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Author(s): Dipak Bej, N. K. Baghmar, Uma Gole

Email(s): bejdipak@gmail.com

Address: School of Studies in Geography, Pt. Ravi Shankar Shukla University, Chhattisgarh, India.
School of Studies in Geography, Pt. Ravi Shankar Shukla University, Chhattisgarh, India.
School of Studies in Geography, Pt. Ravi Shankar Shukla University, Chhattisgarh, India.
*Corresponding Author: bejdipak@gmail.com (Dipak Bej)

Published In:   Volume - 37,      Issue - 1,     Year - 2024


Cite this article:
Dipak Bej, Baghmar and Gole (2024). Soil Erosion Risk Estimation by using Semi Empirical RUSLE model: A case study of Maniyari Basin, Chhattisgarh. Journal of Ravishankar University (Part-B: Science), 37(1), pp. 112-125. DOI:



Soil Erosion Risk Estimation by using Semi Empirical RUSLE model: A case study of Maniyari Basin, Chhattisgarh

Dipak Bej1*, N. K. Baghmar1, Uma Gole1

1School of Studies in Geography, Pt. Ravi Shankar Shukla University, Chhattisgarh, India.

 

*Corresponding Author: bejdipak@gmail.com

Abstract

Soil is the protective skin of our earth's surface, but today’s numerous population pressures on land, along with industrialization, climatic variability such as a vigorous increase in temperature, acid rain, and deforestation, definitely degrade the quality of land. It should have to evaluate the quality of the land and find out the nutrition status as well as the soil health. The present study is employed in a Geographic Information System (GIS) environment to predict erosion risk using the Semi-Empirical Revised Soil Loss Erosion Model (RUSLE). The physiographic soil map has been prepared by visual interpretation of the Sentinal 2 satellite image, from which the soil erodibility factor has been derived. The digital elevation model (DEM) has been prepared from a contour map and used as the base map for the topographic-related analysis. In this model, the slope length (LS) factor has been prepared from the DEM. The crop conservation and management factor (C) and support practice factor (P) factors have been derived from the LULC map. It has been found that 4.45% of the watershed comes under very high erosion, 3.50% under high erosion, 7.80% under moderate erosion, 11.37% under low erosion, and 51.36% under a very low erosion-prone zone.

Keywords: Digital Elevation Model, Erosion, Geographical Information System, RUSLE, Vigorous.

1. Introduction

Today, soil erosion is a vital concern for landscape conservation and management due to climatic factors that, in the form of temperature or rainfall, act as agents of soil erosion. The soil erosion process involves various contributory factors related to the climate land use and soil. It is necessary to investigate various contributory factors to finally assess soil erosion. Rainfall and temperature are the major factors temperature has the power to reduce the moisture level of the soil, so dry soil particles are detached from one another, and finally, the dynamic transportation agent is determined by the slope. Soil erosion estimation is very helpful for sustainable land evaluation. Where soil erosion is the main obstacle to sustainable agriculture, horticulture, livestock farming, etc., in order to mitigate the impact of land degradation, the Geographical Information System (GIS) and Remote Sensing (RS) are very powerful tools to monitor the present scenario. An attempt has been made to prepared a soil loss (soil erosion) map of the study area using various physical and climatic parameters.

 The Land use landcover change detection analysis has been prepared using IRS remote sensing satellite LISS III and LANDSAT-8 satellite data (Saha et al., 1992).  IRS 1D LISS IV satellite data used to prepare land use and land cover to assessed the risk of erosion-prone of Sukha Lake catchment, north India (Shirmali et al. 2001). Almost half of all terrestrial land suffers from land degradation as a result of various landcover land use parameters such as geology, slope, soil, drainage, etc. to determine the land degradation of the Silabati River in west Bengal (Mahala, 2020).    There are different methods to determine the soil erosion such as Universal soil loss erosion (USLE), Revised Universal soil loss erosion (RUSLE), Morgan Finney (MMF), Sediment Yield Index (SYI), Coordination of Information on the Environment (CORINE), Water Erosion Prediction Project (WEPP), Kinematic Runoff and Erosion Model (KINEROS) model. RUSLE model has been developed updated by USDA-Agricultural Research Service (ARS) in co-operation with the USDA-Natural Resources Conservation Service (NRCS). In this present study RUSLE model has been adopted to measure the soil loss in maniyari watershed with the help of Sentinal 2 dataset in a GIS environment. An attempt has been made to measure the catchment wise soil erosion or losses with the help of various physical parameters.

