2. VULNERABILITY ASSESSMENT OF SOIL EROSION USING GEOSPATIAL TECHNIQUES

ISSN 2321–8355 IJARSGG (2015) Vol.3, No.1, 9-21
Research Article
International Journal of Advancement in Remote Sensing, GIS and
Geography
VULNERABILITY ASSESSMENT OF SOIL
EROSION USING GEOSPATIAL TECHNIQUES-
A PILOT STUDY OF UPPER CATCHMENT OF
MARKANDA RIVER
Surjit Singh Saini*
, Ravinder Jangra & S.P Kaushik
Department of Geography, Kurukshetra University Kurukshetra, Haryana (India)-136119 (* saini.surjit@gmail.com)
(Published online: 15
th
January 2015)
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ABSTRACT: Soil erosion remains a major threat to the Shivalik region of sub Himalayan mountainous
environment. The time required for data collection and high cost of research, is the difficulty in identification
of area sensitive to water induced soil erosion by conventional methods. However, these problems can be solved
by the use of GIS based predictive models both at local and regional scale. The main objective of the study is to
assess the sites vulnerable to soil erosion based on multi-criteria evaluation (MCE) in the upper catchment of
Markanda River. The scope of present study is limited to identification of soil erosion sensitive sites. GIS is
used for derivation, integration, and spatial analysis of geographic layers of each theme. Analytical Hierarchy
Process (AHP) is used to calculate the weights of soil erosion influencing factors such as rainfall, vegetation,
slope, soils, drainage density and land use. Using AHP the weights derived for the factors are Rainfall
(31.93%), Drainage network (23.08%), Soil (17.72%), Slope (14.14%), Drainage Density (7.45%) and Land use
(5.68 %). It is observed that about 8 per cent of the total area of watershed is under severe risk of erosion,
around 60 per cent of watershed lies in high to very high risk of erosion and 33 per cent of area shows slight to
moderate risk of soil erosion. The modeling result is validated by field survey and interpretation of high
resolution satellite imagery. Ground verification of resulted sites revealed that there are various visual
indicators of erosional and depositional geomorphic features like sand point bars, cut bank erosion, abandoned
channel and siltation in agriculture fields and the ponds. Thus, the model’s result based on multi-criteria
evaluation in GIS proves that identification of sites vulnerable to soil erosion is pre-requisite. Such models
based soil erosion scenario maps are important in planning conservation and control measures for soil erosion
to prioritize the area according to severity of erosion.
KEYWORDS: Sand Point Bars, Multi-Criteria Evaluation (MCE), Analytical Hierarchy Process (AHP),
Vulnerability, Weights, Weighted Overlay.
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1. INTRODUCTION: Soil erosion is a complex process that physically takes place by the movement of soil particles
from a given site. Although soil erosion is remaining a global environmental crisis in the world but today due to
anthropogenic impact this problem threatens natural environment and also the survival of agrarian society.
Accelerated soil erosion has adverse economic and environmental impacts (1). Worldwide, each year, about 75 billion
tons of soil is eroded from the land-a rate that is about 13-40 times as fast as the natural rate of erosion (2). Asia has
the highest soil erosion rate of 74 ton/acre/year (3) and Asian rivers contribute about 80 % per cent of the total
sediments delivered to the world oceans and amongst these Himalayan rivers are the major contributors, contributing
up to 50% of the total world river sediment flux (4). The alarming facts figured out that in India about 5334 MT (16.4
ton/hectare) of soil is detached annually, about 29% is carried away by the rivers into the sea and 10% is deposited in
reservoirs resulting in the considerable loss of the storage capacity (5). In India, NRSA and NBSS&LUP estimated
that the extent of area under water erosion is 23.62 M ha. (6). Soil erosion persist a major land degradation problem in
Shivalik region of sub-Himalayan mountainous environment. Shiwalik environment is considered the most fragile
ecosystem in the country (7). Shiwalik are comprised of sandstone, grit, and conglomerates, with characters of fluvial
deposits with deep soils, but slopes near the foothills contain pebbles and boulders and these formations are
geologically weak and unstable. Therefore, these areas are highly vulnerable to soil erosion and it is estimated that the
annual rate of soil erosion is more than 15-20 tons /ha/year in Shiwalik region (7). Due to youthful stage of rivers /
ephemeral streams and a good amount of rainfall cause highly dissected topography. Weathering and denudation have
produced a variety of erosional landform features such as rills, gullies, scarps and variously shaped ridges (8). The

Saini, et.al. / International Journal of Advancement in Remote Sensing, GIS and Geography, Vol.2, No. 2 10
combined impact of these factors leads to continuous depletion in the fertility and productivity of soil as well as
deterioration in the water quality (9).
