Analysis of factors determining sediment yield variability in the highlands of northern Ethiopia

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Geomorphology 76

Analysis of factors determining sediment yield variability in the

highlands of northern Ethiopia

L. Tamene a,*, S.J. Park b, R. Dikau c, P.L.G. Vlek a

a Center for Development Research (ZEF), University of Bonn, Walter-Flex-Str.3, 53113, Bonn, Germanyb Department of Geography, College of Social Sciences, Seoul National University, Seoul 151-742, South Korea

c Department of Geography, University of Bonn, Meckenheimer Allee 166, 53115, Bonn, Germany

Received 11 April 2005; received in revised form 16 October 2005; accepted 20 October 2005

Available online 15 December 2005

Abstract

In many developing countries, sustainable land management and water resources development are threatened by soil erosion

and sediment-related problems. In response to such threats, there is an urgent need for improved catchment-based erosion control

and sediment management strategies. The design and implementation of such strategies require data on erosion rates and

understanding of the factors that control the delivery of sediment through the catchment system. In this study, reservoir

sedimentation and corresponding catchment attribute data were used to investigate the major factors controlling sediment yield

variability in a mountainous dryland region of northern Ethiopia. Sediment yield data were acquired for representative 11

catchments above reservoirs. Geomorphological and anthropogenic catchment attributes were extracted for each reservoir from

different sources including digital elevation models, satellite images and field surveys. Different statistical analyses such as

Pearson’s correlation, principal components and multiple regression were implemented to analyze the relationship between

sediment yield and catchment characteristics and to determine the major factors controlling the variability of sediment yield.

The results show that terrain form, gully erosion, surface lithology, and land cover explain most of the variability in sediment yield

among the catchments. The implications of the results, for relevant management intervention targeted at ameliorating the major

causative factors of erosion, are also outlined.

D 2005 Elsevier B.V. All rights reserved.

Keywords: Reservoir survey; Catchment attributes; Statistical analysis; Sediment yield variability; Northern Ethiopia

1. Introduction

Agricultural productivity in Ethiopia is highly influ-

enced by erratic and unpredictable rainfall, which led to

recurrent droughts claiming thousands of human and

livestock lives (e.g., Degefu, 1987; Hurni, 1993). To

mitigate such crisis, it is necessary to minimize the

deleterious impact of rainfall variability through the

provision of adequate water supply and its proper uti-

0169-555X/$ - see front matter D 2005 Elsevier B.V. All rights reserved.

doi:10.1016/j.geomorph.2005.10.007

* Corresponding author. Fax: +49 228 731889.

E-mail address: [email protected] (L. Tamene).

lization (Lawrence et al., 2004). Therefore, the govern-

ment of Ethiopia, in collaboration with international

organizations, launched a massive surface-water har-

vesting scheme through the construction of micro-

dams. For instance, from 1996 to 2001, over 50

micro-dams were built in the Tigray administrative

region of northern Ethiopia.

The construction of the micro-dams resulted in var-

ious economical, hydrological, and ecological benefits

including increase in food production, easy access to

drinking water for people and livestock, rise in the

ground water level and issuance of new springs (e.g.,

(2006) 76–91

L. Tamene et al. / Geomorphology 76 (2006) 76–91 77

Teshalle, 2001; Woldearegay, 2001; Behailu, 2002).

However, such benefits are threatened by the rapid

loss of the water storage capacity of the dams mainly

due to siltation (e.g., Gebre-Hawariat and Haile, 1999).

The rapid siltation of the reservoirs has resulted in the

loss of their intended services and a large amount of

money spent on their construction. Accelerated siltation

is also responsible for the storage capacity loss of

hydro-electric power dams resulting in frequent power

cuts and rationing-based electric power distribution

(Ayalew, 2002). At the same time, the on-site effect

of erosion, which results in the loss of nutrient-rich top

soil and hence reduced crop yields, is chronic in the

country (e.g., FAO, 1986; Hurni, 1993; Sonneveld and

Keyzer, 2003).

To tackle the on- and off-site threats of erosion, there

is an urgent need for improved catchment-based erosion

control and sediment management strategies (Millward

and Mersey, 2001; Walling et al., 2001). To imple-

ment such strategies, it is necessary to understand the

magnitude of the problem and factors responsible for

it (Mitas and Mitasova, 1998). Knowledge of cause–

effect relationships and their spatial patterns are also

essential to plan water-harvesting schemes and to de-

sign necessary management precautions (Lawrence et

al., 2004).

Although catchment erosion is believed to be re-

sponsible for the rapid loss of storage capacity of the

reservoirs in Ethiopia, there are no detailed studies on

the quantification of erosion rates or on the spatial

dynamics of erosion/siltation processes on a catchment

scale (Gebre-Mariam, 1998). Some of the erosion stud-

ies are based on erosion plots (e.g., SCRP, 2000), which

are difficult to extrapolate to a basin scale. Moreover,

sediment yield measurements conducted at some gaug-

ing stations are not correlated with catchment attributes

for evaluating factors controlling sediment yield vari-

ability. Consequently, there is a need to determine the

effects of catchment attributes on sediment yield to

reservoirs.

Sediment deposition in reservoirs is a reflection of

catchment erosion and deposition processes, which are

controlled by terrain form, soil, surface cover, drainage

networks, and rainfall-related environmental attributes

(Renard and Foster, 1983; Walling, 1994). Integrated

analysis of reservoir sediment yield data with respective

environmental attributes of catchments could facilitate

understanding of the dominant factors governing sedi-

ment yield variability and identify cause–effect rela-

tionships at the catchment scale (Hadley et al., 1985;

Dearing and Foster, 1993; Verstraeten and Poesen,

2001a; Phippen and Wohl, 2003). The relationships

between sediment yield and basin attributes could

also serve as a means of predicting sediment yield of

similar catchments in similar environments (Phippen

and Wohl, 2003).

