size analysis was performed and

descriptive statistics for the grain-size parameters (mean size, sorting, skewness, and kurtosis) have been computed. Data were

analyzed by a range of grain size data analysis methods, such as correlation (Pearson’s correlation), bi-variant scatter plots,

box-plots, cumulative probability curves and C-M plots to reveal the effect of controlling variables upon the grain size spatial

pattern. Multivariate statistical analysis methods, such as One-way ANOVA, Linear regression analysis and Student’s t-test

were also used to discriminate within and between different catchment areas of lake Tana. The statistical analysis indicated that

grain-size variations among different river catchments are statistically significant while there is no significant variation within

a particular catchment except for the Megech and Gilgel Abbay, and also revealed that there is no statistically significant

variation in sediments between two adjacent river catchments of the lake except between Megech and Reb, and Megech and

Tana West. These variations may be attributed to various variables like drainage density, lithology, diversity of the sources of

the sediments, physiographic setting, lateral distance to lake and transport dynamics. Field evidence from this study suggests

that variables controlling within river catchment grain-size variability are strongly site specific.

Keywords

Catchment Areas, Grain-Size Distribution, Cumulative Curves, Statistical Analysis

1. Introduction

Sandy fluvial systems with their sediment transport and

deposition features in different settings, particularly in

highland areas, are dynamic and sensitive parts of lake basin

environment. Natural and anthropogenic factors often

produce changes in their conditions. Understanding how

grain-size distributions relate to these is important both in

modern systems and about ancient deposits. Grain-size

analysis, therefore, provides important clues to the sediment

provenance, transport history and depositional conditions [1-

3]. Hence, studying the grain-size distribution is important

for monitoring watershed impacts and changes in stream

habitat [4]. The grain-size statistical parameters such as mean

size, sorting, skewness, and sorting are useful to describe

grain-size distribution of the sediments. These parameters

also form the basis of many schemes for characterization of

sediments and in classifying sedimentary environments [5].

There are many methods for quantifying grain-size data and

presenting them in a graphical or statistical form as well

2 Veeranarayana Balabathina and Ravi Kumar Kandula: Statistical Analysis of Grain–Size Characteristics of

Streambed Sediments in River Catchments of the Lake Tana Basin, Northwest Ethiopia

more sophisticated multivariate statistical techniques have

been explored [6].

As there were no previously published granulometric

studies on the streambed sediments of the drainage basin of

lake Tana, the present study is intended to contribute to the

understanding of streambed sediment characterization and

to the general knowledge of sediment deposition patterns in

river catchments of the lake basin. Therefore, the current

study was focused on investigating the variations among

catchments of the lake Tana basin using grain-size

statistical parameters of various streambed material load

sediments. The main objectives of this work was to

interpret the mechanisms of sediment deposition by

applying comprehensive statistical analysis of sediment

grain-size distributions using various methods such as bi-

variant scatter plots, box-plots, probability accumulation

graphsand CM plots. Multivariate statistical analyses were

also used to find out intra- and inter-catchment variations of

grain sizes.

2. Study Area

Lake Tana, the largest fresh water lake in Ethiopia, is

located in a depression of the northwestern highlands of

Ethiopian plateau at an elevation of about 1800m (m.a.s.l)

(Figure 1). It is nearly a circular basin of 70km in

diameter [7]. It was formed by recent volcanic activity (a

volcanic blockage) related to a set of north-south faults,

known as the Lake Tana Rift [8-10]. This basin represents

a proto-rift west of the present East African Rift System

[11], associated with the oldest volcanic rocks. Lake Tana

presently having a surface area of 3,250 km2 and volume

of 28 km3, is relatively a smaller than the other East

African Great Lakes. The lake is surrounded by a

relatively small drainage basin of only four times of its

surface area, which is a sub-basin of the Abbay River, one

of the major river basins of Ethiopia. The Tana lake basin

drains 13750 km² of very broken and mountainous terrain,

with uplands and valleys, and is surrounded by occasional

rocky peaks with a total relief exceeding 4000 m which

are of volcanic origin (Figure 2). A little of the basin

draining to the lake is above 2575 m but rises to nearly

4000 m to the northeast, east and southeast. The total

annual runoff yield of the lake watershed is about 6.9

x109m

3 [12].