In this present study, about 363 sq.km (61%), of the total catchment area comprised Shiwalik Hills. These
hills are highly degraded with very little forest cover. As a result of natural factors such as high rainfall, the fragile
topography, steep slope along with anthropogenic activities like deforestation, large-scale road and industrial
construction and mining, heavy soil erosion takes place mainly during monsoons. Several watershed development
programmes such as Integrated Watershed Management Programme (IWMP) and National Watershed Development
Project for Rainfed Areas (NWDPRA) are being implemented by State and Central Governments with local
community participation to conserve soil against erosion and to improve the soil fertility for sustainable development
of natural resources (10).Since the Soil erosion is a very dynamic spatio-temporal phenomenon so, the information of
the area vulnerable to erosion and its severity are pre-requisites for soil conservation planning and watershed
management at local and regional scale. When soil erosion problem is wide or regional, the conventional method of
mapping and field survey are expensive and time consuming. Therefore, in practice, such areas should be prioritized
based on severity of risk (very severe erosion- slight erosion) before undertaking them for conservation planning.
Hence, erosion models can be used as predictive tools for assessing soil loss and soil erosion risk for conservation
planning (11). In addition to time and financial constraints, Conventional methods are having limitations because of
difficulty in result based generalization. The accuracy is likely to be poor; and it is not known where the soil came
from and when (12). It is easy to monitor and measure the suspended sediment, the discharge and sediment yield
passing a point at the outlet of a catchment, but the origins and extent of the eroded sediment load from the catchment
itself remain unknown. Similarly, the eroded soil deposited at other locations without reaching the gauging station
cannot be computed. Hence, based on these experimental outputs for quantification of soil loss from diversified area at
regional scale is still disputable (12).
1.1. MODELING SOIL EROSION: Soil erosion prediction and assessment has been a challenge to researchers since
the 1930s’ and several empirical (statistical/metric), conceptual (semi-empirical) and physical process based
(deterministic) models have been designed for specific set of conditions of particular area (13).
Most of these models need information related with soil type, land use, landform, climate and topography to
estimate soil loss. Some of the worldwide known models are Universal Soil Loss Equation (USLE), Revised Universal
Soil Loss Equation (RUSLE), Morgan and Morgan–Finney model. These models are used, modified and improved
within years of research for quantitative and qualitative evaluation of soil erosion by water is presented in literature
[(14)-(19)].The USLE/RUSLE model gave results useful for a general estimation of the erosion phenomena. However,
the outputs are strictly dependent to the single parameter estimation and the model does not permit the simulation of
the erosive rainfall events (20).
With the advent of remote sensing and GIS technologies their integration with the USLE/RUSLE method led
to a more simpler, cost-effective and efficient perception of erosion, and this integrated application was applied by
many researchers in the whole world. The prime input required for soil erosion modeling are terrain, slope gradient
and slope length which can be generated by processing of DEM in GIS. Multi-temporal remote sensing data (satellite
imageries) provide valuable information related to seasonal land use dynamics. Satellite data can be used for
derivation of erosional and depositional features, such as gullies, point bar, braided channel, abandoned channel, and
vegetation cover factor (NDVI) (21). Several studies presented the potential of GIS technique for quantitatively
assessing soil erosion hazard based on various models [(22)-(27)].