This study evaluates the major determinant factors of

sediment yields in a mountainous dryland environment

of Northern Ethiopia. Information related to the amount

and rate of sediment deposition in reservoirs was ac-

quired for 11 sites. Corresponding catchment attributes

data derived from different sources were then integrated

with the sediment yield data to evaluate the determinant

factors of sediment yield variability. Understanding the

significance of the different factors in accelerating ero-

sion and sedimentation enabled prioritization of possi-

ble areas of management intervention, targeted to

addressing the major causative factors.

2. Study areas and site characteristics

The study sites are located in the Tigray region,

which lies roughly within 12800V–15800VN and

36830V–41830VE (Fig. 1). The region has an approxi-

mate area of 80,000 km2 and a total population of 3.6

million with an average population growth rate of 2.9%

(Central Statistical Authority, 1998). Most people in the

region lead agrarian life and agricultural crop produc-

tion there has the oldest history in the country (De

Contenson, 1981). Environmental deterioration has

caused a decline in crop production and a population

increase resulted in a shortage of land. These processes

in turn led to expansion of agricultural and grazing

activities into marginal and steep slopes, which accel-

erated environmental degradation (Gebre-Egziabher,

1989).

The topography of the region is very rugged and

sensitive to erosion, and also difficult for effective

utilization and management. Vegetation cover is poor,

and rills and gullies are prominent features (e.g., Fig.

2). As a result, the region is considered one of the most

degraded and degrading regions in Ethiopia (Eweg and

Lammeren, 1996). To reduce soil loss, widespread

conservation efforts, mainly stone-terrace construc-

tions, are being undertaken in many parts of the region.

However, most of the terraces are broken, largely due to

livestock trampling or runoff, reducing their effective-

ness and even allowing concentrated flow, which un-

expectedly enhances the development of rills and

gullies downstream.

Rainfall in the study area is highly variable both

spatially and temporally. Mean annual rainfall generally

ranges between 200 mm in the lowlands and 800 mm in

the highlands. The coefficient of variation in annual

Fig. 1. Location of Tigray and 11 studied catchments.

L. Tamene et al. / Geomorphology 76 (2006) 76–9178

rainfall for the region is about 28%, much larger than

the national figure of 8% (Belay, 1996). The average

temperature is 18 8C, but varies greatly with altitude

and season.

According to Mohr (1963), the geology of the region

is strongly folded and foliated Precambrian rocks. The

main rock types are greenstones of basic volcanic ori-

gin, but slates, phyllites and granites are also common.

The dominant soil types in the region reflect topog-

raphy. Deeply weathered residual soils are mostly found

on flat plateaux, while rocky or very shallow soils are

dominantly located on steep slopes. Finer textured soils

are more concentrated on moderately undulating slopes,

and deep alluvial soils are found on terrace surfaces and

lower parts of alluvial fans (FAO, 1998).

3. Materials and methods

For this study, the catchments of 11 reservoirs were

selected (Fig. 1). The sites are representatives of

catchments in the study area in terms of size, terrain

form, land use/cover (LUC), lithology and severity of

gully erosion. Details of the site selection are given in

Tamene (2005). Three major approaches were used to

analyze factors controlling sediment yield: (1) sedi-

ment yield estimation, (2) collecting environmental

attributes of catchments, and (3) statistical analysis

of the relationship between catchment attributes and

sediment yield. Descriptions of the key steps are given

below.

3.1. Estimating sediment deposition in reservoirs

Quantitative reservoir sediment deposition data were

acquired for the 11 sites based on two approaches. For

reservoirs that were dry during the fieldwork, pit-based

surveys were conducted, and for those that were full of

water, bathymetric surveys were performed. The details

of the approaches are given in Tamene (2005) and their

brief summary is outlined below.

Fig. 2. Examples of gully erosion in Tigray, northern Ethiopia; (a) gully erosion on a floodplain (about 7–8 m deep and 5–6 m wide), (b) gully

erosion of on a floodplain (6 m deep and 3 m wide), (c) gully bank collapse (about 5–7 m deep and 4–5 m wide), (d) livestock disturbance of gully

banks and floors (2 m deep and 8 m wide). These processes were typically observed in the catchments of the three reservoirs with the highest

sediment yield (see Table 4).

L. Tamene et al. / Geomorphology 76 (2006) 76–91 79

In the pit-based method, spatially distributed pits

were opened on the reservoir floor. The number of

pits for each reservoir varied between 11 and 28

(Table 1) depending on the size of the reservoirs

and the pattern of sediment deposition. Then the

thickness of sediment was measured for each pit.

Table 1

Basic reservoir characteristics used to estimate sediment yield in Tigray, no

Site no. Site name RA (ha) C (106 m3) NPL (m

1 Adiakor 1.4 0.51 n.a.

2 Adikenafiz 12.4 0.67 650,00

3 Gerebmihiz 15.4 1.30 719,27

4 Gerebsegen 1.7 0.34 n.a.

5 Gindae 7.6 0.79 530,00

6 Grashito 8.6 0.17 142,20

7 Korir 18.1 2.00 1,980,0

8 Laelaywukro 7.1 0.85 601,67

9 Maidelle 16.6 1.58 1,583,1

10 Majae 1.0 0.30 n.a.

11 Teghane 19.6 1.80 898,19

RA=reservoir area; C =gross reservoir capacity; NPL=normal pool level (l

Y=age of reservoir, TE=trap efficiency; PO=the number of points surv

approaches and about 15% SSY variability was observed between the twoa Pit-based approach.b Bathymetric-based approach.

Next, Thiessen polygon interpolation was used to

estimate the barea of influenceQ of each pit (Goo-

vaerts, 2000). The volume (m3) of sediment trapped

in each polygon was calculated by multiplying sed-

iment thickness/pit depth (m) by the area of each

polygon (m2). The volume in each polygon was then

rthern Ethiopia

3) A (km2) Y (Years) TE (%) PO (Count)

2.8 5 97 11a

0 14.0 6 91 1169b

8 19.5 6 93 910b

4.0 3 95 15a

0 12.8 5 93 15a

0 5.6 5 86 21a (642)b

00 18.6 8 96 763b

8 10.0 6 95 782b

00 8.7 5 97 28a (857)b

2.8 5 96 17a

0 7.0 7 97 1294b

ive storage capacity, with n.a. data not available); A=catchment area;

eyed. Grashito and Maidelle reservoirs were surveyed using both

techniques (Tamene, 2005).

