Journal of Global Resources
doi.org/10.46587/JGR.2020.v06i02.017

Volume 6 (02)

July 2020
Page 123-133
ISSN: 2395-3160 (Print), 2455-2445 (Online)

17
PRIORITIZATION OF WATERSHED BASED ON SOIL EROSION MODEL: A CASE STUDY
OF NOYYAL RIVER BASIN, TAMIL NADU
Panhalkar Bilal Dastagir1, Gaikwad Vishal Vilas1 and M. Sakthivel2
1
Research Scholars, 2Associate Professor,
Dept. of Geography, University of Madras, Chennai, India
Email:bilalpanhalkar777@gmail.com
Abstract: Soil Erosion is a universal problem as it poses great risk to cultivable land. Erosion ofsoil is more prominent along the regions adjacent to water bodies and in hilly areas. Present
study is carried out in Noyyal River Basin, Tamil Nadu which covers 3,514 square kilometers ofland. The study area lies between 10° 54’ 04” N to 11° 20’ 13” N and longitude of 76 ° 36’ 08” E
to 77°56’ 16’’ E latitude. Average annual soil loss for the study area is calculated using 5
parameters of Rainfall Erosivity Factor (R), Soil Erodibility Factor (K), Slope Length andSteepness Factor (LS), Cover Management Factor (C) and Conservation Practice Factor (P)using Revised Universal Soil Loss Equation (RUSLE) Model. The soil erosion risk factor wasclassified into 4 risk classes of slight (0-0.5 ton/ha/year), Moderate (0.5-10 ton/ha/year), High
(10-80 ton/ha/year) and Very High (>80 ton/ha/year). It was found that majority of the study area
falls under the category of slight erosion and moderate erosion. The result of Annual Soil Lossobtained through GIS platform is further analyzed for prioritization of watersheds. The entire
river basin was divided in 18 Sub-Watersheds. These watersheds are classified into Low (0-80
ton/ha/year), Moderate (80-180 ton/ha/year) and High (>180 ton/ha/year) Priorities based on themean annual soil loss of the particular watershed.
Key words: Soil Erosion Model, P-Factor, LU/LC, Prioritization of Watershed.
Introduction
Soil is the uppermost layer of the earth crust, consisting weathered rocks and minerals withorganic substances and forms one of the most vital resource for the existence of life on theearth. Soil, although being one of vital resource is highly prone to water erosion. Soil and waterinteract with each other in various phases, most important being the hydrological cycle. Whensoil and water interact in hydrological cycle the water detaches and carries away soil particles.
These particles get settled on the bed of the stream, thereby reducing its capacity which further
accelerates to floods and on the other side causes loss of rich fertile soil. Soil loss is deliberated
as a heavier real question as it incredibly threatens physical resources, agricultural, grassland,
forest and environmental. Soil loss is a process of variables parameters rainfall, runoff, close
relative element, soil features, terrain, plantation, and land cover. Soil loss is deeply fallen
because of bio- physical and socioeconomic factors, e.g., pasturage, agriculture system, andimpoverishment. (Narayan Kayet et.al, 2018). USLE model has predicted soil erosion throughthe water and RUSEL also fallow the same principle but contain a several improvement incalculation for various factor. Soil erosion is a vital and non-renewable resource in the earth but
in many region people did not consider soil erosion and land degradation problems for thatcauses vast area economically unproductive so that region consider to give more priority for
management and improve soil productivity. Geo information techniques widely use to assess
the soil loss intended for all the region and recently availability of high resolution data has used
to make a large scale inventory of Digital Elevation Model (DEM), land use/land cover (LULC)
and soil resource maps were effectively used to assessed soil erosion hazard (P. Karthick et. al,2017). The aim of the present study was, to determine the spatial distribution of annual soil loss
and to analyze the effect of Rainfall, land use cover, slope exposition and terrace farming on
soil erosion using RUSLE model and Geo-information technology.
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Study Area
The Noyyal River Basin is situated on the North and East Hemisphere located between latitudeof 10° 54’ 04” N to 11° 20’ 13” N and longitude of 76 ° 36’ 08” E to 77°56’ 16” E. The Noyyal
River is a small river in Western Tamil Nadu, and a tributary of Kaveri River. It rises from the
Vellingiri hills in the Western Ghats in Tamil Nadu, very close to Kerala border, and flows
through many villages and the cities of Coimbatore, and Tirupur finally draining into the Kaveri
River at Noyyal, a village in Karur district named after the river itself. The river's basin is 180 km.
long and 25 km wide and covers a total area of 3,514 sq. km. Cultivated land in the basinamounts to 1,800 sq. km. while the population density is 120 people per sq. km. in the
countryside, and 1000 people per sq. km. in the cities. The area is known for its scanty rainfall
and the development of the Noyyal River Tanks System to hold any overflow from the rains plus
the water of the Northeast and Southwest monsoon season was ecologically important. The
173 km. long tributary of the Kaveri River filled 32 tanks. These interconnecting tanks held thewater flowing from the Noyyal.
Figure 01: Location Map of Study Area