1.2. GIS BASED MULTI-CRITERIA EVALUATION (MCE) APPROACH: One of the most important
applications of GIS is the 2-D and 3-D visualization and spatial analysis of geographic data to support the process of
environmental decision-making. A decision can be defined as a choice between alternatives, where the alternatives
may be different actions, locations, objects, and the like. Since 80 per cent of data used by decision makers is location
based, so with the capability of spatial analysis, GIS can provide better information about decision making situations
(28). GIS allows the decision maker to identify a list meeting a pre-defined set of criteria with the overlay process
(29). GIS based Multi-criteria evaluation (MCE) methods have been applied in several studies [(30)-(31)-(32)-(33)-
(34)-(35)-(36)].
In GIS multi-criteria evaluation has most typically been approached in two ways. In the first, all criteria are
allowed to Boolean (i.e logical true/false) statement of suitability for the decision under consideration. However,
problems have been noted with methods for site selection and resource evaluation that rely on classical Boolean logic
(37). In situations where the threshold value is not precise, loss of information may occur. Furthermore, the method
does not offer any analytical possibility for examining which of the areas fulfilling the criteria are the most appropriate
for the purpose in question. Because of problems with Boolean overlay, multi-criteria evaluation (MCE) methods have
been applied instead of Boolean logic (38). An index model is similar to a binary model in that both involve multi-
criteria evaluation for suitability analysis and vulnerability analysis. An index model produces for each unit area an
index value rather than a simple yes or no. The primary consideration in developing an index model, either vector-or-
raster based, is the method for computing the index value. The weighted linear combination method is probably the

most common method for computing the index value for each unit area and produces a ranked map based on the index
values (39).
The formula to count index value is as follow: With Ii is the index value, n is the criterion, W is the weight
and X is the standard value.
Basically there are 3 steps in calculation of index value:
Step 1: Evaluate relative importance of each criterion against the other criterion or weighting.
Step 2: Standardize the data for each criterion
Step 3: Calculating index value by sum up the multiplication result between weighting with standard value
Analytical Hierarchy Process (39) is one of the most popular methods for calculating criteria weights in MCE
via an expert pair-wise comparison matrix using their weights. Siddiqui et. al. presented an additive approach to a
spatial problem based on the AHP (40). Rao et. al. have suggested that for the development of criteria weights, the
procedure of pairwise comparison in AHP is a logical process (41). It has been shown that the weighted linear
combination operator commonly used with such factors lies on a continuum with these operators, where it represents
the case of intermediate AND and OR (Boolean logic), and full trade-off between the factors considered. However, in
spite of some uncertainties, several studies have compiled the AHP success stories in various fields. These studies
acknowledged the AHP approach combined with weighted linear combination in GIS for strong theoretical framework
as well as provides logic for the standardization of factors, a rationale for the expression of decision risk, and a high
degree of flexibility in the site suitability and vulnerability assessment [(42)-(43)-(44)-(45)-(46)]. According to
Burrough and McDonnell (48), the most important factors affecting the quality of spatial data are completeness,
consistency, accessibility, accuracy, precision and process method. Data quality is often described by thematic
accuracy, positional and temporal accuracy which affects the results of analyses.
With the above theoretical background, the present study performed MCE in GIS using integration of AHP
and weighted linear combination approach to identify sites/area vulnerable to soil erosion using some effective factors
causing soil erosion such as rainfall, soil, vegetation, slope, drainage network and land use. This study is undertaken
after the monsoon period of year 2014.
2. STUDY AREA: The Upper Catchment of Markanda River, a major tributary of Ghaggar River is selected for the
present study. The study area lies between 76˚ 6′19” to 77˚ 22′ 56” E longitudes and 30˚ 18′ 52” to 30˚ 41′18” N
latitudes (Fig.I). The total study area is 593 km2
in which about 53 per cent falling in part of Sirmaur district in the
state of Himachal Pradesh and about 47 per cent area coming in Haryana state covering part of Yamunanagar and
Ambala districts. Markanda River originates from the southern face of the lower Himalayas on the western ranges of
the Kiarda Dun or Paonta valley in the Nahan area of the Sirmaur district in Himachal Pradesh. It is a rain fed river
and has extremely low flow in the winter and summer months, but rises suddenly during monsoon. The Markanda
river flow through Shivalik hills till inter-state border before entering the plains of Haryana. The main river flows in
south-west direction till it merges with Ghaggar River. Sarasvati, which is a major tributary join the main Markanda
River near outlet of the study area at Zaffarpur village of Barara block in Ambala district.