L. Tamene et al. / Geomorphology 76 (2006) 76–9180

summed to estimate the total volume of sediment

trapped in each reservoir.

In the bathymetric survey, the original reservoir ca-

pacity was compared with the current capacity. Infor-

mation on the original capacities of the reservoirs was

acquired from the Tigray Bureau of Water Resources. To

derive existing storage capacity, the depths from the

water surface to the top of the sediment were measured

at more than 600 locations for each reservoir (Table 1)

using a small motorboat with a depth counter and a

global positioning system (GPS). These data were

used to create bathymetric maps and derive existing

storage capacity of reservoirs using Surfer Software.

The original and current storage capacities of each

reservoir were then compared and their differences

gave the volume of sediment deposition (m3).

The measured sediment volume (m3) was converted

to the sediment mass (ton) using representative dry-

bulk density (e.g., Butcher et al., 1993; Verstraeten and

Poesen, 2001b) acquired from the dry-bed reservoirs. In

addition, the weight of deposited sediment was adjusted

for the reservoir trap efficiency (TE; %) calculated

based on Brown (1943) and Verstraeten and Poesen

(2000), which is given as:

TE ¼ 100 1� 1

1þ 0:0021D CA

!ð1Þ

where C is reservoir storage capacity (m3), A is catch-

ment area (km2), and D is a constant ranging from 0.046

to 1 with a mean value of 0.1. Considering the difficulty

of objectively defining a value of D and the scarcity of

data to employ more process-based models, a mean D

value of 0.1 was operationally used in this study.

Finally, total annual sediment yield (SY; t year�1)

and area-specific sediment yield/load (SSY; t km�2

year�1) of reservoirs were calculated based on:

SYp ¼ 100

XPdiATi

� �4dBD

TE4Yð2Þ

SYb ¼ 100SV4dBD

TE4Yð3Þ

SSY ¼ SY

Að4Þ

where SYp and SYb are pit- and bathymetry-based

sediment yields (t year�1), respectively; Pdi is depth

of each pit (m); ATi is area of a Thiessen polygon

around each pit (m2); dBD is dry-bulk density (t

m�3); Y is age of a reservoir (years); and SV is sedi-

ment volume calculated from differences in the storage

capacity of reservoirs (m3).

3.2. Catchment properties

In order to assess factors affecting sediment yield to

the reservoirs, key catchment attributes related to ero-

sion were collected. Contours, streams and spot heights

shown in 1:50,000 topographic maps acquired from the

Ethiopian Mapping Authority (Ethiopian Mapping Au-

thority, 1996) were digitized, and the TOPOGRID tool

of ARC/INFO (Hutchinson, 1989) was used to con-

struct digital elevation models (DEMs) of 10-m cell

size. Major topographic attributes such as the slope,

height difference, relief ratio, and hypsometric integral

were then derived from the DEMs based on their

definitions described in Table 2.

Land use and cover (LUC) maps of the catchments

were derived from 15-m resolution ASTER satellite

images (near infrared bands) acquired in November/

December 2001. Field surveys supported by topographic

maps and GPS as well as aerial photographs were used to

collect training areas for the LUC classification. Then the

maximum likelihood supervised classification algorithm

of IDRISI was employed to derive LUC maps and they

were resampled to a resolution of 10 m to conform to the

cell sizes of the DEMs.

If detailed soil data are unavailable, surface lithology

could serve as a proxy to estimate erodibility potential

(e.g., Evans, 1997; Fargas et al., 1997; Bull et al.,

2003). Shale, marl and limestone are considered to be

more sensitive to erosion than sandstone or metavolca-

nics (e.g., Fargas et al., 1997). Geomorphological and

geological maps (Coltorti et al., undated; Russo et al.,

1996) were digitized to estimate the proportion of such

erodible lithology for each catchment.

Besides the above basic environmental variables of

the catchments, data on gullies, which are prominent

features in the region (e.g., Fig. 2) and substantially

affect erosion/siltation processes, were also collected.

Since it was difficult to acquire quantitative information

on gullies, expert-based rankings were used to drive

semi-qualitative data related to the severity of gully

erosion and bank collapse. Three experts (the first

author and two senior hydrogeology students of

Mekelle University, Ethiopia) individually assessed

the status of gully erosion along a transect in the

field. The number of transects was two to four for

each catchment depending on the size and shape of

catchments. During the survey, information related to

the distribution of gullies and their status such as

dimensions, disturbance by livestock, extent of cultiva-

tion and the degree of bank collapse were collected.

Each expert then assigned a rank of gullying to each

catchment. In order to derive discrete values for statis-

Table 2

Major terrain characteristics and their relation to erosion processes

Terrain attributes Definition/description Example reference

Catchment area (A) Proxy to discharge Kirkby, 1988

Height difference (HD) Runoff potential, erosive power (HD=maxE�minE) Verstraeten and Poesen, 2001a

Slope Flow velocity and momentum of runoff Zevenbergen and Thorne, 1987; Moore et al., 1991

Hypsometric integral (HI) Distribution of elevation within catchment

HI ¼ meanE �minE

maxE �minE

� � Willgoose and Hancock, 1998; Awasthi et al., 2002;

Bishop et al., 2002

Relief ratio (RR) Intensity of erosion process

RR ¼ maxE �minE

L

� � Strahler, 1964; Verstraeten and Poesen, 2001a

Ruggedness number (RG) Index of basin ruggedness

RG ¼ HD � A�0:5� � Church and Mark, 1980

Main stream slope (SS) Rate of change of elevation with respect to distance

Proxy to surface runoff velocity

SS ¼ HD

DL

� �Gordon et al., 1992

Drainage length (DL) Sediment transport potential Verstraeten and Poesen, 2001a

(DL=sum of length of all stream/gully length)

Drainage density (DD) Balance between erosive forces and surface resistance,

degree of dissection of terrain

DD ¼ DL

A

� �Berger and Entekhabi, 2001; Sarangi et al., 2003;

Tucker et al., 2001

Catchment shape indeces:

circularity index (CI)

and elongation ratio (ER)

Speed of sediment delivery, deposition potential

CI ¼ A

ACp; ER ¼ Dc

L

� � Gordon et al., 1992; Sarangi et al., 2003

A=basin planimetric area (m2 ); meanE, maxE, minE =mean, maximum, and minimum elevations of catchment (m); L=horizontal distance

between the outlet of a catchment and the most remote point on the water divide (m); HD=Height difference or basin relief (m), DL=drainage

length (m); ACp=area of a circle having a perimeter equal to the perimeter of a catchment (m2 ); Dc=diameter of a circle with the same area as the

catchment area (m), L=maximum length of a watershed (m).