2. Study Area

The Maniyari River basin is a part of Shivnath catchment (Part of Mahanadi). The river maniyari rises from Satpura Maikal hill, in the North West of the Central plateau. So, the Maniyari River flows through all the three ideal stages of life cycle like hilly, plateau and plain. The study area covered three districts namely Kabirdham, Mungeli and Bilaspur. Latitude and Longitude of the study area 21°55' 0" N to 22° 32'0" N and 81°15' 0" E to 82° 5' 0" E. Total area of the study region approximately 3790 sq. km. with the total population of near about10 lakhs. Summer temperatures peak at 43º C, with a mild winter temperature of 11ºC. And the average annual rainfall is 1128.34 mm, which is less than the 1292 mm average rainfall in Chhattisgarh. (Fig.1).


Fig.1: Location map of the Study area

3. Materials and methods

Data base is one of the prime raw materials to finish a work. Both primary and secondary database has been used to find out the productivity of land. Secondary data like topographical sheet, Sentinal 2 satellite imagery with the resolution of 10 m. and Aster DEM has been collected from survey of India and USGS Earth Explorer respectively. All of the primary and secondary data sources are included in Table 1.able 1

Maniyari Basin: Sources of data

Data types

Data sources

Details

Soil Sample

Field Survey

Top soil up to 20 cm.

Topo sheet

Survey of India

64F-06 to 16, 64F16,64G13, 64J03,64J04, 64K01

Satellite imageries

NRSC, ISRO

Sentinal 2 (30 m), Aster DEM (30 Mt.)

 

Various Secondary dataset has been collected from Indian Metrological Dept., Survey of India and USGS earth Explorer. Before going to the field, it has been identified the sample location on basis of visual interpretation like tone, texture, pattern, colour, site, shade etc using topographical sheet and Sentinal 2 satellite image. The imageries are visually interpreted for estimate the soil erosion with following steps (Fig. 2).


Fig.2. Methodology for revised soil loss erosion model

4. Results and Discussion

The above five (5) parameters has been discussed bellow one by one. These elements show how geography, soil, climate, and land use affect sheet and rill degradation. RUSLE calculates soil loss for particular sites by giving values to these elements in accordance with site-specific circumstances, and it may be used to inform conservation strategy relevant to certain field sites. There are 19 primary physiographic land unit has been identified with the combination of topography, slope and Landuse Landcover map. Ninety soil samples has been collected according to the physiographic unit, some broad unit have contained more than one sample like PLS11 plain agriculture land have approximate 50 samples because of their soil textural differences as well as HLS12 hilly forest areas have more than ten samples to identified the soil nature. Physiographic unit details and codes are shown in Table 2 and Figure 3.

 Table 2

Maniyari Basin: Physiographic unit details and code

Sl. No.

Land Facets

Topography

Landuse Landcover

Unit code

1

Hill Surface (HLS)

Steep sloping
(40-50%)
Hilly (H)

Agriculture Land

HLS11

2

Forest

HLS12

3

Scrub Land

HLS13

4

Pleatue summit surface (PDS)

Moderate
sloping
(7-10%)

Agriculture Land

PDS11

5

Forest

PDS12

6

Scrub Land

PDS13

10

Pediment Fringe surface (PFS)

Gently sloping
(2-7%)

Agriculture Land

PFS11

11

Forest

PFS12

12

Scrub Land

PFS13

13

Plain Surface (PLS)

Very gentle
sloping
(1-2%)

Agriculture Land

PLS11

14

Barren Land

PLS12

15

Forest

PLS13

16

Scrub Land

PLS14

17

Residual Hill (RH)

Moderate
sloping
(7-10%)

Agriculture Land

RH11

18

Forest

RH12

19

Scrub Land

RH13

20

Water Body (WB)

-

-

WB

21

Built Up and Industry (BI)

-

-

BI

(Source: Personal Survey, 2020)


