




































East Afr. J. Biophys. Comput. Sci. (2022), Vol. 3, Issue. 1, 24-42 
 

 

 

 

*Corresponding author: 

  Email: tamenetadele@gmail.com,  +251 916 138218 https://dx.doi.org/10.4314/eajbcs.v3i1.4S 

 
 

 

 

Landslide Hazard Assessment and Zonation by using Slope Susceptibility Evaluation Parameter 

(SSEP) Rating Scheme- a Case from Debre Sina, Northern Ethiopia 

Tamene Tadele  

 

Department of Geology, College of Natural and Computational Sciences, Hawassa University, P. O. 

Box 05, Hawassa, Ethiopia 

 

 

KEYWORDS:  

Landslide Hazard Zonation; 

Slope Susceptibility;  

Debre Sina;  

Landslide Triggering;  

Slope Facet 

 

 

 

 

 

 

 

ABSTRACT 

Rainfall-induced landslides of different types and sizes frequently affect the hilly and 

mountainous terrains of the highlands of Ethiopia. The principal aim of the proposed 

research work was intended to prepare a landslide hazard zonation map of the area, 

particularly for hazardous zones. In this study, the Slope Susceptibility Evaluation 

Parameter rating scheme has been implemented as a relevant approach to map the 

landslide hazard of the Debresina area, which has experienced slope failure problems for a 

long period of time. The geology of the area includes quaternary sediments, ignimbrite, 

rhyolite, different kinds of basalts, and tuff deposits, which are highly weathered and 

changed into unconsolidated sediments at some localities. Locally observed geological 

structures such as joints, dykes, and other discontinuities have a considerable role in the 

initiation of landslide hazard. As a general methodology, a facet map was prepared from a 

topographic map (1:50,000) and rating values were assigned to each causative parameter 

(both intrinsic and external) based on its severity in triggering landslide hazard. The study 

area was classified in to three hazard classes, of which 25 % of the slopes fall in to a 

moderate hazard zone, while 58 % and 17 %were found to be high and very high hazard 

zones, respectively. Authentication of the landslide hazard zonation map with past 

landslide activities suggests the rationality of the considered leading parameters, the 

adopted technique, tools, and procedures in developing the study area's landslide hazard 

map. Furthermore, in order to validate the landslide hazard map prepared during the 

present study, active landslide activities and potential instability areas, delineated through 

inventory mapping, were overlaid on it, which yielded promising results.  

 

INTRODUCTION 

The earth’s surface is always in a dynamic 

change. These changes are more pronounced in 

mountainous terrains as a result of different 

mass wasting processes. One of these mass-

wasting processes is landslides (Hansen, 1984). 

According to Mohammad et al. (2012), 

landslide is a slow to rapid downward motion of 

unbalanced rock and debris masses because of 

gravity, whereas Baeza and Corominas (2001), 

identified landslides occur at a very slow rate, 

particularly in areas that are very dry and areas 

that receive sufficient rainfall such that 

vegetation has stabilized the surface. They may 

also occur at very high speed, such as in rock 

slides or landslides, with disastrous 

consequences, both immediate and delayed, e.g., 

East African Journal of Biophysical and Computational Sciences 

Journal homepage : https://journals.hu.edu.et/hu-journals/index.php/eajbcs 

  
Hawassa University

College of Natural & Computational Sciences

Year 2021

Volume xx No xx

 
Research article

mailto:tamenetadele@gmail.com
https://dx.doi.org/10.4314/eajbcs.v3i1.4S


East Afr. J. Biophys. Comput. Sci. (2022), Vol. 3, No. 1, 23-42 
 

24 

 

resulting from the formation of landslide dams 

(Saro and Biswajeet, 2006). Depending on 

techniques applied, activities involved, data 

analysis, scale of study, and data availability, 

various landslide hazard-mapping approaches 

were introduced. Barredo et al. (2000) divided 

these methods into inventory, heuristic, 

statistical, and deterministic approaches. 

Performing deterministic slope stability analyses 

demands a considerable amount of time and a 

deep understanding of both geological and 

geotechnical factors, coupled with a clear grasp 

of potential slope failure mechanisms. Besides, 

such analysis techniques may be suitably 

applied to small areas, at the scale of a single 

slope only (Clerici, 2002; Casagli et al., 2004; 

Raghuvanshi et al, 2014). McClelland et al. 

(1997) emphasized the heuristic or expert-

driven approach, as a method in which a 

geomorphological expert decides on the type 

and degree of hazard for each area, using either 

a direct mapping or indirect mapping approach. 

This approach is a time consuming and it 

depends on a large degree on the expertise of 

the geomorphologist (Barredo et al, 2000). The 

statistical approach compares the spatial 

distribution of existing landslides in relation to 

different causative factors (Aleotti and 

Chowdhury, 1999). These methods are good for 

assessing the spatial probability but there are 

problems in evaluating either temporal 

probability or the effects of future 

environmental changes (Van Westen et al., 

2006). The Landslide Hazard Evaluation Factor 

(LHEF) technique has been utilized successfully 

over the years by many researchers but as 

proposed by Raghuvanshi et al. (2014), its 

major drawback is that it does not account for 

external causative factors. Further, it does not 

predict for anticipated adverse conditions during 

construction and performance stage rather it 

offers stability condition for the slopes only for 

the existing situations prevailed at the slopes 

during the time of investigation. 