L. Tamene et al. / Geomorphology 76 (2006) 76–91 81

tical analysis, the gully-erosion and bank collapse-re-

lated ranks of each expert were standardized (Voogd,

1983; Eastman, 2001):

SBCR ¼Xni

Rai � n

N � n

� �=n4100 ð5Þ

where SBCR is standardized bank collapse and gully

erosion; Rai is the rank given to each catchment by

each expert, N is the maximum possible rank (=11), n

is the minimum possible rank (=1), and i is the expert

number (1–3).

Finally, mean annual rainfall for each catchment was

acquired from data for the closest rainfall station. As the

density of rainfall stations was not high, some of the

catchments share the same amount of rainfall at a station.

Table 3 shows the major catchment attributes collected

for the studied catchments using the above approaches.

3.3. Statistical analyses

Different statistical analyses including Pearson’s cor-

relation, principal component analysis (PCA) and mul-

tiple regression were performed to assess the relationship

between the sediment yield to reservoirs and the catch-

ment characteristics. Correlation analysis was used to

evaluate the role of individual catchment environmental

variables. Terrain attributes are in most cases correlated,

which could reduce the explaining power of some fac-

tors. PCAwas applied to reduce the dimensionality of the

data into a few uncorrelated components (McGarigal et

al., 2000) and to assess the role of different factors in

determining sediment yield. In addition, multiple regres-

sion was run to evaluate the cause�effect relationshipsbetween a combination of catchment attributes and

corresponding SSY. It was also used to establish a pre-

dictive model of SSY for similar environments.

4. Results

4.1. Sediment yield to reservoirs

Table 4 shows that SSY to the studied reservoirs

ranges between 345 and 4935 t km�2 year�1 with a

mean of 1900 t km�2 year�1. SY ranges from 1427 to

76320 t year�1 with a mean of ca. 20000 t year�1. The

Table 3

Basic morphometric properties of the studied catchments

Catchment A MEE HD RR HI MES SS RG DL DD RF

Adiakor 2.9 2394 81 0.03 0.53 2.9 17.9 48.5 6.6 2.4 568

Adikenafiz 14.0 2378 665 0.09 0.39 10.7 77.2 177.0 34.3 2.4 668

Gerebmihiz 19.5 2254 659 0.07 0.23 7.9 72.4 149.0 46.0 2.4 668

Gerebsegen 4.0 2125 122 0.02 0.37 1.6 19.2 61.0 7.9 2.0 550

Gindae 12.8 2144 488 0.10 0.40 8.4 82.6 136.2 15.4 1.2 690

Grashito 5.6 2118 294 0.05 0.44 5.6 34.4 125.5 10.8 1.9 658

Korir 18.6 2233 607 0.08 0.38 11.0 104.4 140.7 36.1 1.9 685

Laelaywukro 9.9 2240 552 0.08 0.43 16.4 79.0 175.2 37.1 3.7 677

Maidelle 10.3 2177 527 0.05 0.34 5.3 50.0 164.3 4.8 0.5 658

Majae 2.8 2138 171 0.04 0.48 5.4 45.3 101.4 3.3 1.2 634

Teghane 7.0 2791 178 0.03 0.40 8.8 30.8 66.9 10.9 1.5 559

Site name SBCR ER CI EL DENSE CULT NGL BUSH TBARE

Adiakor 16.7 1.9 0.81 81 2.7 72 9 13 81

Adikenafiz 93.0 4.2 0.33 93 9.0 60 13 17 73

Gerebmihiz 96.7 5.0 0.68 90 7.5 61 14 16 75

Gerebsegen 40.0 2.3 0.53 98 0.5 81 5 11 86

Gindae 70.0 4.0 0.88 88 9.5 39 15 33 54

Grashito 80.0 2.7 0.66 97 1.2 85 9 4 94

Korir 60.0 4.9 0.80 41 18.5 38 9 31 47

Laelaywukro 36.7 3.6 0.79 25 15.5 33 13 37 46

Maidelle 43.0 3.6 0.53 95 2.5 77 11 8 88

Majae 10.0 1.9 0.79 83 0.5 66 17 8 83

Teghane 3.0 3.0 0.35 20 10.5 46 12 17 58

A=catchment area (km2 ); MEE=mean elevation (m); HD=height difference (m); RR=relief ratio; HI=hypsometric integral; MES=mean slope

(degrees), SS=main stream slope (m km�1 ); RG=surface ruggedness; DL=drainage length (km), DD=drainage density (km/km2 ); RF=mean

annual precipitation (mm); SBCR=standardize bank collapse and gully erosion; ER=elongation ratio, CI=circularity index; EL=proportion of

erodible lithology (%); DENSE=proportion of dense vegetation cover (%); CULT=proportion of cultivated land (%); NGL=proportion of open

grazing land (%); BUSH=proportion of bush/shrub cover (%); TBARE=proportion of total bare land (%).

L. Tamene et al. / Geomorphology 76 (2006) 76–9182

mean SSY of 1900 t km�2 year�1 is somewhat higher

than the global and African averages, about 1500 and

1000 t km�2 year�1, respectively (Lawrence and Dick-

inson, 1995).