The lake has many tributaries that rise on the Ethiopian

plateau and major inflows to the lake that include the rivers

Gilgel Abbay (from the south), Megech (from the north) and,

Rib and Gumara rivers (from the east) with a high and

perennial, but highly seasonal, runoff [13]. More than 40

seasonal streams most of which are located in the west of

lake Tana also feed the lake with trivial runoff yield that

depends mostly on the local climate.

The alternating dry and rainy seasons result in difference

of runoff between the lowest (May-June) and the highest

(October-November). A typical radial drainage pattern is

observed around the lake basin that has provided a possible

site for storage to centripetal drainage pattern. The study

area is totally covered by volcanic rocks in the form of

composite volcanoes mainly alkaline basalts, trachytes and

phonolites (Figure 3). The runoff in the hilly catchments of

the lake Tana basin is characterized by significant spatial

variations, in terms of drainage density, topography,

lithology, land use, and rainfall which were found to affect

the runoff depth and runoff coefficients in the lake Tana

basin [14].

Figure 1. Location of study area.

American Journal of Earth Sciences 2019; 6(1): 1-13 3

Figure 2. Elevation and Drainage network of Lake Tana.

Figure 3. Geology map of study area.

3. Methods and Materials

In late 2017, thirty one sediment samples of bed-material

load were collected from the mobile streambeds in the

catchment areas of the rivers namely, Megech (11 samples),

Rib (5 samples), Gumara (5 samples), Gilgel Abbay (5

samples), and Tana West (5 samples). The sample locations

are shown in Figure 4. In order to ensure the quality of the

results, samples were collected from unmodified non-

depositional regions of streambeds in the basin which, in

general, were low in the fine-grained materials (less than 5%

of <60 µm over this area). Bed-load samples were skimmed

directly from the bed surfaces and collected into a plastic

sample container by using a grab sampler.

All bed-load sediment samples were dried and sieved

using 0.5phi intervals from -3.5 to +4.0 phi for grain-size

analysis. The grain-size data obtained were used to calculate

the grain-size statistical parameters such as mean size,

sorting, skewness, and kurtosis. The graphical computational

method of Folk and Ward (1957) was used for determination

of statistical size parameters. The statistical analysis results

and graphical representations of the data were performed by

using Gradistat software 8.0 [15] and SPSS 20. SPAN index

is calculated by the percentile (D10, D50 and D90) values

from the grain-size distribution to assess the relative

contribution of either the coarse or the finer granular

components. The graphical representations such as Box-plots

for determining the range of average grain- size parameters,

Bi-variant scattergraphs between mean size vs. sorting, mean

size vs. skewness, and sorting vs. skewness for understanding

the relationship between different grain-size parameters, CM

plots for relating the grain-size characteristics of the

sediments to the processes of their deposition and sediment

transport patterns, and trivariant plots for determining the

relationship between gravel-sand-mud from the bed-load

samples, and frequency curves, cumulative curves,

cumulative probability distribution graphs were prepared to

discriminate various parameters. In an attempt to better

understand how grain- size characteristics can vary within

and among river catchments of lake Basin, the results were

statistically analyzed using various statistical applications

like One-way Analysis of Variance (ANOVA), Linear

regression analysis, and Student’s t-test.

The SRTM Digital Elevation Model (DEM) data were

used to extract the drainage network of the lake Tana basin

and the drainage channels on the basis of which they were

classified into three orders. The boundary of the basin as well

as of the major river catchment areas were created from

topographic maps using GIS. The elevation and slope maps

were generated from the fine resolution DEM. Digital terrain

model was generated using Landsat-7 ETM+ satellite

imagery and DEM data. These data sets were processed with

the help of geo-processing techniques and various tools in

Arc GIS 10.3.1 and Erdas 10.2.



Figure 4. Sample locations of study area.

4. Results and Discussion

Different grain-size statistical parameters for the

streambed sediments from the river catchments of lake Tana

basin showed distinct differences in their values (Table 1).