Objectives of the Study To Estimate of the average annual soil loss from the Noyyal River basin and its spatial
distribution.
 Prioritization of sub watersheds of Noyyal River basin based on degree of severity of soil
erosion for watershed treatment.
 To suggest specific conservation practices to minimize the soil erosion rate.
Materials and Methods
Table 01: Required Dataset for Soil Erosion Model#
1.

Datasets
Rainfall Data
(2000 To 2018)

2.

Soil data

3.

ASTER – DEM
Sentinel – 2A
(Satellite Image)4.

SourceState Ground & Surface Water
Resources Data Center, Taramani.
Soil and Land use
survey of India, Bangalore
https://earthexplorer.usgs.gov.
https://earthexplorer.usgs.gov.

124

Resolution/ Data types
Excel Format.shp & Excel Format
30 Meter
10 Meter

1. Rainfall Erosivity Factor (R)The soil loss is closely related to rainfall detaching power of raindrop striking the soil surface
which is the contribution of rain to runoff. The most suitable expression of the erosivity of rainfall
is an index based on kinetic energy of the rain. There are different ways of analyzing the R
factor. For instance,
R= 79 + 0.363*Xa … Eq. (1)Where Xa is Average Annual Rainfall (MM) [Chaudhary and Nayak, (2003)]
These formulas have been applied in different parts of the India. Whereas, the application forother countries are less satisfactory. Especially with the annual rainfall below 900 mm, like partof the study area, the equation yields estimates of erosivity, which are obviously meaningless.
2. Soil Erodibility factor (K)Soil erodibility factor (K) signifies the sensitivity of soil or surface components to erosion,transportability of the silt, and the quantity and amount of runoff assumed an individual rainfall
contribution as calculated in a standard condition. Soil texture is critical for hydrologic soil group
determination. Soil textures were classified by the fractions of each soil different present in thesoil. K parameter is determinate by various soil characteristic similar to the texture of soil orparticle size allocation, organic matter amount, penetrability, and environment of clay minerals,of which soil texture is the significant issue for standardized soil erodibility.
K= 27: 66 * M1.14*10-8*(12 - A) + (0.0043* B – 2) + (0.0033 * (C - 3)) …. Eq. (2)[Narayan Kayet et al. (2018)]
Where,
M = silt (%) + very fine sand (%) *(100-clay (%)),
A is organic matter (%),
B is structure code
1. Very structured or particulate,
2. relatively structured
3. Slightly formal
4. Solid.
C is profile permeability system rapid, moderate to rapid, moderates, moderate to slow, slow
and very slow.
3. Slope length and steepness (LS) Factor
LS factor depends on the slope length and steepness of the area and it has played an importantrole in soil erosion. They produce for topography on the top soil erosion and its containing thelength and steepness of the slope that persuade the surface runoff speed. The RUSLE
formulate that no separation among the rill and inter rill in the S factor that compute theconsequence of slope steepness in the soil loss. The study area LS factor was preparedthrough ASTER DEM (30 m Resolution) data its show in Figure-10. There are lots ofrelationships for the assessment of LS factor while the best suitable for intergraded with GIS
platform it is proposed by Moore et al.22. It’s based on the unit stream power theory that
equation as follows:
LS= (A/22.13)0.6 + (sin (B × 0.01745 /0.0896))1.3…. Eq. (3)Where
A is a slope length factor,
B is the slope steepness factor;
22.13 is the RUSLE unit plot length,
0.6 is the slope length
Exponent variable (generally it’s taken from respective grid).
A = (Flow accumulation × grid size)
B = Slope of DEM
125