The tolerable soil loss, or the maximum rate of soil

erosion that permits economically sustainable crop pro-

ductivity (Renard et al., 1997), is from 2 to 18 t ha�1

Table 4

Sediment yields to the studied reservoirs based on reservoir sedimen-

tation surveys

Site name SY (t year�1) SSY (t km�2 year�1)

Adiakor 1427 510

Adikenafiz 69095 4935

Gerebmihiz 76320 3914

Gerebsegen 4681 1170

Gindae 25022 1955

Grashito 16909 3019

Korir 19684 1058

Laelaywukro 6494 649

Maidelle 20569 2364

Majae 1908 682

Teghane 2412 345

SY=annual sediment yield; SSY=area-specific annual sediment

yield.

year�1 for Ethiopia (Hurni, 1985). Six SSY values in

Table 4 are within this range, and five values are higher.

The SSYvalues in Table 4 are also often higher than the

Ethiopian soil generation rate of 3–7 t ha�1 year�1

(Hurni, 1983). Because the values of SSY are highly

variable (345–4935 t km�2 year�1; Table 4), a loga-

rithmic transformation was applied to the SSY data

before the following analyses.

4.2. Pearson’s coefficient of correlation

The correlation between sediment yield and catch-

ment attributes shows that the height difference (HD),

surface ruggedness (RG), surface lithology (EL) and

gully erosion/bank collapse (SBCR) show significant

correlations (a b0.05) with logSSY (Table 5; Figs. 3a

and 4c). In contrast, none of the LUC-related factors

are significantly correlated with logSSY, although the

trends of the correlations are acceptable (e.g., propor-

tion of bush/shrub cover correlates negatively with

logSSY but cultivated/bare land correlates positively;

Table 5, Figs. 3b and 4d). Mean slope also shows a

very poor correlation with the sediment yield.

Table 5

Correlation between catchment attributes and SY or logSSY for the 11 reservoirs

A MEE HD RR HI MES SS RG DL DD RF SBCR ER CI EL

A 1.00

MEE �0.03 1.00

HD 0.91** �0.18 1.00

RR 0.76* �0.22 0.85** 1.00

HI �0.73* 0.07 �0.62* �0.28 1.00

MES 0.55 0.19 0.67* 0.71* �0.13 1.00

SS 0.85** �0.19 0.88** 0.91** �0.38 0.76* 1.00

RG 0.68* �0.32 0.92** 0.81** �0.44 0.68* 0.78** 1.00

DL 0.84** 0.03 0.80** 0.68* �0.54 0.72* 0.76** 0.62* 1.00

DD 0.13 0.10 0.20 0.23 0.09 0.55 0.20 0.17 0.63* 1.00

RF 0.68* �0.46 0.84** 0.88** �0.30 0.61* 0.86** 0.90** 0.57 0.06 1.00

SBCR 0.69* �0.38 0.73* 0.66* �0.58 0.21 0.53 0.64* 0.62* 0.17 0.65* 1.00

ER 0.99** �0.01 0.93** 0.78* �0.73* 0.60* 0.86** 0.72* 0.82** 0.13 0.70* 0.69* 1.00

CI 0.02 �0.54 �0.01 0.25 0.29 0.07 0.28 0.01 0.05 0.12 0.36 �0.04 �0.03 1.00

EL 0.24 �0.40 0.27 0.17 �0.47 �0.36 �0.01 0.22 0.16 �0.08 0.05 0.75* 0.22 �0.26 1.00

DENSE 0.68* 0.29 0.60* 0.65* �0.20 0.86** 0.79** 0.45 0.72* 0.43 0.46 0.16 0.70* 0.12 �0.43CULT �0.49 �0.31 �0.42 �0.62* 0.03 �0.82** �0.70* �0.31 �0.54 �0.34 �0.40 0.05 �0.51 �0.28 0.53

NRG 0.22 0.05 0.32 0.47 �0.04 0.41 0.41 0.41 0.19 �0.10 0.50 0.04 0.22 0.20 �0.09BUSH 0.52 0.06 0.51 0.70* �0.08 0.79** 0.74* 0.40 0.60* 0.48 0.45 0.12 0.55 0.38 �0.37BARE �0.48 �0.32 �0.39 �0.56 0.03 �0.79** �0.67* �0.25 �0.53 �0.38 �0.33 0.06 �0.50 �0.26 0.55

SY 0.75* �0.05 0.74** 0.57 �0.69* 0.23 0.49 0.59 0.67* 0.15 0.50 0.84** 0.72* �0.27 0.72*

logSSY 0.52 �0.45 0.64* 0.50 �0.57 0.01 0.35 0.63* 0.36 �0.09 0.57 0.92** 0.53 �0.19 0.68*

DENSE CULT NGL BUSH TBARE SY logSSY

DENSE 1.00

CULT �0.90** 1.00

NGL 0.16 �0.42 1.00

BUSH 0.87** �0.92** 0.24 1.00

TBARE �0.93** 0.98** �0.25 �0.94** 1.00

SY 0.17 �0.02 0.40 0.03 0.03 1.00

logSSY �0.13 0.31 0.16 �0.19 0.34 0.81** 1.00

Symbols are explained in Table 3. ** Significant at 0.01; * significant at 0.05.

L. Tamene et al. / Geomorphology 76 (2006) 76–91 83

4.3. Principal component analysis

Principal component analysis (PCA) applied to

the 20 catchment attributes shows that the first

(a) (

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

SBCR

logSSY

(t

km

-2 y

-1)

-2-1

0 4020 60 80 100 120

Fig. 3. Correlation between logSSY and some catchment attributes. The

significantly correlated with logSSY while the LUC-related factors (e.g., C

four components account for about 88% of the

variability in the dataset (Table 6). The eigenvalues

associated with the first two principal component

(PC) axes contain about 71% of the variance in

b)

logSSY

(t

km

y)

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

0 10 20 30 40

BUSH (%)

terrain-and gully erosion-related factors (e.g., HD and SBCR) are

ULT and DENSE) are not.

Fig. 4. Scatter plot showing the relationships between (a) PC1 and logSSY, (2) PC2 and logSSY, (c) HD and logSSY, (d) DENSE and logSSY. The

distribution of points in (a–b) shows that catchments with similar attributes tend to have similar SSY compared to the others and therefore tend to

dgroupT together (see c and d for comparison). The catchment numbers correspond to those in Table 1.