Table 2 summarizes these grain-size parameters in percentage

distribution of sediment characteristics of the different

catchment areas. Sediment samples from the streambeds of

the Megech river catchment in the northern part of the lake

Tana basin were covered by medium to very coarse sand,

moderately to poorly sorted, very coarse skewed (negatively

skewed) to symmetrical, and meso to leptokurtic. The

streambeds of the Reb river catchment were mostly

composed of coarse sand, poorly sorted, very coarse skewed

to symmetrical and lepto to meso kurtic. The Gumara river

catchment area had streambed sediments of very coarse to

coarse sand that were poorly sorted, coarse skewed to

symmetrical, and lepto to platy kurtic. The Gilgel Abbay

river catchment in the southern part of the lake Tana basin

had bed-load sediments mainly composed of coarse sand,

poorly sorted, near symmetrical and leptokurtic. The interim

catchments of the Tana-West were basically covered by very

coarse to coarse sand that were poorly sorted, very coarse

skewed to symmetrical, and lepto to meso kurtic.

Table 1. Descriptive statistical analysis for the grain-size parameters (Logarithmic (ɸ) Folk and Ward, (1957) Graphical measures).

Catchment Area

Meansize (Mz)/ (ɸ) Sorting (σ) Skewness (Sk) Kurtosis (KG) D10 (ɸ) D50 (ɸ) D90 (ɸ) SPAN Index

MEGECH Minimum -0.090 0.788 -0.376 0.962 -2.209 0.119 1.349 1.574

Maximum 1.452 1.501 0.100 1.481 0.379 1.350 2.561 31.359

Average 0.565 1.297 -0.166 1.188 -1.268 0.699 2.025 10.880

Deviation 0.517 0.840 0.706 0.546 0.729 0.471 0.433 11.397

RIB Minimum 0.120 1.229 -0.345 1.018 -2.525 0.181 1.566 4.748

Maximum 0.553 1.983 -0.056 1.368 -1.490 1.094 2.670 17.639

Average 0.393 1.478 -0.139 1.174 -1.746 0.544 2.155 9.097

Deviation 0.162 0.295 0.116 0.141 0.439 0.334 0.412 4.951

GUMARA Minimum -0.616 1.267 -0.145 0.877 -2.230 -0.537 0.952 -5.924

Maximum 0.910 1.494 0.034 1.167 -0.855 0.924 2.564 15.905

Average 0.270 1.349 -0.057 1.020 -1.485 0.340 1.933 5.182

Deviation 0.554 0.087 0.069 0.115 0.511 0.546 0.631 7.783

GIGELABBAY Minimum 0.623 1.031 -0.024 1.171 -0.914 0.554 1.693 4.060

Maximum 0.720 1.081 0.029 1.364 -0.565 0.645 2.054 4.705

Average 0.682 1.062 0.008 1.267 -0.768 0.615 1.936 4.409

Deviation 0.036 0.019 0.026 0.075 0.127 0.037 0.158 0.283

TANA WEST Minimum -0.491 1.157 -0.349 1.008 -2.046 -0.418 0.960 -105.06

Maximum 0.909 1.589 0.033 1.470 -1.105 1.193 2.529 6.926

Average 0.251 1.345 -0.133 1.159 -1.731 0.379 1.849 -19.274

Deviation 0.532 0.183 0.142 0.182 0.380 0.622 0.593 48.268

Table 2. Percentage distribution of grain-size statistical parameters (in percentage of the total no. at each location of the river catchment).

MEGECH RIB GUMARA GIGEL ABBAY TANA WEST

Mean size (Mz)

MG 1.49 2.22 0.93 0.54 1.24

FG 4.91 5.96 5.40 2.65 5.89

VFG 8.03 8.73 12.97 5.27 12.78

VCS 16.87 17.23 21.91 15.31 19.88

CS 27.23 30.21 26.47 40.12 27.83

MS 29.14 22.41 21.58 26.74 22.07

FS 9.63 9.80 7.16 7.62 8.32

VFS 2.67 3.41 3.55 1.74 1.96

Sorting (σ) MoS 9.09 0.00 0.00 0.00 0.00

PS 90.90 100.00 100.00 100.00 100.00

Skewness (Sk) NSy 27.27 60.00 80.00 100.00 40.00

CSk 54.54 20.00 20.00 0.00 40.00


MEGECH RIB GUMARA GIGEL ABBAY TANA WEST

VCSk 0.00 20.00 0.00 0.00 20.00

Kurtosis (KG)