The accuracy can be estimated through the digital elevation model (DEM) data resolution. Arc
GIS Raster calculator tool used to drive LS factor map it’s based on the slope steepness andflow accumulation.
4. Cover and Management (C) Factor
The cover and management factor are an important factor for reduction of soil erosion and it
depends on land use pattern of an area. The C factor is the ratio of soil loss from an area withprotective cover and management to soil loss in the continuous clean tilled fallow land. The
values of C factor vary from 0 to 1 based on land use land cover types without including areasof water body. In this study, Normalized Difference Vegetation Index (NDVI) was used to
calculate the C factor. The equation (4) for calculating the NDVI is:
𝑁𝐷𝑉𝐼 =NIR − RED
NIR + RED

Eq. (4)After prepared NDVI, following equation (5) has been used to create C factor map:
C = 1.20 - 1.21* NDVI
Eq. (5)[Asish Saha et al. (2018)]
5. Land Use / Land CoverLand use refers to human’s activities and various uses of land cover. Land cover refers tovegetation cover, water bodies, agricultural land, built-up land cover and others resulting due toland transformation. Although land use is generally inferred based on the cover, yet both theterms land use and land cover are closely related and interchangeable. Thematic land classes
can be derived digitally by grouping pixels that have similar spectral signatures from themeasurements of individual bands throughout the spectrum. Usually this classification is madewith visible, near-infrared, and middle infrared part of the spectrum. The Land Use/Land Coveris classified by supervised classification by using ArcGIS 10.3 software.
6. Conservation Practice Factor (P)The conservation practice factor (P) is the most important parameter in RUSLE method and it isa boundless factor defined P factor as the ratio of soil loss in a particular support practice to thecorresponding soil loss with up and down slope cultivation. P-value ranges from 0 to 1, wherethe value 0 indicates good erosion resistant facility made by man and the value 1 indicate anabsence of erosion resistant facility.
Results and Discussions
Rainfall Erosivity Factor (R)The rainfall intensity is the primary factor for estimating the Rainfall erosivity factor. Rainfall
erosivity factor indicates the regions of study area that are more prone to erosion due to rainfall.
The values of R factor are computed for a range of 206 to 383. 75percent of the region lie inmoderate rainfall erosivity regions while there are small pockets of land towards the westernregion of the study area that have high soil erosivity. Low level of rainfall erosivity is seen
towards the eastern region of the study area. Average R-factor for locations in Coimbatore
district is 294, Tiruppur district is 278. While the values for Erode and Karur districts are 254 and288 respectively. Thus, it is fine to say that rainfall intensity in Coimbatore district iscomparatively more than other districts; hence the R-factor values are also more.

126

Table 02: Station wise Annual Average Rainfall and R - Factor#

Station Name
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
17.
18.
19.
20.
21.
22.

Districts

Annur
Chithraichavadi Anicut
Coimbatore Airport
Agriculture College
CMC Medical College
Periyanayakkanpalayam
Pothanur railway
Sulur
Thiruppur
AvinashiKankeyamMangal Patti
PalladamPongalur
Sundakkampalayam
Arachalur
Chennimalai
Ichipalayam
Karur
K Paramathi(Karur)
Krishnarayapuram
Mayanur

Coimbatore
Coimbatore
Coimbatore
Coimbatore
Coimbatore
Coimbatore
Coimbatore
Coimbatore
Coimbatore
Tiruppur
Tiruppur
Tiruppur
Tiruppur
Tiruppur
Tiruppur
Erode
Erode
Erode
Karur
Karur
Karur
Karur

Annual Average
Rainfall (2000 to 2018)
481.4
724.1
588.1
590.4
838.6
576.3
519.6
477.9
554.9
665.1
543.4
349.9
582.4
463
689
366.7
626.3
478.7
577.7
610.5
483.8
635.5

R Factor
253.74341.86292.48293.32383.39288.18267.61
252.48280.43320.44276.25206.03290.42247.07329.11
212.11309.62252.76288.71300.62254.63309.7

Figure 02: Rainfall Erosivity Factor (R)Soil Erodibility Factor (K)Soil erodibility factor is the measure of potential erodibility of the soil depending on its inherent
soil properties. Soil texture, organic matter and permeability are all factors for computing the soil
erodibility factor. K factor values range from 0.004 to 0.35. The sandy clay loam soil has thehighest K factor of 0.35 followed by sandy loam and sandy clay of 0.14 and 0.12 values
respectively. The lowest K factor is computed for clay soil with a value of 0.004. Soils with low
clay content are less cohesive and are inherently more unstable, thus making them more
vulnerable to erosion through water and wind. Soil with high sand content is not cohesive andhas a high value risk of erosion. This is implied by the values obtained using the K-factor.
127

Figure 03: Soil Erodibility Factor (K)Figure 04: Slope Length and Slope

Table 03: K Factor Values
#
1.
2.
3.
4.
5.
6.
7.