In the current study effort is made to overcome 

the shortcomings of above approaches and thus, 

a slope susceptibility evaluation parameter 

(SSEP) rating technique, which encompasses 

both intrinsic and external parameters, has been 

implemented. Landslide problem has been 

causing lots of casualties, economic and social 

problems to societies especially to those who 

are living in the mountainous areas. According 

to Kifle Woldearegay (2013), the hilly and 

mountainous terrains of the highlands of 

Ethiopia are frequently affected by rainfall-

induced landslides of different types and sizes. 

According to Gebreslassie (2011), the 

widespread occurrence of landslides in Ethiopia 

is largely due to a combination of predisposing 

factors, including rugged morphology, high 

topography, and the characteristics of 

outcropping rocks. The triggering factors are 

essentially connected with the rainfall regime 

and to a minor extent with seismicity 

(Gebreslassie, 2011). According to Asmelash 

Abay and Barbieri (2012), Debre Sina is located 

along the southwestern Afar rift margin, and it 

was frequently affected by landslides in the past 

few years. It is bounded by different mountains 

because of which it has experienced rainfall 

triggered landslides which endangered the life 

of people and destroyed public and private 

properties including various infrastructures. 

 

 

 



East Afr. J. Biophys. Comput. Sci. (2022), Vol. 3, No. 1, 23-42 
 

25 

 

MATERIALS AND METHODS 

Description of the study area 

Location and climate 

The study area, Debre Sina, is situated in 

Amhara Regional State at a distance of 200 Km 

toward NNE of Addis Ababa, the capital city of 

Ethiopia. Geographically, it is bounded between 

UTM coordinates of 582000-593000 mE; 

1080000-1100000 mN (Fig.1). The prevailing 

climatic condition of the area is "Dega" with 

mean annual rainfall of 1736 mm/year and 

temperature varying between 10°C to15°C.  

Physiography and the drainage pattern 

Physiographically, the study area is located in 

the Showan highlands, the smallest highlands of 

the Ethiopian northwestern highlands, which 

also includes the Tigrean north central massif, 

South Western highlands of Gojam and Gondar 

(Leta Alemayehu, 2007). The Showan plateau is 

bounded by the Ethiopian rift on the eastern and 

south eastern sides while Abay gorge border it 

on the north western side. The study area 

generally is characterized by highly variable 

topographical features which are a reflection of 

the past geological and erosion processes. The 

landscape includes plateaus, steep hill slopes, 

deeply incised valleys and gorges. The elevation 

of the study area ranges from 1500 m in the 

Southern sector to 3100 m in the Northern 

section.

 

 Figure 1. Location map of the study area.



East Afr. J. Biophys. Comput. Sci. (2022), Vol. 3, No. 1, 23-42 
 

26 

 

The drainage system of the area includes many 

small tributaries feeding the main rivers. Most 

of the tributaries were dry during the present 

field visit. Majority of them, especially small 

tributaries, arise from high mountains and join 

the main rivers at the valley floor. The general 

drainage pattern of the study area is dendritic 

type as portrayed in (fig.1). 

Geology 

The Paleozoic–Mesozoic sediments associated 

with transgression regression of the sea and 

Cenozoic volcanic rocks which is directly 

overlying the Precambrian metamorphic and 

Mesozoic sedimentary rocks in Ethiopia 

(Kazmin, 1973). Among these rock units, the 

geology of the study area and its surroundings 

can be grouped in to the Cenozoic volcanic 

rocks (fig.2).According to Astis et al. (1997), 

the Ethiopian volcanics can be related to two 

main magmatic stages. The first is the 

Oligocene – Pliocene large fissure eruptions of 

basalts which build up abundant flood lava 

sequence known as Ashange and Aiba Basaltic 

Formations associated with late ignimbrite sheet 

(AlajiRhyolitic Formations).  

 

 

 

Figure 2. Geological map of the study area.



East Afr. J. Biophys. Comput. Sci. (2022), Vol. 3, No. 1, 23-42 
 

27 

 

This magmatic stage ended with the 

development of large basaltic shield volcanoes, 

known as the Tarmaber Formation. However in 

the recent studies about the continental flood 

basalts of north-western part of Ethiopia, the 

whole formations were considered as a single 

unit (Hofman et al., 1997; Piket al., 1998. 

Recent classification for continental flood basalt 

of North western part of Ethiopia was followed 

as Lower Formation, Upper Formation and the 

Shield Volcano. Both the Lower and Upper 

Formation of the continental flood basalt was 

emplaced 30 my ago within short period of 

time, less than 1My (Hofmann et al., 1997). 

The central Ethiopian highlands exhibit a 

geological structure consisting of Tertiary 

volcanic rocks capping Mesozoic sedimentary 

rocks, which are visible only in the deep valleys 

carved by major rivers. Examples include: Abay 

Gorge, Jimma Gorge, and Mugher Gorge. 