LKu 63.63 60.00 20.00 100.00 40.00

MKu 36.36 40.00 60.00 0.00 60.00

PKu 0.00 0.00 20.00 0.00 0.00

MG = Medium Gravel; FG= Fine Gravel; VFG= Very Fine Gravel; VCS= Very Coarse Sand; CS= Coarse Sand; MS= Medium Sand; FS= Fine Sand; VFS=

Very Fine Sand; MoS = Moderately Sorted; PS= Poorly Sorted; NSy= Near Symmetrical; CSk= Coarse Skewed; VCSk=Very Coarse Skewed; LKu=Lepto

Kurtic; MKu= Meso Kurtic; PKu= Platy Kurtic.

4.1. Graphical Representations of Grain-Size Trends of the River Catchments

Figure 5. Box-plots of measured statistical size parameters for bed-material load samples from each river catchment.

Box-plots show the inter-quartile range of observations of

statistical grain-size parameters in each river catchment area

(Figure 5). Generally, mean sizes of the bed-material load

sediments were noticeably coarse sand in each catchment.

However, a little variation in the mean sizes within the same

river catchment was observed. Overall, the Gumara tends to be

the coarsest river and the Gilgel Abbay the least coarse,

although these differences are small. Sorting for bed-load

samples from all catchments were, in general, poorly sorted.

Gilgel Abbay streambed sediments had the lowest average

value for sorting i.e., are more inclined towards moderately

sorted than the corresponding values of the samples from the

other sites. Asymmetry parameters also showed that the values

of the bed-load samples tend to be symmetric or slightly

skewed toward coarse-grained particles. Except the catchment

of the Gilgel Abbay that was symmetrical, the catchments of

all other rivers were covered by sediments having grain-sizes

skewed towards the coarse fraction (negatively skewed).

Kurtosis parameters showed that the Megech, Reb and Gilgel

Abbay rivers were lepto kurtic having the highest average

values than Gumara and Tana West rivers were meso kurtic.

Results of all the samples analysis indicated that the

catchments of lake Tana is mainly composed of coarse sand

(M=0.4ɸ) and size range of very coarse to medium sand (-0.1



to 1.25ɸ); poorly sorted (M=1.35ɸ) ranging from moderately

to poorly sorted sands (0.9 to 1.6ɸ); coarse skewed (negatively

skewed, M=-0.15ɸ) ranging from symmetry to slight coarse-

grained asymmetry (0.1 to -0.35ɸ); leptokurtic (M=1.2ɸ)

ranging from platy to very leptokurtic (0.7 to 1.5ɸ). The consistent differences between grain-size distributions

within and among the river catchments were found (Figure

6). The areas covered by sediments showing unimodal to

bimodal distributions had a noticeable fine to very fine-

gravelly tail (Figure 6 b). Most of the samples from the lake

basin area show about 30-45% of class weight especially

from coarse fraction (0-1ɸ) followed by medium sand (1-2 ɸ)

of 20-30% whereas the very coarse to fine gravel were about

5-15%. The Gumara and Gilgel Abbay river catchments had

unimodal distributions, whereas the catchments of other

rivers show different modes of distribution (unimodal and

bimodal). The bimodal frequency curves showing are of

distinctive grain-size distributions in the bed-loads of the

catchments. Among the relatively fine grained unimodal

deposits from many parts of the world, river sands are less

well sorted and usually are positive skewed [16]

Figure 6. A. Grain-size distribution of streambed samples obtained from each of the catchment; B. Grain-size distributions for all samples of the lake

catchments.

Figure 7 shows the ternary triangle diagrams representing

the gravel, sand and mud (silt/clay) content in bed-load

samples from the catchments. Overall, the results indicate

that the catchment areas of the lake are mostly composed of


gravelly sand with a few samples in sandy gravel texture.