Soil Texture
Clay
Loamy Sand
Rock Land
Clay Loam
Sandy Clay
Sandy Loam
Sandy Clay Loam

K Factor
0.004
0.04
0.08
0.11
0.12
0.14
0.35

Slope Length and Steepness (LS) Factor
LS factors include Slope Length and Slope Steepness Factor which mainly reflect the effect ofSurface Topography and slope characteristics on erosion by water action. The values of LS
factor lie between the range of 0 – 92. High values of LS factor are seen in the western regionof the map where there is high slope compared to others, indicating a hilly terrain. Thus, it canbe interpreted that the values of LS factor are more in the hilly terrain regions as erosion due towater is more in hilly regions or regions with high slope values. Higher the steepness of theslope, more the risk of erosion as the soil particles present in steep slopes are unstable and are
subjected to gravitational forces of earth.
Cover and Management (C) Factor
Cover and Management Factor are important for the reduction of soil erosion as it depends on
the land use pattern. The values of C factor range from 0.37-1.2 based on NDVI classes
excluding the water bodies in this study area. In this study, C factor is calculated using NDVI,Normalized Difference Vegetation Index. The C factor for the study area is low in the highly
vegetated regions while the values of C factor are high in the regions of non-vegetation or low
vegetation. Soil erosion is less in highly vegetated regions compared to other regions because
the roots of the vegetation hold the soil particles together making them less vulnerable toerosion by wind and water. Regions of low C factor are found in the western regions of the map;
possibly hilly terrain and the regions of high C factor are found in the eastern and central
regions of the study area. Thus, the vegetation cover is an important factor to analyse the soil
erosion.

128

Figure 05: NDVI Map

Figure 06: Cover Factor C

Conservation Practice Factor (P)P factor is used to find the areas with good conservation practices deployed by man andthe regions that further need steps taken for conservation of soil. The value for regionswith good conservation steps is 0.28 and found to be in the agricultural lands. Farmers
tend to safeguard the soil from erosion through the setting up of soil bunds etc.,
ensuring the erosion of soil is prevented to some extent. However, it is clear from thestudy that region apart from agricultural land misses the same efforts and conservation
steps. Forests, Fallow land, Built-up land, rock land all have the value for p-factor as 1,
indicating minimum efforts of man in conservation.
Figure 07: LU/LC Map (2018)

Figure 08: Conservation Practice Factor (P)Table 04: LU/ LC of Noyyal River Basin
#
1
2
3
4
5
6
7
8

LU/ LC
Dense Forest
Deciduous Forest
Built-up Land
Agricultural Land
Waste Land
Fallow Land
Waterbodies
Rock land
Total

Area (sq. km.)95.816212.56436.981609.491405.226
1486.11260.447206.658
3513.291

129

#
1
2
3
4
5
6
7
8

Table 05: P-Factor values
Land Use / Land CoverArea (sq. km.)Dense Forest
95.816
Deciduous Forest
212.56
Built-up Land436.981
Agricultural Land609.491
Waste Land405.226
Fallow Land1486.112
Waterbodies
60.447
Rock land
206.658

P - Factor
1
1
1
0.28
1
1
1
1

Average Annual Soil Loss (A)The R, K, LS, C and P Factors as elements of the RUSLE equation can be considered as
naturally occurring factors determining the sheet and rill erosion processes. Together, they canbe considered as the erosion susceptibility or potential erosion or soil loss (tons/hectare/year)
for the area.
Annual Soil Loss (A) = R * LS * K * C * P ….. Eq. (6)Figure 09: Potential Soil Erosion Area (2018)