According to Mohr and Zenitttin (1988), the 

Ashangie Formation has been defined by three 

characteristics: it has experienced a marked dip 

into the flow sequence of up to 40o; flow 

thickness averages only about 5m; and 

individual flows are rarely traceable for more 

than a few kilometers along strike. Mohr and 

Zenitttin (1988), highlighted that the Aiba 

Formation is typically composed entirely of 

massive flood basaltic flows, with or without 

intervening agglomerate beds. According to 

GezahegnYirgu (1997), two major phases of 

magmatic activity took place, which produced 

different formations. A first phase was 

responsible for the eruption of lavas that built 

thick succession, up to nearly two kilometers, of 

fissural basalts (known as Ashange and Aiba 

Basaltic Formation) and later emplacement of a 

thick series, up to 500 meters, of silicic lavas 

mainly in the form of ignimbrite sheets (Alaji 

Rhyolite Formation). The building up of huge 

shield-like volcanic complexes followed this 

fissural magmatic stage from central vents with 

the predominance of basalts over evolved 

volcanics (Termaber Basalt Formation). 

Methodology 

Most of the landslide hazard zonation methods 

are based on the basic assumptions that mass 

movements are caused by the geological, 

geomorphic, human induced, etc. factors that 

can be described through physical parameters, 

and that the knowledge about these conditions 

enables drawing conclusions on future 

landslides (Lang et al., 1999). According to 

Anbalagan (1992), Landslide susceptibility 

maps can be constructed by using the relation 

between each landslide and causative factors. 

Different landslide hazard mapping 

methodologies in relation to types and scales are 

portrayed in Table 1.  

 

 

 

 

 

 



    
 

28 

 

 

Types Techniques Activities 

Characteristics Scale 

D
ir

ec
t 

In
d

ir
ec

t 

Q
u

a
li

ta
ti

v
e 

Q
u

a
n

ti
ta

ti
v

e 
1

:1
0

0
,0

0
0
 

1
:2

5
,0

0
0
 

1
:1

0
,0

0
0
 

Heuristic Geomorphologic 

analysis 

Use field-expert opinion in 

zonation 

X X X  X X X 

 mapQualitative

combination 

Use expert-based weight values 

of parameter maps 

 X X X X X  

Statistical statisticalBivariate  

analysis 

Calculate importance of 

contributing factor combination 

 X  X  X  

 Multivariate 

statistical analysis 

Calculate prediction formula 

from data matrix 

 X  X  X  

 Probabilistic analysis Calculate prediction from 

inventory and time period 

 X  X X X  

Deterministic factorSafety

analysis 

Apply hydrological and slope 

stability models 

 X  X   X 

Inventory Remote sensing and 

field investigations 

Show locations and 

characteristics of past landslides 

X X X   X  

 

Landslide Hazard Evaluation Factor (LHEF) 

Rating Scheme 

The weight rating system is usually designed in 

many different ways on the basis of studying the 

impact of each selected factor, for their 

importance in inducing the instability. 

Anbalagan (1992), has suggested a landslide 

hazard evaluation factor (LHEF) rating system 

that incorporates all the causative factors as 

listed in Table 2.  The LHEF rating scheme may 

be more relevant as it is based on an empirical 

approach using important natural contributing 

factors of slope instability such as; lithology, 

structure, slope morphometry, land use and land 

cover, relative relief and hydro-geological 

conditions. In this scheme, the external factors 

including rainfall and seismicity have not been 

included. The maximum weight for individual 

factor has further been sub-divided into a 

number of categories to form a detailed LHEF 

rating scheme. This scheme can then be used for 

calculating total estimated hazard (TEHD) for 

individual facets.  The total estimated hazard 

(TEHD) value indicates the net probability of 

instability of a slope facet. It is calculated slope 

facet-wise, because adjoining slope facets may 

have completely different stability situations. 

The TEHD value of an individual slope facet is 

obtained by summing up the ratings of each 

causative factor, obtained from the LHEF rating 

scheme for that slope facet. Thus, TEHD value 

is equal to the sum of ratings of categories of all 

causative factors. As depicted in Table 2, TEHD 

values are then arbitrarily categorized into 

different landslide hazard zones. 

 

East  Afr.  J.  Biophys.  Comput.  Sci. (2022), Vol. 3, No. 1,  23-42

Table 1. Methods of landslide susceptibility mapping in relation to types and scales.



    
 

29 

 

 

S.No Causative Factors Maximum LHEF Rating 

1 Lithology 2 

2 Relationship of structural discontinuities with slope 2 

3 Slope morphometry 2 

4 Relative relief 1 

5 Land use and land cover 2 

6 Hydrogeological condition 1 

 Total 10 

 

The LHEF rating scheme employs an empirical 

method to evaluate slope instability by 

considering the individual and combined 

influences of inherent causative factors. These 

inherent factors then form the basis for 

Landslide Hazard Zonation. (LHZ) mapping on 

macro-zonation approach. Maximum values of 

rating for each parameter is awarded keeping in 

mind its estimated significance in resulting 

slope failure and also to denote overall field 

circumstances (Table 3).  