Among all five catchment areas, the mud (silt/clay) content

in bed-load samples appears less than 5% having a

significant fine-grained tail. The textural group for the

sediments of Gumara river catchment varied from sandy

gravel to gravelly sand. The ternary plots also show that all

samples are noticeably homogeneous however with a little

variation in gravel, sand and mud content. The bed-load

samples of the catchment areas appeared diverse in color

mostly from blackish, grey, brownish, grayish brown to dusty

with composite mineral compositions (mainly composed of

tephra are intercalated with the fluvial deposits) that

correspond to the provinces of topographic massifs in the

area.

Figure 7. A Ternary triangle plots showing the distribution of gravel, sand and mud sized sediments; B. Ternary plots for all samples of the lake catchments.



The slope has significant effect on the velocity of

overland flow in the river catchments of the lake because of

high topography and very broken and hilly, with grassy

uplands, swamp valleys and sparse vegetation. Most of the

lake basin is under nearly level to very gentle slopes (0.5-2

to 2-5%) covering about 27%, gentle to moderately slopes

(5-9 to 9-15%) about 30%, strong to steep slopes (15-

100%) about 22%, and very steep slopes (>100%) covering

about 21% of the basin (Figure 8). Very gentle slopes are

observed in the downstream, plain or undulating plateau

except the Tana West whereas the above lower part of all of

the catchments has moderate slopes are in association with

lava flows and foot of mountains. Steep slopes are mostly

observed in the west, north, northwest and northeast of the

basin in association with mountain peaks which are of

volcanic origin and are characterized with high drainage

density, high stream frequency and high flood potentiality.

This may impact on hydrodynamics of the catchments

sediments to the basin. Much less stream frequency and low

drainage density are found on the very gentle slopes

covering the plains.

Figure 8. Percent Slope map of the Lake Tana Basin.

Sample grain-size cumulative distributions for each

catchment were plotted on semi-logarithmic scale (Figure

9), showing composites in spatial variations of grain-size

distribution curves. The distribution curves indicate that

these catchments are composed of composite sediments.

However, the distribution curves for the samples of Gilgel

Abbay catchment, follows the same trend and all bed-load

samples are considered to have unimodal distribution,

with noticeable coarse grained tail. Cumulative

distribution curves on a probability scale for all bed-load

samples of the catchments are shown in Figure 9b with

particular regard to their inclination and spread of the

grain-size. They appear on a probability scale as segments

(A, B, C) with different inclination and are distinguished

with the course of the cumulative curves for the prevailing

fractions of the deposit (>60% of the total sample mass)

and the sorting of these fractions, as marked by the

section’s inclination [17]. Cumulative curves for all

catchment samples comprise curves in which the major

section represents poorly sorted material, and it is

characterized by a steeply inclined section ‘A’ within the

75-85° interval indicating that admixtures of more coarser

and very less finer grains occur as well (segments C and

B). These cumulative curves are characteristic of fluvial

deposits (particularly in meandering and braided-rivers) of

largest grain-size derived from high-energy currents and

transported in the limit of upper competence of a given

density medium. According to the Visher’s (1969) [18]

classification system, the distinction of size population is

mostly traction and saltation populations indicate to those

channel sediments that are transported through rolling and

dragging, and by saltation.

Figure 10a shows the C-M diagram wherein the values of

the first percentile (C) are plotted against the median (M)

whichare obtained from the cumulative probability curves.

The C-M diagram has been widely used as a technique to

relate the grain-size characteristics of sediments to the

processes of their deposition and mode of transportation [19].

In addition, it provides an evaluation on the hydrodynamic

forces working during the deposition of the sediments. This

C-M diagram shows the two types of characteristic zones

according to Passega (1964) and Ludwikowska-Kedzia [20-

21] indicating different modes of transport of grains and

sedimentation. The C-M patterns results of both the zones

show that these deposits are covered by varying mode of

transport, ranging from transport exclusively through rolling

(N or NO) to saltation with a contribution of rolling (PQ).

The suspension is much less rapid than the saltation from

rolling indicated that sediments were transported as bed-load

through a mixed of both modes of rolling and saltation with a

contribution of rolling which are characteristic of flood plain.