The elevation within this catchment varies between 200m to 1500m above mean sea level. The slope of
general terrain varies from very steep (North) to very gentle (South). Land use / land cover analysis revealed that 49 %
area of watershed are under cultivation followed by 41 % of area under hill forest and scrubs of varying thickness,
water bodies (4%), open vegetation (4%) and settlement ( 2%). The topography of the catchment in north-east side is
controlled by rolling and dissected hills, comprising about 60 per cent of the total catchment area with an elevation
range between 400m to 1500m. Dissected topography created by ephemeral streams, weathering and denudation have
produced a variety of erosional landform features such as rills, gullies, scarps and variously shaped ridges. The
southern part of the study area is mainly alluvial plain, which forms a part of vast Indo-Gangetic alluvial plain. The
study area belongs to sub-tropical climate. About 80 per cent annual rainfall concentrated during monsoon period
(June-September). Despite heavy rainfall in this area, water retention is very low. It is due to high surface run off
causing erosion in the agriculture fields. Some area comes under temporarily water logged during flood season of
Markanda River. The rainwater from upper lands located at hill slopes passes through the farm lands and forms a
network of shallow and deep gullies which keep on widening and deepening. These gullies not only damage the lands
located along their banks but are source of debris which is carried down and deposited in gully beds and cause
meandering patterns, again a cause of bank erosion source (49).

Fig.I: Study Area
3. DATABASE & METHODOLOGY: Present study is based on both primary and secondary data sources.
Primary data involved sample survey of geomorphic features sites (erosion, deposition) using handheld Global
Position System (GPS) instrument. Secondary data is collected from concerned public department, published report
and internet. The sequence of procedures followed for the assessment of vulnerable areas to soil erosion is
schematically shown in Fig. II.
Fig.II Methodology
3.1. PRODUCING GIS LAYERS OF INFLUENCING FACTORS: Initially, GIS maps are created for each factor
in common geo-referencing scheme. Rainfall and soil maps are prepared using data collected from district wise report
on ground water information published by Central Ground Water Board (50). Drainage network and slope maps are

generated from Digital Elevation Model (DEM) produced using digitized contours (20m) from SOI toposheets.
Drainage density (Dd) is measured, where Dd= L/Ad, the total length of all channels (L) divided by the area of the
drainage catchment. TM Landsat (30m) imagery of the year 2009 downloaded from GLCF is used for vegetation layer
and Normalized Difference Vegetation Index (NDVI) expressed as NDVI= (NIR-VIS)/ (NIR+VIS) is prepared using
Erdas Imagine 2010 software. Land use layer is also produced using same satellite imagery later on updated with
imagery of the year 2011 available on Google earth. After producing GIS layers of each parameter, all layers are
converted to an integer raster maps in grid format having the same pixel size (30m).
3.2. STATISTICAL TECHNIQUE AND DATA ANALYSIS: To assess the sensitivity to soil erosion multi- criteria
analysis is applied in producing and combining spatial data for describing the causal factors. AHP Pairwise
Comparison Methods is used through Weighted Linear Combination (WLC) Approach in GIS environment.
3.3. EXPLANATION AND RANKING OF INFLUENCING FACTORS: Each factor under consideration is
ranked in the order of the decision maker’s preference. To generate criterion values for each sub-class evaluation unit,
each factor is ranked according to the estimated significance influence on soil erosion. The inverse ranking was
applied to these factors. Based on the literature review, knowledge and field experience, each sub-class is ranked 1-5
in decreasing order of impact, where 5 indicates high sensitivity and 1 indicates low sensitivity to soil erosion. The
ranking scheme is shown in Table I. The present study assume that, the sites/locations vulnerable to soil erosion
depends on multiple factors such as basin size, topography, rainfall amount, vegetation, soil types and land use
practices. Therefore, with respect to these factors, soil erosion varies considerably from time to time and place to
place. The present study used the following six factors and each one is presented and stored in separate layer with their
sub-class rank attached as an attribute as shown in Fig.III.