 

 

S.No TEHD Value Hazard Class 

1 <3.5 Very Low Hazard (VLH) 

2 3.5-5.0 Low Hazard (LH) 

3 5.1-6.0 Moderate Hazard (MH) 

4 6.1-7.5 High Hazard (HH) 

5 >7.5 Very High Hazard (VHH) 

 

EvaluationSusceptibilityStabilitySlope

Parameter (SSEP) Rating Scheme 

mappingzonationhazardlandslideThis

methodology is a modified technique which is 

developed by Raghuvanshi et al. (2014) and is 

applicable in large areas demanding rapid slope 

stability assessment. It mainly relies on field 

data and produces landslide hazard zonation 

map by combining both intrinsic and external 

slope instability triggering parameters. It was 

developed in order to overcome the 

shortcomings of Anbalagan (1992) LHEF rating 

scheme and is found to be suitable to be applied 

in present study. The SSEP rating technique 

involves both internal and external activating 

parameters accountable for slope instability. The 

slope stability is mainly governed by intrinsic 

parameters such as; slope geometry, slope 

material (lithology or soil type), structural 

Table 3. LHZ classes on the basis of Total Estimated Hazard (TEHD)

East  Afr.  J.  Biophys.  Comput.  Sci. (2022), Vol. 3, No. 1,  23-42

Table 2. Maximum LHEF rating for causative factors for macro-zonation.



East Afr. J. Biophys. Comput. Sci. (2022), Vol. 3, No. 1, 23-42 
 

30 

 

discontinuities, land use and land cover and 

groundwater (Wang and Niu, 2009). In addition 

to the factors mentioned, both natural and 

human-induced external parameters that 

contribute to slope instability are also taken into 

account. The primary natural factors identified 

as triggers for slope instability include seismic 

activity (Keefer, 2000) and rainfall (Collison et 

al., 2000; Dahal et al., 2006). Other natural 

influences that can lead to slope instability, such 

as snow and avalanches, wind erosion, 

permafrost conditions, shoreline processes, and 

volcanic activity, are not considered in the SSEP 

for landslide hazard assessments. Manmade 

activities mainly include constructions and 

cultivation practices on slopes (Wang and Niu, 

2009). Slope Facet is defined as a land unit, 

which is characterized by uniform slope 

geometry in terms of slope inclination and slope 

direction (Anbalagan, 1992). To demarcate the 

slope facets, topographic maps were utilized. 

Facet boundaries were delineated using 

prominent and minor hill ridges, as well as 

primary and secondary streams, along with 

other topographical features. For the present 

study, Debre Sina topo map of 1:50,000 was 

utilized to delineate slope facets of the study 

area. Rating values was assigned for each 

intrinsic and external causative factor based on 

its severity in landslide initiation and the 

summation of all causative factors will provide 

Evaluated Landslide Hazard (ELH). Finally 

landslide hazard zonation (LHZ) map was 

prepared based on the facet-wise distribution of 

ELH values. Table 4 portrays distribution of 

higher SSEP ratings assigned to each causal 

factor. 

 

  

   

Triggering Parameters  Maximum Rating 

Intrinsic Parameters   

 1. Slope Geometry Relative Relief 1 

  Slope Morphometry 2 

 2.  Slope Material  1 

 3.  Structural Discontinuities  2.5 

 4. Land use Land cover  1.5 

 5. Groundwater  2 

External Parameters   

 1. Seismicity  2 

 2. Rain Fall  1.5 

 3. Man-made Activities  1.5 

Total   15 

ELH= Summation of ratings of intrinsic parameters (relative relief + slope morphometry + slope material + 

structural discontinuity + land use and land cover + groundwater) + Summation of ratings of external parameters 

(rainfall + seismicity + man-made activities) 

 

 

Table  4.  Distribution  of  maximum  SSEP  ratings  assigned  to  different  intrinsic  and  external  

factors (Source: Raghuvanshi  et al.,  2014)



East Afr. J. Biophys. Comput. Sci. (2022), Vol. 3, No. 1, 23-42 
 

31 

RESULTS & DISCUSSION 

Preparation of facet map 

For convenience and ease assessment of 

landslide hazard, the study area has been 

divided into different slope facets which were 

defined by major or minor hill ridges, primary 

and secondary streams, and other topographic 

waves. According to Anbalagan (1992), slope 

facets are characterized by more or less uniform 

slope inclination and slope direction. These 

slope facets were prepared from topographic 

map of scale 1:50,000 and verified in the field. 

Slope facet was used as base map to award 

rating values for landslide hazard triggering 

parameters (Table 5). The slope facets were 

usually delimited by ridges breaks in slope, 

streams, spurs, gullies and rivers etc. The facet 

maps form the basis for the preparation of 

thematic maps in general and SSEP mapping in 

particular and individual facet is the smallest 

mappable unit. In all 60 facets have been 

delineated in the study area on the basis of 

visual interpretation of topographic maps fig. 

3a. 

Landslide Hazard Triggering Parameters 

For landslide hazard zonation, numerical ratings 

have been assigned to each of the internal and 

external activating parameters on the basis of 

their impact towards instability of slope, based 

on standard SSEP rating table. 

Intrinsic Parameters 

Intrinsic parameters are considered in hazard 

mapping because they play a great role in the 

stability conditions of the slope. These intrinsic 

parameters are relative relief, slope 

morphometry, slope material, structural 

discontinuities, land use and land cover and 

groundwater (Anbalagan, 1992; Wang and Niu, 

2009). Based upon the given conditions for each 

of these internal parameters they may have an 

effect over the stability condition of the slope. 