Upward shifts of the basic limits towards higher ‘C’ values

indicate thatthe sediment deposits in river catchments of

various gradients and current dynamics. No samples were

identified to be transported as suspended materials on the

diagram. Some of the samples do not point explicitly to a

particular environment because of variability in depositional

conditions within settings as well as due to process of similar

hydrodynamic conditions in different environments. The bi-

variant scatter plots between one percentile (C) and median


(M) as shown Figure 10b.

Figure 9. A. Cumulative particle size distribution curves showing composite samples from the five catchment areas of the lake basin; B. Cumulative

probability distribution curves.



Figure 10. A. C-M plot; B. Bi-variant plot between C and M.

For discriminating the depositional environments and

understanding of the energy processes, bi-variant plots are

made between the grain-size parameters [22]. The bi-variant

plots between mean size and sorting, mean size and

skewness, and sorting and skewness are shown in Figure 11.

The bi-variant plots between mean size and sorting of

sediments of the Megech (Pearson’s correlation coefficient, r

=−0.494, Sig. (2-tailed) p=0.122), Reb (r=0.743, p=0.050),

Gumara (r=0.239, p=0.698), Gilgel Abbay (r=0.693,

p=0.195) and Tana West river catchments (r=0.741, p=0.170)

show both negative and positive correlations. The plot of

mean size against sorting for the Megech shows a negative

correlation and the coefficient of determination (r2) indicated

that the sorting decreases (progressively moderate to poorly

sorting) with the increasing grain-sizes, while other

catchments do not show any significant trends. In general,

these catchments are distinguished by having poorly sorted

sediments in the size range of coarse to very coarse fraction.

The bi-variant plots of mean size against skewness for

the Reb (r= −0.457, p=0.439) and Tana West (r= −0.780,

p=0.120) show a negative correlation and indicate that

skewness increases (from coarse skewed to symmetrical)

with the increasing grain-size, while the plots for the

Megech (r=0.165, p=0.628), Gumara (r=0.173, p=0.781),

Gilgel Abbay (r= 0.594, p= 0.291) do not show any

significant trends i.e., skewness increases (fine skewed)

with the decreasing grain-size. With this, the higher content

of coarse sediment fraction did not obviously affect

sediment sorting in the samples of all catchments except for

the Megech and did result in an increased degree of

skewness.

The bi-variant plots between sorting and skewness for the

Gumara (r= 0.611, p=0.273) and Gilgel Abbay (r= 0.116,

p= 0.852) show a positive correlation and indicate that the

skewness increases (i.e., fine skewed, +ve) with the

decreasing sorting (i.e., poorly sorted), while the plots for

Megech (r= −0.638, p=0.017), Reb (r=−0.917, p=0.014),

Tana West (r= −0.717, p=0.173) do not show any

significant trends i.e., slightly skewed towards the coarse

fractions (− ve skewed) with the decreasing sorting.

Plotting of skewness against kurtosis is a powerful tool for

interpreting the genesis of sediment, by quantifying the

degree of normality of its size distribution [23]. In general,

sediments from most of the catchments lie within coarse

skewed and meso to leptokurtic field (Table 2). The

coarsely skewed sediments did not obviously affect

sediment kurtosis in the samples.

The results of bi-variant plots between mean sizes vs.

sorting for all samples of the catchment of lake Tana show

a negative correlation (r=−0.047, p=0.802, r2=0.028) i.e.,

the sorting decreases with the increasing grain-size. The

higher content of coarse sediment fraction did obviously

affect sediment sorting in the samples. The mean sizes vs.

skewness results also show negative correlation

(r=−0.060, p=0.749, r2=0.002) i.e., the skewness increases

with the increasing grain-size. The higher content of

coarse sediment fraction did result in an increased degree

of skewness. Whereas, the bi-variant plots between sorting

vs. skewness and skewness vs. kurtosis do not show any

significant trends (i.e., indicating as a negative

correlation).


Figure 11. Bi-variant plots showing the relationship between the grain-size and sorting; grain-size and Skewness, and Sorting and Skewness of the sediments

from each catchment of the lake.