Fig.III: Criterion Maps and their Sub-Class Ranking
3.3.1. RAINFALL: Since soil erosion generally occurs when the soil is displaced by rain and transported from the
specific area, therefore rainfall is considered as the major driving factor of soil erosion. High rainfall amount is
indicative of significant soil loss hence where the rainfall is more than annual average, chance of erosion will be more.
Therefore, class weightage of 3 is assigned to high rainfall zone in the study area and 1 for relatively less rainfall zone
(Table I).

3.3.2. VEGETAL COVER: The factor that significantly affects the soil displacement by rain is vegetation cover.
The reduction of vegetation cover can increase soil erosion. This relationship is a reason why vegetation cover and
land use have been widely included in soil erosion studies (51). Where the top soil is open and covered with poor
vegetation, chances of erosion will be more hence, maximum class weightage of 5 is assigned to poor vegetation or
barren land and less weightage to subsequent classes in order of increasing density of vegetation.
3.3.3. SOIL TYPE: Soil map is ranked according to the infiltration/retaining characteristics of the soil type. The
zone in the study area having soil with low retaining property produced high runoff causing high soil erosion. Thus
higher rank is tagged to the zone with low retaining capacity and vice-versa.
3.3.4. SLOPE: Slope plays a major role in erosion control. Generally, wherever steeper the slope, chance of soil
erosion will be high. Therefore, class rank of 5 is assigned to very steep slope areas and subsequently low ranking to
areas according to decreasing magnitude of slope.
3.3.5. DRAINAGE DENSITY: It can be summarized that the length of streams and channels in an area can also be
considered as an index to describe soil erodibility. Although their precise relationship has not been established, there is
agreement that drainage in an area can be considered as an index of soil erodibility (52). The critical value of drainage
density per square km that may cause soil erosion by water is 0.90 km per square km of area (53). Therefore, drainage
density higher than this critical value will automatically make for greater soil erosion. Hence, higher drainage density
areas are assigned higher rank and vice versa.
3.3.6. LAND USE: Different land use types in terms of area size and pattern influenced the soil erosion risk. The
area with smaller land cover obviously has the higher risk of soil erosion than the larger land cover did. Therefore,
depending on type of land use and its vulnerability to soil erosion ranks are assigned as shown in Table I.
Thus, result will be determined on the combination of all the causative factors. All the rating classes will be multiplied
by their weights calculated using AHP method. This weight is a relative percent-age, and the sum of the percentage
influence weights for all the raster maps will be equal to 100.
Erosion Parameters Sub-class of Parameters Ranking
1.Rainfall More than – 1200 mm 3
1101 mm – 1200 mm 2
1000 mm – 1100 mm 1
2. Vegetation cover Very less vegetation cover 5
Less vegetation cover 4
Moderate vegetation cover 3
High vegetation cover 2
Very high vegetation cover 1
3. Soil Eurtrochrepts/ Udorthents (shallow & loamy) 3
Udipsamments/Udorthents (loamy sand to sandy loam) 2
Udipsamments/Udorthents ( sandy loam to clayey loam) 1
4. Slope Very Steep (>40%) 5
Steep (30.1-40%) 4
Moderate (20.1-30%) 3
Gentle (10.1-20%) 2
Very Gentle (<10%) 1
5. Drainage Density >6 km/sq.km 5
5.1-6.0 km/sq.km 4
4.1-5.0 km/sq.km 3
2.1-4.0 km/sq.km 2
<2 km/sq.km 1
6.Landuse Agriculture 5
Sparse vegetation 4
Forest 3
Water bodies 2
Built-up 1
Table. I: Ranking of Influencing Factors and their Sub-Classes
4.1.DETERMINING THE WEIGHTS OF THE FACTORS USING AHP: Assigning weights to factors is
complex decision problems that involve multiple criterion function. In such a situation, mis-perception can arise if a
logical, well-structured decision-making process is not followed. The MCE methodology can be objectively solve
complex decision problem with multiple criteria. The present study used the AHP method introduced by Saaty (54).