Relative Relief 

Relative relief is one of the important causative 

factors which may cause slope instability. It 

affects the instability condition by increasing the 

gravitational energy which pulls the slope 

material down the slope. The relative relief map 

represents the local relief of maximum height 

between the ridge top and the valley floor within 

an individual facet (Anbalagan, 1992). Relative 

relief map of the study area has been prepared 

by taking the altitude difference between hill top 

and valley bottom within each slope facet which 

was later processed by ArcGIS-10.8 software. In 

the study area 57% of the facets fall in very high 

relative relief whereas 22% fall in high relief. 

The remaining facets (21%) fall in medium and 

moderate relative relief (fig.3d). This implies 

that more than half of the study area possesses 

very high relative relief which renders it 

susceptible to landslide. 

Slope Morphometry 

Slope morphometry map of the study area has 

been designed by calculating the slope angles 

from topographic map. The ratio of height 

difference between two points in a given facet to 

horizontal distance gives decimal value of the 

slope. By taking the inverse tangent of this 

value slpes in degrees have been manipulated. 

These slopes fall in to different slope classes as 

escarpment/cliff (> 45°), steep slpe (36°-45°), 



East Afr. J. Biophys. Comput. Sci. (2022), Vol. 3, No. 1, 23-42 
 

32 

moderately steep slpe (26°-35°), gentle slpe 

(16°-25°) and very gentle slpe (< 15°). Later, 

slope morphometry map of the study area has 

been prepared by using ArcGIS-10.8 software.  

Accordingly, 42% of the facets experience 

moderately steep slope (26°-35°) while 33% fall 

under gentle slopes. The remaining facets 

possess steep slopes, escarpment and very 

gentle slope which account for about 10%, 8% 

and 7%, respectively. Generally, most of the 

facets have moderately steep slope ranging from 

26° to 35°(fig 3e). 

Slope Material 

The rock sub classes in SSEP rating system are 

adopted from classification of rocks based on 

field estimates of strength by observation which 

is proposed by (Hoek and Bray, 1997). Thus, 

slope material is classified as very weak rck 

(1-5 MPa), weak rck (5-25 MPa), medium 

strong rck (25-50 MPa), strng rck (50-100 

MPa), very strng rck (100-250 MPa) and 

extremely strng rck (>250 MPa). Slope 

material map of the study area has been 

prepared from field bservation using l:50,000 

scale tpgraphic map as base map (fig. 3f). 

Slope material of the study area is well 

described by highly weathered and fragmented 

rock mass that made it difficult to distinguish 

some rocks from soil during field visit. 

Ignimbrite, Alaje Basalt and fractured rhyolite 

are some of the lithologies on which intense 

weathering was observed. Generally, 38% of the 

study area is covered by medium strength rocks 

while colluvium materials cover about 27% of 

it. Highly weathered materials and weak rocks 

each comprise 22% and 13%, respectively. 

 

Land Use Land Cover 

Land use and land cover pattern is one of the 

important parameters governing slope stability. 

Vegetation has major role to resist slope 

movements, particularly for failures with 

shallow rupture surfaces. A well spreaded 

network of root system rises the shearing 

resistance of the slope material because of 

natural anchoring of slope materials, particularly 

for soil slopes. Moreover, a thick vegetation or 

grass cover reduces the action of weathering and 

erosion, hence adds to stability of the slopes. On 

the other hand, barren or sparsely vegetated 

slopes are usually exposed to weathering and 

erosion action, thus rendering it vulnerable to 

failure (Wang and Niu, 2009).  Slope instability 

is also induced because of anthropogenic 

activities, i.e., urbanization, particularly on 

higher slope angles (>300). It not only removes 

vegetation cover but also adds to the natural 

weight of the slope as surcharge due to the 

weight of civil structures. In a hill slope with 

higher slope angle, buildings are usually located 

by constructing local cut slopes and flat terraces. 

With this concept urbanization is broadly 

classified into three categories (Zubair et al., 

2012). A sparsely urbanization slope is where 

construction terraces are located far apart (more 

than 15 m of horizontal spacing) providing a 

considerable distance between two terraces 

along the slope. When we see the areal coverage 

of land covers in the present study area, bushes 

and shrubs alone cover 29 % of the study area 

whereas bare land comprises 23 %. On the other 

hand heterogeneous agricultural areas, arable 

land and forest encompass 20 %, 17 % and 11 

% of the study area, respectively as shown on 

fig. 3c. The LULC map of the study area was 



East Afr. J. Biophys. Comput. Sci. (2022), Vol. 3, No. 1, 23-42 
 

33 

prepared from ERDAS 9.2 softer and later 

verified during field visit. 

Groundwater conditions of the study area 

The susceptibility of rock/ soil to failure is 

majorly determined by groundwater of an area. 