4.2. Statistical Characterization of

Streambed Sediments of the

Catchments

In an attempt to obtain a similarity between and within the

catchments, statistical treatments were applied to the

streambed samples from all of the catchment areas. One-way

ANOVA analysis, Liner Regression analysis and the Student

t-test analysis were used in the present work. To confirm the

statistical discrimination results of the bi-variant plot, CM

plot and probability accumulation distribution, as well as to

define the controlling variables of each catchment. The

analysises were performed using a data matrix comprising

variables of grain-size statistical parameters and 31 sample

location attributes of all five catchments.

The results of One-way ANOVA analysis (Table 3)

revealed that there was a significant differences among the

five river catchments on sediment textures, F (4, 26) = 0.027,

p <0.05, ɳ2

ρ =0.24. This testing also revealed significant

differences between pairs of regions with North and South

having more textural variations than East and West, i.e., the

Megech from north, (M=0.56, S.D= 0.52); Gilgel Abbay

from south, (M=0.68, S.D=0.036); Reb (M=0.30, S.D= 0.16)

and Gumara from east (M= 0.27 S.D=0.25); and Tana West,

(M=0.25 S.D=0.53). These findings indicate that there are

more grain-size variations in river catchments from north and

south regions of lake Tana basin. This is attributable to the

unlikeness in the physiographic setting.

Table 3. Results of One-Way ANOVA.

Source of variation Degree of freedom (df) Variance Ratio (F) Sig. (p) Remark at 5% LS*

Between catchments 4 &26 4.398 0.027 Significant

*LS= Level of Significance @0.05

The ANOVA results of Linear regression (Table 4) revealed that there was a statistically significant difference in streambeds

within the Megech and Gilgel Abbay catchments, while there was no statistically significant difference in streambeds of the

Reb, Gumara and Tana West catchments. This indicates that there may be variations in the conditions of sediment deposition.

Table 4. Results of ANOVA in Liner Regression analysis.

Source of variation Catchments Degree of freedom (df) Variance Ratio (F) Sig. Remark at 5% LS

Within catchments

Megech 1&9 3.382 0.039 Significant

Reb 1&3 0.009 0.929 Not Significant

Gumara 1&3 0.619 0.489 Not Significant

Gilgel Abbay 1&3 5.849 0.044 Significant

Tana West 1&3 2.234 0.232 Not Significant

The Student’s t-test analysis results revealed that there was a statistically significant difference in grain-size parameters of

streambed sediments of the Megech and Reb, and, Megech and Tana West catchments, while there was no statistically

significant difference in the streambed sediments of the Reb and Gumara, Gumara and Gilgel Abbay, and, Gilgel Abbay and

Tana West catchments.

Table 5. Results of Students’ t-test (clock-wise paired samples test).

Catchments t df Sig. Remark at 5% LS

Megech vs. Reb 2.244 10 0.047 Significant

Reb vs. Gumara 0.415 10 0.687 Not Significant

Gumara vs. Gilgel Abbay -1.569 10 0.148 Not Significant

Gilgel Abbay vs. Tana West 1.616 10 0.137 Not Significant

Tana West vs. Megech -2.739 10 0.021 Significant

5. Conclusions

Using the statistical grain-size parameters various

graphical plots are made for seeing the trends and

characterization of the river catchments that influence in turn

the character of the lake Tana basin. Multivariate statistical

techniques were also employed for discriminating intra- and

inter-catchment variations of grain sizes. The results indicate

statistically significant variation among different river

catchments, while there is no statistically significant variation

within a particular river catchment except for the Megech

and Gilgel Abbay. These variations may be due to the

variables like diversity in the sources of the sediments, the

hydrodynamic forces, lateral distance and the physiographic

setting of the study area. The results suggest that these

variables are most affecting the grain-size patterns and the

role of particular variable is differed between various river

catchments. It is also noted that variables controlling within

any given river catchment grain-size variability are strongly

site specific.

Acknowledgements

The authors would like to thank the fieldwork

collaboration efforts of team members. We are also grateful


to the Manager, Ethiopian High way Road and Construction

Material office, Gondar for providing the laboratory facilities

for sample analysis. Our appreciation is expressed to their

staff members for technical assistance. Finally, our sincere

thanks to Prof. R. P. Raju, Department of Environmental &

OHS, and University of Gondar for their unconditional

support.

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