Fig. 3: Physiographic Land Units wise Digital soil map

4.1. Rainfall Erosivity Factor (R)

The erosivity of rainfall varies with the variation of the location of the study area, mainly depends on rainfall, relief, slope, etc. The rainfall erosivity factor (R) map is prepared using daily and monthly rainfall data obtained from Pendra and Bilaspur stations located in the Chhattisgarh basin. This rainfall erosivity map has been prepared based on 11 years of average rainfall to estimate average rainfall erosivity (R) factor values (Wischmeier, 1959). The advantage of using the daily rainfall as a better indication to change sediment production which expresses the seasonal sediment yields in the study area. While employing yearly rainfall has certain benefits, like its easy access, simple computation, and greater exponent’s consistency, which varies by area, Consequently, in the R factor for the current study, using daily rainfall amounts (Wischmeier, 1959) made the suggestion, and it was validated by (Panigrahi et al., 1996) suitable for Indian circumstances. Panigrahi et al. 1996 developed a model for the estimation of R factor (equation E1)

R = P2 (0.00364 log 10 P – 0.000062) ……………………………. (E1)

Whereas, R= rainfall erosivity factor

    P= Annual average rainfall

Table 3 

Maniyari Basin: Rainfall recorded in different station in Maniyari basin (2000-2020)

Station

Latitude

Longitude

Rainfall (mm.)

R factor

A

21.884

82.035

1120

507.96

B

22.337

82.000

1611

696.013

C

22.564

81.696

1758

752.314

D

22.499

81.317

1636

705.588

E

22.101

81.501

1279

568.857

F

22.336

81.630

1394

612.902

G

22.109

81.849

1518

660.394

H

22.077

82.136

1576

682.608

(Source: Indian Metrological Department, Nagpur, 2000-2020)

Rainfall erosivity factor is high in northern part of the Maniyari basin due to rainfall and relief condition, where as the R factor is low in lower part of the maniyari basin basically found in Teasua and Agar River where the amount of rainfall is low due to become under rain shadow zone shown in Fig. 4 and Table 3.

4.2. Soil Erodibility Factor (K)

The soil erodibility (K) factor has been calculated with the help of soil physical and chemical properties such as soil texture, stoniness, organic matter, etc. 90 soil samples have been collected from different parts of the study area, which was discussed in the methodology part. These samples were carried out to estimate the texture, structure, and organic matter with the help of soil testing instruments and methodologies. Wischmeier et. al. developed an idea of a semi-empirical method to determine the K factor by using an equation based on four soil parameters, which were derived after analyzing the soil in a soil testing laboratory. (Wischmeier et al., 1971)        

100K = 2.1M1.14 (10-4) (12 - a) + 3.25 (b -2) + 2.5 (c-3) ……………………….. (E2)

Where, K = Soil erodibility factor,

M = texture (% silt + % very fine sand) sand > 0.10 mm,

a = Organic matter percentage, b = soil structure, c = Soil permeability

The K value varies from 0.21 to 0.38. Soil with a high K factor value denotes a low emotional risk potential, while soil with a low K value denotes a low emotional risk potential. Soil with high permeability, clay texture, and coarse structure contains a low K value. Whereas soil with a sandy, low-permeability, and fine structure contains a high K value. In this present study, the northern part of the basin indicates a hilly and dissected plateau with a sandy, loamy texture with a low permeable rate and a high K value (0.30–0.38). And most of the plain areas and hilly valley areas with loamy texture and moderate to fine granular structure were assigned a medium K value (0.25-0.30). The south western part of the Maniyari basin (also a part of Kabirdham district), along with the clay texture (shallow black soil), assigned a high K value (0.5 – 0.24) shown in Table 4. If the k value is high, then soil erosion is also very high, it was reported that there is a negative correlation between K values and soil erosion. (Fig. 4) The above table shows the soil structure and soil permeability, which were collected from secondary sources (Food and Agriculture Association, 2010 guidelines).