The average soil loss is calculated multiplying all the factors and classified into regionsof Slight, Moderate, High and Very High regions. From the analysis, it is interpreted that the
majority of the regions in the Noyyal River Basin fall under the category of Slight Soil Erosion
Regions with Average Annual Soil Loss up to 0.5 tonnes/hectare. However, few regions
towards the western region of the study area that receives higher intensity of rainfall, higher
slope (hilly terrain), highly forested regions and regions with minimal conservation practice arefound to fall under Moderate Soil Erosion with average annual soil loss value up to 10
tonnes/hectare/year. High vegetation is one of the key factors in this region as it prevents
higher levels of soil erosion.
Prioritization of Watershed based on Soil Loss
The prioritization of watershed in terms of soil loss act as powerful tool for identification of areas
which require urgent soil conservation measures to check and restore soil loss. The Noyyal river
130

basin was divided into 18 sub-watersheds to identify sub-watersheds, which needs erosion
control treatment on priority basis. Distribution of soil loss from this sub-watershed has been
depicted in Fig. 10 to Fig. 12. The average annual soil loss from these sub-watersheds havebeen worked out and presented in Table 6. The results show that W16 sub-watershed is highest
prone to soil loss among all other sub-watersheds. W7 is least prone to soil loss. SW2 is least
prone because the topography or the slope in this sub-watershed is flat whereas in W16 thetopography is highly undulating hence it is highly prone to soil loss.
Figure 10: Soil Loss of Sub-Watersheds W-1 to W-6

Figure 11: Soil Loss of Sub-Watersheds W-7 to W-12

131

Figure 12: Soil Loss of Sub-Watersheds W-13 to W-18

Table 06: Prioritization of Sub-Watersheds
#
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
17.
18.

Sub - Watersheds
W1
W2
W3
W4
W5
W6
W7
W8
W9
W10
W11
W12
W13
W14
W15
W16
W17
W18
Total

Area in sq. km.
276.9
159.3
260.9
255.5
158.2
281.0
128.4
254.6
139.8
261.9
107.5
220.3
123.9
184.6
230.9
172.9
129.4
167.5
3513.2

Soil loss (T/ha_1_yr_1)
238.7867.98
124.36165.3444.4
160.23
20.78
166.7
38.18
180.45216.4996.2
47.15
123.5789.47
368.12
187.11163.632498.94

Ranking
2
14
10
6
16
9
18
7
17
5
3
12
15
11
13
1
4
8

These sub-watersheds have been further grouped into priorities (Table 7) for soil erosion
control treatment and results are presented in (Table 7) results. There are 5 sub-watersheds viz,
W1, W10, W11, W16, W17 which are in high priority class, and hence require soil erosion
control treatments at highest priority and cover 992 sq. km. area of the watershed. There are 8
sub-watersheds with medium priority and 5 sub-watersheds with low priority.
Table 07: Categorization based on Priority Class of Sub-Watershed
#
1.
2.
3.

Soil Loss Classes (T/ha/Yr)
0 to 80
80 to 180
Above 180

Sub - Watersheds
W2, W5, W7, W9, W13
W3,W4,W6,W8,W12,W14,W15,W18
W1,W10,W11,W16,W17

Priority Classes
Low
Moderate
High

Conclusion
In Noyyal River Basin, high soil erosion prone zone is observed in Western and Eastern part ofthe basin where the confluence of Noyyal and Cauvery rivers take place. The western part ofthe basin has high elevation resulting in high soil loss values compared to other parts of thebasin. On contrary, low and moderate soil erosion zone are observed in the middle parts of the132basin. The land use and land cover changes have an effect on the increasing soil erosion rate.
The regions in western part of the map have higher soil erosion rate because of higher rainfall
and higher slope. However, the values are under moderate category as the region is highly
vegetation. Clearing of forests in this region may accelerate the soil erosion rate as theconservation practice adopted for this region is very minimal. Clearing of forests must be strictly
avoided and afforestation must be practiced to ensure lower levels of soil erosion. Changes incrop patterns also have an effect on the change in rate of soil erosion. Conservation practices
like terracing, agro-forestry, bunding, crop-rotation techniques etc. must be followed to minimize
the rate of soil erosion. Majority of the region have lesser slope up to 8 degrees, and theamount of soil erosion is calculated to be in the lower to moderate levels. However, the rate ofsoil erosion can increase in future due to deforestation, over grazing etc. Thus, from this study,
it is highly recommended that suitable soil conservation practice must be adopted everywhere
possible, so that the amount of soil erosion can be kept under control preventing it from leading
to soil degradation and increase of waste lands resulting in lesser production of crops.
Figure 13: Prioritization of Watersheds Based on Soil Erosion

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