AHP is widely accepted statistical and is a very popular means to calculate the needed weighting factors with the help
of a preference matrix where all identified relevant criteria are compared against each other with reproducible

preference factors. All factors which are considered relevant for a decision are compared against each other in a pair-
wise comparison matrix which is a measure to express the relative preference among the factors. Therefore, numerical
values expressing a judgment of the relative importance of one factor against another have to be assigned to each
factor. Since it is known from psychological studies that an individual cannot simultaneously compare more than 7 ± 2
elements, Saaty & Vargas (55) suggested a scale for comparison consisting of values ranging from 1 to 9 which
describe the intensity of importance (preference/dominance). A value of 1 expresses “equal importance” and a value
of 9 are given for those factors having an “extreme importance” over another factor (Table II).
Intensity of Importance Description
1 Equal importance
2 Equal to moderate importance
3 Moderate importance
4 Moderate to strong importance
5 Strong importance
6 Strong to very strong importance
7 Very strong importance
8 Very to extremely strong importance
9 Extreme importance
Reciprocals Values for Inverse Comparison
Table.II: Example scale for comparison
4.2. DEVELOPMENT OF PAIRWISE COMPARISON MATRIX: The Table III shows a pairwise comparison
matrix of order 6 where 6 criteria (C1, C2, C3, C4, and C5 and C6) are compared against each other. In the direct
comparison of the criteria C1 and C2, criterion C1 is regarded equal to moderate importance and similarly relative
importance are assigned to the remaining criterion. The transpose position automatically gets a value of the reciprocal;
it is 1/4 which equals 0.25.
Criteria Rainfall Vegetal
Cover
Soil Slope Drainage
Density
Land Use
C1 C2 C3 C4 C5 C6
Rainfall C1 1.00 2.00 3.00 2.00 4.00 4.00
Vegetal Cover C2 0.50 1.00 2.00 3.00 4.00 2.00
Soil C3 0.33 0.50 1.00 2.00 3.00 5.00
Slope C4 0.50 0.33 0.50 1.00 4.00 3.00
Drainage Density C5 0.25 0.25 0.33 0.25 1.00 3.00
Land Use C6 0.25 0.50 0.20 0.33 0.33 1.00
Total 2.83 4.58 7.03 8.58 16.33 18.00
Table.III Development of Pairwise Comparison Matrix
4.3. NORMALIZED PAIRWISE COMPARISON MATRIX: In the next step, the assigned preference values are
synthesized to determine a numerical value which is equivalent to the weights of the factors. Therefore, the eigen
values and eigen vectors of the square preference matrix revealing important details about patterns in the data matrix
are calculated. The above square matrix of order six gives six eigen values with which six eigen vectors - each having
six vector components - can be calculated. It is regarded sufficient to calculate only the eigen vector resulting from the
largest eigen value since this eigen vector contains enough information to provide – by its eigen vector components -
the relative priorities of the factors being considered (55). The pair-wise matrix is normalized and the eigen values of
the normalized matrix, which represent the parameter weights, are computed as sown in Table IV.
riteria
Rainfall Vegetal
Cover
Soil Slope Drainage
Density
Land
Use
Row
Total
(C1 to C6)
Priority
Vector
(Row Sum/ 6)
Weight
(%)
C1 C2 C3 C4 C5 C6
Rainfall C1 0.35 0.44 0.43 0.23 0.24 0.22 1.92 0.32 31.93
Vegetal Cover C2 0.18 0.22 0.28 0.35 0.24 0.11 1.38 0.23 23.08
Soil C3 0.12 0.11 0.14 0.23 0.18 0.28 1.06 0.18 17.72
Slope C4 0.18 0.07 0.07 0.12 0.24 0.17 0.85 0.14 14.14
Drainage Density C5 0.09 0.05 0.05 0.03 0.06 0.17 0.45 0.07 7.45
Land Use C6 0.09 0.11 0.03 0.04 0.02 0.06 0.34 0.06 5.68
Total 1.00 1.00 1.00 1.00 1.00 1.00 6.00 1.00 100.00
Table.IV Normalized Pairwise Comparison Matrix
4.4. CALCULATING CONSISTENCY RATIO (CR): At this stage the consistency ratio (CR) is calculated to
measure how consistent the judgments have been relative to large samples of purely random judgments. The AHP
always allows for some level of inconsistencies which should not exceed a certain threshold (56). The Random
inconsistency indices (RI) (Table V) developed by Saaty is used to determine the consistency ratio (CR), which
measures the degree of consistency. If the value of CR is smaller or equal to 0.1, the inconsistency is acceptable or
else the pair-wise comparison may be revised if the CR is much in excess of 0.1 the judgments are unreliable because
they are too close for comfort to randomness. (56). Therefore, the weights can be accepted.