L. Tamene et al. / Geomorphology 76 (2006) 76–9184

the dataset, while the other two contain about

17%.

Table 6 shows that the set of variables with high

loadings of PC1 are mainly composed of some prop-

erties related to terrain relief, elongation ratio, catch-

ment area, gully erosion, and drainage length,

explaining about 50% of the variability in the dataset.

Since catchment area, elongation ratio and drainage

length are significantly correlated with most of the

variables related to terrain relief (Table 5), the major

elements of the PC1 can be considered as terrain- and

gully-erosion-related factors. On the other hand, the

set of variables in PC2 (which explain about 20% of

the variability) are mainly composed of LUC-, erod-

ible lithology- and mean slope-related attributes. Mean

slope is significantly and positively correlated with

bush/dense cover and negatively correlated with cul-

tivated/bare land and lithologic erodibility (Table 5).

As a result, PC2 can be considered as an LUC- and

lithology-related axis. The last two PCs, however, do

not contain clearly identifiable logical dimensions,

implying that the PCs have little merit for further

analysis (Burley et al., 1996). Step-wise regression

between the four PCs and logSSY using the probabil-

ity F for entry (0.05) and removal (0.1) also selected

only the first two components (R2=0.92, a b0.001).As a result, only the first two PCs are used in ana-

lyzing the correlation between PCs and SSY. The

model that best explains the variability in sediment

yield from the two PCs is:

logSSY ¼ 0:314PC1 � 0:191PC2 þ 1:129

R2 ¼ 0:92; n ¼ 11; ab0:005 ð6Þ

where PC1 and PC2 are factor loadings of the signif-

icant principal components.

To visually evaluate the patterns of variables in the

PC-logSSY spaces and assess the possible reasons for

the resulting arrangements, the two significant princi-

pal components were plotted against logSSY (Fig.

4a,b). These figures were then compared with plots

of selected factors of PC1 and PC2 against logSSY

(Fig. 4c,d).

Table 6

Eigenvalues, percentages of variation (%Var) explained and eigenvectors of variables for each component evaluated by PCA

Parameter PCA Variable Component

PC1 PC2 PC3 PC4

Eigenvalue 10.3 4.0 2.0 1.3

%Var 51 20 10 7

Eigenvector Height difference (HD) 0.928 0.270 0.076 0.164

Elongation ratio (ER) 0.903 0.350 �0.080 0.046

Catchment area (A) 0.896 0.323 �0.059 0.032

Standardized gully erosion (SBCR) 0.889 �0.213 0.170 �0.103Surface ruggedness (RG) 0.805 0.181 0.214 0.308

Hypsometric integral (HI) �0.780 0.128 0.345 0.086

Drainage length (DL) 0.775 0.484 0.008 �0.260Mean annual precipitation (RF) 0.729 0.213 0.484 0.370

Main stream slope (SS) 0.725 0.544 0.277 0.229

Relief ratio (RR) 0.724 0.434 0.351 0.292

Proportion of total bare land (TBARE) �0.171 �0.957 �0.034 �0.045Proportion of cultivable land (CULT) �0.186 �0.944 �0.057 �0.183Proportion of dense cover (DENSE) 0.401 0.891 �0.039 �0.050Proportion of erodible lithology (EL) 0.197 �0.889 0.264 0.069

Proportion of bush/shrub cover (BUSH) 0.259 0.870 0.274 �0.030Mean slope (MES) 0.431 0.815 0.048 0.122

Circularity index (CI) �0.117 0.221 0.864 �0.022Mean elevation (MEE) �0.252 0.424 �0.800 0.033

Proportion of open grazing land (NGL) 0.233 0.239 0.065 0.767

Drainage density (DD) 0.141 0.469 0.163 �0.623The first four PCs explain about 88% of the variability in the dataset. Bold values indicate highest eigenvector score for a given variable of the first

two significant components.

L. Tamene et al. / Geomorphology 76 (2006) 76–91 85

4.4. Multiple regression

Before applying multiple regression, it was neces-

sary to keep the number of explanatory variables (20)

lower than the number of cases (11) to avoid the

problem of inflated R2 (Phippen and Wohl, 2003). It

was also necessary to minimize the effect of autocorre-

lation of environmental factors on multiple regression

analysis (e.g., Phippen and Wohl, 2003). The PCA

made it possible to reduce the dimensionality of the

data and to address the problem of multicollinearity.

From the two significant PCs (Table 6), catchment

attributes with a high eigenvector value were selected,

and the actual values of the factors were used for

regression. The applied Model 1 in Table 7 shows

that over 80% of the variability in sediment yield can

be explained by the height difference (HD) and the

cultivated land percentage (CULT). A similar level of

significance was achieved (over 80%) when only HD

and dense cover percentage (DENSE) were used

(Model 2). Inclusion of factors from PC3 and PC4

into the regression analysis resulted in very minor con-

tributions of about 1%, confirming the insignificance of

these PCs. The Model 3 uses the gully/bank-erosion-

related factor (SBCR) for PC1 instead of HD, and the

variability explained has increased to over 90% (Table

7). A similar level of significance is achieved when

SBCR and DENSE are used in the regression (not

shown). Again, the contribution of the parameters

representing PC3 and PC4 to the overall regression

was very minor (less than 3%).