Hydrological characteristics of an area include 

underground water conditions, saturation state 

of rock/soil, presence of streams, rivers, and 

drainage pattern of the area. Water bodies 

displaced because of presence of interruptions 

and shallow water-table environments in hilly 

terrains along with torrential rainfall make the 

slopes susceptible to instability. According to 

Murck et al. (1996) during the prolonged 

monsoon phases, increased pore-water pressure 

creates favorable conditions for deep-seated 

landslides. In the present study area, 

groundwater is not uniformly distributed over 

all facets. Therefore, groundwater investigation 

has been conducted facet wise. Some facets 

have small flowing streams whereas others 

display wet to dry conditions. As fig. 3b depicts, 

surface terraces of groundwater of the study 

area portrays dry slopes (28 %), wet (22%), 

flowing (18 %), damp (18 %) and dripping (14 

%), respectively.  

 



East Afr. J. Biophys. Comput. Sci. (2022), Vol. 3, No. 1, 23-42 
 

34 

 

 
 

 

 

  

  

 

a b 

d 

c 

e f 

Figure  3.  Maps showing  slope facets and intrinsic landslide triggering parameters of the study area.  (a)Slope facet;(b)  

groundwater;(c)land use land cover;(d) relative relief;(e) slope morphometry;  (f)  slope material.



East Afr. J. Biophys. Comput. Sci. (2022), Vol. 3, No. 1, 23-42 
 

35 

 

LandslidExternal TriggeringHazarde

Parameters 

Rain Fall 

To assess and see the effect of rainfall to the 

landslide occurrences of the area, rain fall data 

for 35 years (1981-2013) was collected from 

National Meteorology Agency of Ethiopia. It 

indicates that the maximum, minimum and 

mean annual rainfall in the study area is 3592.7 

mm, 683.6 mm and 1,735.591 mm, respectively. 

The maximum annual rainfall was recorded in 

the year 1997, while the minimum in the year 

2012. The monthly maximum rainfall is always 

in the months of July and August for all the 

recorded data. The study area is one of the areas 

receiving a high rainfall in the country having a 

bimodal rainfall nature which possesses 

alternating dry and rainy seasons. It receives 

exceptionally peak precipitation in the months 

of July and August. These two months alone 

contribute 43% of annual precipitation, which 

maximizes landslide occurrences. On the other 

hand months such as; March, April, May and 

September experience moderate amount of 

monthly precipitation. Low amount of mean 

monthly precipitation is recorded in the months 

of October and November whereas December, 

January and February are generally regarded as 

dry months as they receive very low amount of 

monthly precipitation (fig. 4b) 

 

 

Most of the landslides recorded in the study area 

happened when the annual rain fall exceeds the 

long term average rainfall. However, there were 

lower occurrences of major landslides between 

the years of 1983 to 1994 where the amounts of 

annual rainfalls were lower than the long term 

average except for 1986 and 1989. 

Seismicity 

The earthquake shocks may be responsible for 

triggering new landslides and reactivating old 

landslides. The vibrations due to earthquake 

may induce instability, particularly in loose and 

unconsolidated material on steep slopes. The 

Afar rift margin, where the study area is 

  

a b 

Figure 4. Mean annual and mean monthly rainfall of the study area (1981-2013).



East Afr. J. Biophys. Comput. Sci. (2022), Vol. 3, No. 1, 23-42 
 

36 

 

situated, is known for its earthquake 

occurrences. Most of the earthquake ranges 

from small to medium level (Atalay Ayele, 

2007). Although not registered, the occurrences 

of landslide in association with Afar earthquake 

in the area are common as evidenced by local 

dwellers.  For example, as obtained from local 

information, there was a landslide occurrence 

around the Nibamba Gebriel and Sina Aregawi 

contemporaneous with the 1961 Kara-Kore 

earthquake. 

 

 

 

As portrayed above in figure 5, the present 

study area falls in seismic zone which has an 

intensity of 8, this zone has a ground 

acceleration of 0.1- 0.5g. Thus, the rating for 

ground acceleration of 0.1-0.5g, as per the 

standard SSEP table is estimated to be 1.5. 

Accordingly, this rating value has been 

Figure 5. Seismic risk map of Ethiopia (Source: Laike Mariam Asfaw, 1986).



East Afr. J. Biophys. Comput. Sci. (2022), Vol. 3, No. 1, 23-42 
 

37 

 

distributed to all 60 slope facets in order to 

generate landslide hazard zonation map of the 

study area. Earthquake induced landslide is so 

frequent in most parts of Ethiopia that it needs 

intensive analysis of causative factors for any 

landslide including seismic factor. 

  

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1 0.6 0.6 1.66 0.6 0.4 0.5 1 1.5 0.75 