n 2 3 4 5 6 7 8 9 10
RI 0.00 0.52 0.90 1.12 1.24 1.32 1.41 1.45 1.49
Table.V: Random Indices for matrices of various sizes (Saaty & Vargas,1991)
CR= CI/RI. Where CI= max-n/n-1, RI=Random consistency index, N=Number of criteria, max is priority vector
multiplied by each column total.
C1 C2 C3 C4 C5 C6
Criteria
weight
weight sum vector/
criteria weight
Consistency
Vector
C1 Rainfall 1.00 2.00 3.00 2.00 4.00 4.00 0.32 2.12 6.64
C2 Vegetal Cover 0.50 1.00 2.00 3.00 4.00 2.00 0.23 1.58 6.85
C3 Soil 0.33 0.50 1.00 2.00 3.00 5.00 0.18 1.19 6.71
C4 Slope 0.50 0.33 0.50 1.00 4.00 3.00 0.14 0.93 6.61
C5 Drainage Density 0.25 0.25 0.33 0.25 1.00 3.00 0.07 0.48 6.40
C6 Land Use 0.25 0.50 0.20 0.33 0.33 1.00 0.06 0.36 6.33
Total 1.00 1.00 1.00 1.00 1.00 1.00 1.00 39.54
=39.54/6=6.59
Table.VI: Calculating Consistency Ratio (CR)
= 6.59
n (no. of criteria)= 6
CI= -n/n-1= 0.59
RI = 1.240
Consistency Ratio= 0.0952
An analysis for inconsistencies is performed and the value of CR= 0.0952 falls little below the threshold value of 0.1
and it indicates a high level of consistency. Therefore, therefore, the weights can be accepted.
5. RESULT AND DISCUSSION:
5.1.SENSITIVITY ANALYSIS: Based on AHP method weights are calculated in per cent as 31.93, 23.08, 17.72,
14.14, 7.45, 5.68 respectively for annual rainfall, vegetal cover, soils, slope, drainage density and land use of the
catchment and consistency ratio (CR) is found as 0.0952. This indicated a reasonable level of consistency in the
pairwise comparison of the factors. Raster layer in grid format of each parameter is multiplied by their given weight
and summing them together using arithmetic weighted sum overlay tool in Arc GIS 10 software. The resultant
composite values are obtained in the range of 124 to 354 (Fig.IV), where less value indicates low sensitivity and high
value indicates high sensitivity to soil erosion. Using Standard Deviation (SD) statistical method, this combined map
is re-classified into five classes as severe, very high, high, medium and slight as shown in Fig. V. The area liable to
soil erosion is calculated corresponding to each category as 7.13% (severe erosion), 26 % (very high), 34 % (high),
28.86% (moderate) and 4% area vulnerable to slight erosion in the catchment (Table. VII). Based on combined
weighted factors taken into account, the results of the WLC operation revealed that, the areas that come under very
high to severe erosion categories are primarily located in the north and middle part of the catchment. In contrast,
relatively area under slight to moderate erosion is located to the southwest of the catchment.