4.5. Step-wise regression

In order to further explore the role of factors affect-

ing the variation in observed sediment yield, a step-

wise regression was run using 18 out of the 20 variables

shown in Table 3, while the multicollinearity between

independent variables was kept to the minimum. Two

factors, mean elevation and the circularity ratio, were

not used because they are negatively correlated with

most of the terrain attributes which are positively and

significantly correlated with logSSY. The best predic-

tive equation to estimate SSY to reservoirs is:

logSSY ¼ 0:007SBCRþ 0:003ELþ 0:002RG

� 0:007BUSHþ 2:33

R2 ¼ 0:96; a b 0:05 ð7Þ

Eq. (7) shows that terrain-, lithology-, gully- and LUC-

related factors explain most of the variation in SSY,

Table 7

Multiple regression coefficients estimated by each unstandardized statistical model for the 11 reservoirs

Model Independent variable b estimate Standard error P-value Pearson’s r

Model 1 (R2=0.83, P=0.002) Height difference 0.0016 0.000 0.000 0.64*

Cultivated land 0.015 0.003 0.002 0.31

Constant 1.59

Model 2 (R2=0.83, P=0.002) Height difference 0.0019 0.000 0.000 0.64*

Dense cover �0.015 0.003 0.002 0.31

Constant 2.731

Model 3 (R2=0.92, P b0.001) Gully/bank erosion 0.011 0.001 0.000 0.92**

Cultivated land 0.0056 0.002 0.03 0.31

Constant 0.26

**Significant at 0.01. *Significant at 0.05. Inclusion of the elements of PC3 and PC4 does not change the significant level as such (R2 =0.84 for

models 1 and 2, and R2 =0.95 for model 3).

L. Tamene et al. / Geomorphology 76 (2006) 76–9186

which agrees with the result of the PCA. When regres-

sion was run after excluding gully erosion, which is the

most significantly correlated variable with SSY, the

following relationship can be obtained:

logSSY ¼ 0:0011HDþ 0:009ELþ 0:019

R2 ¼ 0:87; a b 0:001 ð8Þ

5. Discussion

5.1. Correlation analysis

The height difference (HD) and ruggedness number

(RG) show positive correlation with logSSY (Table 5)

because they reflect potential energy available to detach

and transport soil particles (Sarangi et al., 2003). The

proportion of erodible lithology (EL) plays an impor-

tant role in the siltation of reservoirs. Erodible lithology

types showing high SSY are shale and marl, and less

erodible lithology types are sandstone and metavolca-

nics. Similar observations are reported elsewhere (e.g.,

Lahlou, 1988; Fargas et al., 1997; Phippen and Wohl,

2003). Gully erosion/bank collapse (SBCR) plays a

very significant role in the siltation of reservoirs.

Dense networks of gullies (e.g., Fig. 2) increase slope

collapses and catchment connectivity, and facilitate

sediment delivery (e.g., Ownes and Slaymaker, 1993;

Wasson, 1994; Trimble, 1995; Walling et al., 1998;

Poesen et al., 2003). Livestock disturbances of gully

floors and banks as well as trampling of areas nearby

reservoirs (Fig. 2d) worsen gully erosion in the study

area. Similar observations are reported in other regions

(e.g., Trimble and Mendel, 1995; Lloyd et al., 1998).

Among the terrain-related attributes, the mean slope

(MES) shows a very poor correlation with sediment

yield in the study area, although their strong correla-

tions have often been reported (e.g., Hicks et al., 1996;

Schiefer et al., 2001). This observation suggests that the

role of slope is masked due to its association with dense

surface cover and less erodible lithology. In the study

areas, MES is positively correlated with dense surface

cover (DENSE), because the conservation and affores-

tation efforts have been concentrated on steep slopes.

Most steep slopes are also less accessible and therefore

less exposed to human and livestock disturbances. MES

is also negatively correlated with easily erodible lithol-

ogy (Table 5), which is also observed by Kirkby et al.

(2003) and Mills (2003). In addition, most steep slopes

are located away from the reservoirs so their influence

on reservoir siltation could be limited (Verstraeten and

Poesen, 2001a).

If it is difficult to assess the separate effects of

different factors, it may be necessary to stratify the

sites (Lu and Higgitt, 1999; Rustomji and Prosser,

2001). The correlation between some catchment attri-

butes and logSSY improved when three catchments

(Laelaywukro, Korir and Teghane) with relatively bet-

ter management, surface cover, and more resistant li-

thology (Fig. 5) were excluded from analysis. For

instance, catchment area (R2=0.80), mean slope

(R2=0.73), drainage length (R2=0.71), and elongation

ratio (R2=0.84) became significantly correlated with

logSSY (a b0.05). Catchment area (A) is positively

correlated with sediment yield as opposed to the

buniversalQ area�SSY relationship (e.g., Walling,

1983; Verstraeten et al., 2003). This may be because

of its positive and significant correlation with most of

the terrain attributes that are positively correlated to

logSSY (Table 5). The relationship between catchment

area and sediment yield could also be positive when the

main sediment source is from channels and floodplain

depositions (e.g., Walling and Webb, 1996), which is an

observed phenomenon in the study sites (Tamene et al.,

submitted for publication).

Fig. 5. Scatter plot of specific sediment yield against mean catchment slope showing a difference between less-managed and well-managed

catchments.

L. Tamene et al. / Geomorphology 76 (2006) 76–91 87

5.2. Principal component analysis

According to the constants in Eq. (6), sediment yield

is more sensitive to changes in terrain- and gully-related

factors (PC1) than to changes in LUC and lithology

(PC2) (Fig. 4a,b), which is also reflected in the corre-

lation result (Table 5). Fig. 4a,b shows that the first two

PCs account for about 92% of the total variability in

sediment yield (a b0.05). The PCA revealed the effect

of LUC on sediment yield (e.g., Fig. 4b), which was not

the case in the correlation analysis (Table 5).

Fig. 4a indicates that sites with low HD and SBCR

correspond to low sediment yield while those with high

HD and SBCR correspond to high sediment yield. On

the other hand, Fig. 4b indicates that those sites with

low proportion of dense cover or high proportion of

bare land and less resistant lithology correspond to high

sediment yield.

Comparison of Fig. 4a,b with Fig. 4c,d shows the

significant role of the factors of the two PCs in the

arrangement of sites within the PC spaces. Catch-

ments located at the lower end of PC1 have generally

similar characteristics, mainly low HD and low SBCR

(Fig. 4a,c). Adiakor (1) is located at the lowest end of

PC1 due to its large hypsometric integral. Gindae (5),

Grashito (6) and Maidelle (9) occupy a proximity

space because of their similar SBCR values. Adike-

nafiz (2) and Gerebmihiz (3) are located at the upper

end of PC1 mainly because they have high HD and

SBCR.