2 0.8 2 1.86 0 0.4 0.8 1.25 1.5 0.75 

3 0.2 0.3 2.31 0 0.75 0.8 1 1.5 0.75 

4 0.2 0.6 2.49 2 0.4 0.3 1 1.5 0.75 

5 0.6 1 1.82 1 1.5 0.4 0.75 1.5 0.75 

6 0.2 0.6 2.5 1 1.5 1 0.75 1.5 0.75 

7 0.8 0.6 2.38 1.5 1.2 0.5 0.1 1.5 0.75 

8 0.8 2 1.84 1.5 1.5 0.4 0.1 1.5 0.75 

9 0.6 0.6 1.58 0 0.4 0.4 0.1 1.5 0.75 

10 0.8 0.6 1.88 0 0.75 0.5 0.15 1.5 0.75 

11 1 1 2.49 0 1.5 0.4 1.25 1.5 0.75 

12 1 1 2.23 1.5 1.5 0.1 1 1.5 0.75 

13 0.6 0.6 2 1.5 0.4 0.4 1 1.5 0.75 

14 0.6 0.6 1.86 0 0.75 0.8 1 1.5 0.75 

15 0.8 1.7 2.5 1.5 1.5 1 1 1.5 0.75 

16 0.2 0.6 2.43 2 1.5 1 1.25 1.5 0.75 

17 0.8 0.6 1.86 0 0.4 0.3 1 1.5 0.75 

18 1 0.6 2.24 0.6 1.2 0.3 0.5 1.5 0.75 

19 1 1.7 2.32 2 1.5 1 1 1.5 0.75 

20 1 0.6 1.81 0 0.4 1 0.1 1.5 0.75 

21 1 1.7 2.5 2 1.5 1 1.25 1.5 0.75 

22 0.6 1 1.6 1 0.4 1 0.75 1.5 0.75 

23 0.2 0.6 2.11 0.6 1.2 0.8 0.15 1.5 0.75 

24 0.6 1 2.13 0 0.4 0.8 0.1 1.5 0.75 

25 0.8 1 2.38 1 0.4 0.25 0.75 1.5 0.75 

26 1 1 2.5 0 0.4 0.5 0.75 1.5 0.75 

27 0.8 2 1.71 2 1.5 1 1 1.5 0.75 

28 0.8 0.6 1.94 0 0.4 0.5 0.1 1.5 0.75 

29 1 0.6 2.39 0.6 0.75 1 1 1.5 0.75 

30 1 0.3 1.78 0 1.5 1 0.1 1.5 0.75 

 

 

Table  5.Rating  values assigned to each causative parameter  in all slope facets



East Afr. J. Biophys. Comput. Sci. (2022), Vol. 3, No. 1, 23-42 
 

38 

 

….Continuation of Table 5 
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31 1 2 2.32 1 1.5 1 1.25 1.5 0.75 

32 1 1 2.16 2 0.4 1 1.25 1.5 0.75 

33 1 1 1.6 0.6 0.75 0.5 1 1.5 0.75 

34 0.8 1 2.45 0.6 1.2 0.8 0.75 1.5 0.75 

35 1 0.6 1.62 2 0.4 0.1 1.25 1.5 0.75 

36 1 1.7 2.46 2 1.5 0.4 1 1.5 0.75 

37 1 0.6 2.17 2 1.5 0.8 0.75 1.5 0.75 

38 0.6 1 2.5 2 1.5 1 1.25 1.5 0.75 

39 1 1.7 1.69 1 0.4 0.8 0.1 1.5 0.75 

40 0.8 1 2.22 0 0.4 0.8 0.1 1.5 0.75 

41 0.8 0.3 2.48 0.6 0.75 0.8 0.75 1.5 0.75 

42 1 1 2.03 1 0.4 1 0.15 1.5 0.75 

43 1 1 1.94 1 1.5 0.5 1 1.5 0.75 

44 1 1.7 2.48 1.5 1.5 1 1.25 1.5 0.75 

45 1 1 1.55 0.6 0.4 0.8 0.1 1.5 0.75 

46 1 0.6 1.7 0 1.2 0.5 1 1.5 0.75 

47 1 1 1.64 0 0.75 1 0.1 1.5 0.75 

48 1 1 1.83 0.6 1.2 0.5 0.75 1.5 0.75 

49 1 1.7 2 1 1.2 1 0.1 1.5 0.75 

50 1 2 2.48 1.5 0.75 0.8 1.25 1.5 0.75 

51 1 0.3 2.24 0 0.75 1 1 1.5 0.75 

52 1 1 2.5 1 1.2 1 1 1.5 0.75 

53 0.8 1.7 1.98 1 1.5 0.5 0.75 1.5 0.75 

54 1 2 2.33 1 1.5 0.5 0.75 1.5 0.75 

55 1 1 1.84 0.6 1.2 0.8 1 1.5 0.75 

56 1 1.7 2.4 2 1.5 1 1 1.5 0.75 

57 1 1 2.39 0.6 1.5 0.4 1.25 1.5 0.75 

58 1 1 1.98 0 1.2 0.4 0.1 1.5 0.75 

59 1 1 2.08 1.5 0.75 1 1 1.5 0.75 

60 1 1.7 2.08 1 1.2 0.4 1 1.5 0.75 

 

Estimation of Evaluated Landslide Hazard 

(ELH) 

The evaluated landslide hazard indicates the net 

likelihood of instability and has been calculated 

facet-wise. The ELH of an individual facet was 

obtained by adding the ratings of the individual 

causative factors obtained from the SSEP rating 

scheme. Evaluated Landslide Hazard is 

estimated as summation of ratings of intrinsic 



East Afr. J. Biophys. Comput. Sci. (2022), Vol. 3, No. 1, 23-42 
 

39 

 

and external parameters. On the basis of 

evaluated landslide hazard (ELH), three 

categories of landslide hazard zones have been 

identified for the present study area (fig.6) viz., 

moderate hazard (MH), high hazard (HH) and 

very high hazard (VHH). These zones are 

distributed in accordance with the geology and 

geomorphology of the area.  Areal coverage of 

moderate hazard is 25 % whereas those of high 

hazard and very high hazard are 58 % and 17 %, 

respectively. These figures indicate that 75 % of 

the study area is very susceptible to landslide 

hazard 

Moderate Hazard Zone 

Moderate hazard zone represents relatively safe 

areas for construction and various infrastructural 

activities. It covers 25 % of the study area and 

out of 60 slope facets 15 fall in moderate hazard 

zone. Moderate hazard zones are commonly 

distributed in Northern, Central and Southern 

parts of the study area. Chira Meda area falls in 

this zone. Even if this zone is not totally 

suitable, it should not be avoided because it has 

less probability of landslide occurrence as 

compared to others. 