Sl No. Erosion Sensitivity Class Area (Sq.km) Area (%)
1 Slight Erosion 23.77 4.01
2 Moderate Erosion 171.17 28.86
3 High Erosion 201.65 34.00
4 Very High Erosion 154.10 25.99
5 Severe Erosion 42.31 7.13
Total 593.00 100.00
Table. VII: Area under Soil Erosion Vulnerability

Fig.IV: Weighted Sum Overlay based Soil Erosion Sensitivity Index
Fig. V: SD based Reclassified Weighted Sum Map for Soil Erosion Sensitivity

5.2. VALIDATION OF STUDY: Since present study scope was limited to identify the soil erosion sensitive sites
therefore, in order to validate the model predicted results, interpretation of high resolution satellite imagery and field
surveys are conducted. During field visit, with the help of resulted map, vulnerable soil erosion sites have been cross-
checked by selected samples from each erosion category. With the help of hand held GPS the geographic coordinates
of each sample has taken and later on imported in GIS and super-imposed on weighted sum map. Ground verification
of resulted sites (Fig.VI & VII) revealed that there are various visual indicators of erosional and depositional
geomorphic features like sand point bars, cut bank erosion, abandoned channel and siltation in agriculture fields etc.
Thus, the modeled indicative range against each erosion severity class can be generalized to map the extent &
location.
Fig. VI: Validation of Modeled Result in field

Fig. VI: Validation of Modeled Result using High Resolution Satellite Imagery
5. CONCLUSION: As mentioned earlier that monitoring and measuring of the suspended sediment passing a point at
the outlet of a catchment is not difficult but the origins and extent of the eroded sediment load from the catchment
itself remain unknown. Similarly, the eroded soil deposited at other locations without reaching the gauging station
cannot be computed. Hence, based on these experimental outputs for quantification of soil loss from diversified areas
at regional scale is still put question mark. The present study focused on identification of origin and extent of soil
erosion vulnerable sites rather than quantification of soil erosion. This study demonstrates that geo-spatial techniques
are indeed valuable tools in assessment and mapping of areas vulnerable to soil erosion hazard. Identification of sites
vulnerable to soil loss in the study area is modeled by performing MCE in GIS framework. Modeled result indicates or
shows that about 8 per cent of total area of watershed is found under severe risk of erosion. Around 60 per cent of
watershed lies in high to very high risk of erosion and 33 per cent of area shows slight to moderate risk of soil erosion.
The modeled predicted sites vulnerable to soil erosion are validated by comparing with the actual current erosion and
depositional features found in the field. Field survey revealed that, terrace agriculture field and bared hill patches in
the Shiwalik part of watershed, are more vulnerable to soil erosion. It is found that there are various eroded and

deposition geomorphic features in the downstream side of the watershed. These features include sand point bar, sand
layer in agriculture fields, cut bank erosion and the like exposed that vulnerability to soil erosion risk is more in the
high to steep slope area (north) of the catchment. Thus, the modeled result based on multi-criteria evaluation in GIS
proves that identification of sites vulnerable to soil erosion is pre-requisite. Such models based integrated maps can
help us better understand the causes, make predictions, and plan how to implement preventative and restorative
strategies and to prioritize the area according to severity of erosion. Conventional methods for identifying the erosion
potential area are based on physical survey but in practices when erosion problem is very wide it is a difficult task and
time consuming. Therefore, GIS based spatial modeling produce useful information for solving complex problems by
identifying relationship among various dependent geographic features clearly and logically. However, further research
is needed firstly, to incorporate more number of factors in addition to physical/natural like agriculture practices,
irrigation facilities and ongoing soil & water conservation programmes. Secondly, there is need to improve the robust
multi-criteria evaluation method to minimize the uncertainties. The integration of such methods in a GIS has the
potential to enhance its analytical strength.
ACKNOWLEDGEMENT: This paper is extracted from the ongoing research (Ph.D.) work entitled “Risk and
Vulnerability Assessment of Flood Hazard in Ghaggar River Basin” funded by University Grant Commission (UGC)
under Major Research Project. So, Authors are sincerely acknowledging the financial support of UGC and also thanks
for intermittently technical and conceptual supports provided by the faculty of Department of Geography,
Kurukshetra University, Kurukshetra, Haryana-India.
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