Similar interpretation can be given to the distribution

of sites in relation to PC2 (Fig. 4b,d). Catchments are

generally clustered into two categories mainly based on

surface cover and lithology. Laelaywukro (8), Korir (7)

and Teghane (11) are located at the higher position of

PC2 because these sites have relatively good surface

cover and more resistant surface lithology compared to

the others. Laelaywukro (8) is located at the higher end

of PC2 mainly due to its relatively high BUSH cover,

high MES and low CULT relative to the others. Grashito

(6), Maidelle (9) and Gerebsegen (4) are located at the

lower end of PC2 mainly because of their poor surface

cover and shale- and marl-dominated susceptible sur-

face lithology. The position of Adikenafiz (2) and Ger-

ebmihiz (3) as well as Majae (10) and Adiakor (1) in the

PC2 space is a result of their CULT or TBARE.

5.3. Multiple regression analysis

The results of the regression analysis (models 1 to 3

and Eqs. (7) and (8)) show that terrain form, surface

lithology, surface cover and gullies play significant

roles in the siltation of reservoirs. The catchments

characterized by pronounced terrain, less resistant li-

thology and wide spread gullies show high sediment

yield to reservoirs in the study area. Most of the studied

L. Tamene et al. / Geomorphology 76 (2006) 76–9188

catchments are characterized by rugged terrain in their

upslopes and predominantly cultivated land or bare

grazing areas in their lower slopes. The high HD accel-

erates flow energy, and the poor surface cover offers the

minimum protection. As a result, most of the flood-

plains are being eroded contributing sediment genera-

tion and enhancing efficient transport of sediment from

upslopes.

The main purpose of multiple regression analysis is

to assess the relationship between several independent

or predictor variables and a dependent variable. Once

the relationships are established, the equations can be

used to predict the status of a dependent variable in

relation to the independent variables. The equations

presented in Table 7 and Eq. (7) or Eq. (8), for example,

can be used to predict SSY of other similar catchments.

However, detailed analysis for a larger number of sites

will be required to establish a robust prediction model

of annual sediment yield.

5.4. Sediment yield controlling factors and

management implications

Based on the rate of sediment deposition (Table 4)

and the reservoir live storage capacity shown in Table 1,

most of the reservoirs will be filled with sediment within

less than 50% of their intended service time. For in-

stance, the Adikenafiz, Gerebmihiz and Grashito reser-

voirs have lost over 40% of their live storage capacity

within about 25% of their expected service time. The

above two and the Gindae and Maidelle reservoirs have

also lost more than 100% of their dead storage capacity

in less than a quarter of their expected life time. There-

fore the planned food security improvement scheme will

be under threat unless relevant preventive measures are

put in place.

The benefits of proper catchment management and

conservation can be demonstrated by Fig. 5. Three

reservoirs with comparatively proper catchment man-

agement and conservation (pink symbols in the upper

left graph) show relatively low sediment deposition

despite high terrain potential for erosion.

This study shows that terrain-from, surface lithology,

gullies and LUC play significant roles in determining

SSY variability, and gully erosion may be the most

important. The four catchments with high SSY have a

very high problem of gully erosion, whereas three

catchments with low SSY have very limited evidences

of gully erosion. Shibru et al. (2003) also shows that

gullies alone produced an annual soil loss rate of 25 t

ha�1 year�1 in eastern Ethiopia. Therefore, attention

needs to be paid to the rehabilitation/stabilization of

gullies and their banks, and prevent their destabilization

due to livestock trampling, although soil loss assess-

ment programs only infrequently take account of the

contribution of gully erosion (e.g., Liggitt and Fincham,

1989; Poesen et al., 2003). The location of reservoirs

should also be carefully determined to avoid severe

siltation. Reservoirs are often located at the confluences

of a few streams to efficiently collect water. However, if

one of the streams is characterized by heavy gullying, it

may lead to a higher risk of accelerated siltation.

Because only lumped attributes of catchments were

used in this study, the spatial distribution of the factors

responsible for siltation within each catchment could

not be examined. However, we could specify the terrain

attributes that land managers should focus on to tackle

the rapid sedimentation of the reservoirs. For instance,

covering the upland non-cultivable areas with vegeta-

tion looks effective (Tamene, 2005).

6. Conclusions

Identification of the major causative factors of ero-

sion that accelerate siltation in reservoirs is necessary to

guide targeted management. Against this background,

different statistical analyses were performed to assess the

role of different catchment attributes in the siltation of

reservoirs in the drylands of northern Ethiopia. The

results show that pronounced terrain steepness, easily

detachable slope material, poor surface cover, and

gullies accelerate siltation in the reservoirs. These factors

often co-exist, but the distribution of gullies is the most

influential. Some of the catchments show lower sedi-

ment yields despite their steep terrain because catchment

management practices significantly reduced erosion.

The sedimentary records from depositional environ-

ments such as lakes and reservoirs can be used to

provide information on the past long-term behavior of

a river basin (Foster and Walling, 1994; Owens et al.,

1999). We did not perform such analyses because the

reservoirs studied have ages of less than 10 years.

However, discussion in this study will serve as a useful

benchmark for future studies.

Although this paper has mostly performed quantita-

tive analyses, the semi-qualitative expert-based techni-

ques were employed to determine the severity of gully

erosion. In future work, more detailed, quantitative

assessments of gullies need to be performed.

Acknowledgments

The authors acknowledge the financial support by

DAAD (Germany) and fieldwork support by Mekelle

L. Tamene et al. / Geomorphology 76 (2006) 76–91 89

University (Ethiopia). We thank Hailu Girmay and

Tsehaye Gizaw for their assistance during filed work.

Assefa Abegaz and Kifle W. Aregay are also acknowl-

edged for their technical support, mainly related to the

pit-based survey and lithologic mapping, respectively.

We are grateful for the constructive comments given by

the anonymous reviewers of our paper. We are also very

thankful to Takashi Oguchi for his valuable comments

and suggestions.

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