High Hazard Zone 

The maximum area of the study area is covered 

by high hazard zone which accounts about 58 % 

of the study area. This zone represents high 

susceptibility to landslide hazard as compared to 

moderate hazard zone.  Some of the inventoried 

landslides are known to occur in this zone. High 

hazard zone is mostly dispersed in Northern, 

Eastern and Southern parts of the study area. 

Out of 60 slope facets, 35 are categorized under 

this zone. Part of the town Debre Sina and 

Armania also fall in High hazard zone. Those 

slopes falling in this zone should be partially 

avoided or detailed study on larger scale 

(1:1000) should be done to evaluate the status of 

stability of these slopes. Suitable control 

measures should also be identified before taking 

up constructions in order to minimize related 

geo-environmental hazards. 

Very High Hazard Zone 

This zone represents totally unsuitable areas for 

constructions and settlement as well as 

agricultural activities. It covers the least area 

coverage and accounts 17 % of the study area. 

Because of very high susceptibility of landslide 

occurrence in very high hazard zone, it is not 

advisable and should be totally avoided. About 

10 facets of the study area have been identified 

to be very susceptible to landslide hazard. 

Among these, very high hazard zones are 

located in North-Western, Central and South-

Eastern part of the study area. Part of town 

Armania also falls in this zone. 

Validation of SSEP Results 

The results obtained in present study correlated 

with the past landslide events recorded in the 

study area. Thus, the final Landslide Hazard 

Zonation map has been checked against the 

inventoried landslides of the study area for its 

validity. Landslide inventory map of the study 

area has been prepared by integrating field 

observation and GPS data collection with some 

ideas obtained by interviewing local people 

living around the study area. Most of the 

inventory landslides are concentrated in high 

and very high hazard zones. Out of 36 landslide 

inventories prepared during the field visit, 22 

(61 %) of them fall in high hazard zone while 



East Afr. J. Biophys. Comput. Sci. (2022), Vol. 3, No. 1, 23-42 
 

40 

 

the remaining 14 (39 %) fall in very high hazard 

zone. The methodology followed during the 

present study relates intrinsic and external 

landslide triggering parameters to landslide 

occurrences. It has produced the results that 

match to past landslides. Thus, Slope Stability 

Evaluation Parameter (SSEP) rating scheme is 

found to be suitable methodology in landslide 

hazard zonation as it validated with the past 

landslide hazard events.  

 

 

 

 

CONCLUSIONS  

The present study area has been affected by 

landslide hazard which devastated both public 

as well as private properties including 

infrastructures. It also threatened the life of 

people and animals living around the study area. 

Landslide hazard and fatalities have been 

reported in the study area for a long period of 

time. Several landslide hazard assessment 

approaches are available based on the kind of 

data input, study area size, data availability, 

kind of topography, etc., each of these 

approaches has its own benefits and 

shortcomings. Concerning slope susceptibility 

in the present study area, areas covered with 

Figure 6. A map showing Evaluated landslide hazard of the study area.



East Afr. J. Biophys. Comput. Sci. (2022), Vol. 3, No. 1, 23-42 
 

41 

 

bare land, shrub and urban classes are more 

vulnerable to landslides, as compared to 

vegetation cover and forest classes which 

disfavor landslides. North, Northeast and West 

facing slopes are favorable to landslides, 

whereas South, Southwest and West orientation 

disfavor slope failure. Moreover, slopes inclined 

at greater than 25° angle have strong 

susceptibility to landslides. Distance to streams 

also has strong relations with landslide 

occurrence because areas close to streams prove 

to be highly prone to slope instability suggesting 

that slope undercutting by stream is an 

important process. Most of the recent landslides 

observed during field visit and delineated 

through inventory mapping fall into high and 

very high hazard classes suggesting the 

reliability of the SSEP rating scheme in 

delineating landslide prone areas of 

mountainous terrains. This approach is found to 

be more effective as it heavily depends on 

realistic field data andhelps to map landslides in 

large areas in a short period. 

Acknowledgements 

The author is grateful to all who have 

contributed in one or other ways during office 

and fieldwork. Specifically, I would like to 

express heart-felt thanks to Tarun Raghuvanshi 

(Associate Professor of Engineering Geology) 

for all academic inputs. I also appreciate Addis 

Ababa University, Department of Geology for 

providing all facilities including financial 

coverages needed to accomplish the present 

study without major obstacles. My appreciation 

is also extended to Hawassa University, 

Department of Geology for creating favorable 

environment during publication process.  

 

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