 

Table 4

Maniyari Basin: K factor Details, 2020

Sl No

Physiography

LULC

Texture

OC (%)

K factor

1

Hilly

Forest

Sandy clay loam

< 0.5

0.27

2

Forest

Sandy loam

< 0.5

0.27

3

Agriculture

Sandy loam

< 0.5

0.27

4

Piedmont

Agriculture

Loam

< 0.5

0.38

5

Agriculture

Sandy clay

< 0.5

0.27

6

Agriculture

Sandy clay loam

< 0.5

0.27

7

Agriculture

Sandy loam

< 0.5

0.27

8

Forest

Clay loam

< 0.5

0.28

9

Forest

Loam

< 0.5

0.38

10

Forest

Sandy clay loam

< 0.5

0.28

11

Forest

Sandy loam

< 0.5

0.27

12

Plain

Agriculture

Clay

< 0.5

0.21

13

Agriculture

Clay loam

< 0.5

0.25

15

Agriculture

Sandy clay loam

< 0.5

0.27

16

Agriculture

Sandy loam

< 0.5

0.24

17

Forest

Sandy clay loam

< 0.5

0.25

18

Forest

Sandy loam

< 0.5

0.24

19

Plateau

Forest

Sandy clay

< 0.5

0.27

21

Forest

Sandy clay loam

< 0.5

0.28

25

Agriculture

Sandy clay loam

0.5 - 2

0.27

26

Agriculture

Sandy loam

0.5 - 2

0.27

27

Forest

Sandy clay

0.5 - 2

0.27

28

Forest

Sandy loam

0.5 - 2

0.27

30

Plain

Agriculture

Clay

0.5 - 2

0.21

31

Agriculture

Clay loam

0.5 - 2

0.25

32

Agriculture

Sandy clay loam

0.5 - 2

0.25

34

Agriculture

Sandy loam

0.5 - 2

0.24

35

Forest

Sandy clay loam

0.5 - 2

0.25

36

Scrub

Sandy clay loam

0.5 - 2

0.27

37

Upper Plateau

Forest

Sandy clay loam

0.5 - 2

0.27

38

Forest

Sandy loam

0.5 - 2

0.24

(Source: Personal Survey, 2020)

 

Fig.4: R and K factor map of Maniyari Basin

4.3. Slope length Factor (LS)

The slope length factor has been computed using Aster DEM with a resolution of 30 mt. with the help of Arc GIS 10.5 software. The DEM-Hydrology tools of ArcGIS 10.5 software have been used to compute the slope length factor. Fig. 5 shows the steps.


Fig.5: Methodology to estimate the slope length factor

Table 5

Maniyari Basin: Categories of slope length and their distribution, 2020

Sl. No.

LS factor

Area (Sq Km)

Area (%)

1

< 2

3065.34

80.76

2

2-5

240.16

6.32

3

5-10

278.86

7.34

4

10-20

143.63

3.78

5

>20

67.43

1.77

(Source: Researcher prepared using Aster DEM)

 

Fig.5: Fill sink, Flow Direction and Flow accumulation of the study area

The LS factor has been calculated for each and every grid of the study area. (Fig.6) The slope length factor has been categorized into five classes using the quintiles method. 80 percent of the area is occupied by less than 2 slope length factors, whereas only 1 percent of the of the area covered by more than 20 slope length factors. There is a positive correlation that has been noticed in this study area. The area of high sloppy areas along with scrub and bare land recorded a high slope length factor, whereas the areas of plain and agriculture areas with a gentle slope recorded a low slope length factor. In this study, where the slope length factor is greater than 20, there is a very high potential for soil erosion risk.


Fig.6: Slope in Degree and Slope length factor of the study area

4.4. Crop conservation and management factor (C)

The cover conservation and management factor (C) takes into account how different types of land use and land cover, crop cover, and crop management affect annual soil loss. The C-factor, which indicates the circumstances that can be handled the easiest to prevent erosion, is possibly the most significant aspect when it comes to policy and land use decisions. The C factor value of the different land uses and land covers has been assigned in Table 6. There are eight composite land use land cover classes that have been prepared: agricultural land, barren land, forest, built-up land, scrub land, industrial areas, and water bodies. The range of the C factor value in this study area is 0 to 1. Water bodies have been assigned the lowest (0), dumped and abundant mining land has been assigned the highest (1), and the rest of the classes have been assigned between 0 and 1, as shown in Fig. 7. Zero C values express the greater potential of soil erosion.

 Table 6

Maniyari Basin: C factor in based on land use/land cover, 2020

Sl. No.

Classes

C

Area (Sq km)

Area (%)

1

Agriculture Land

0.28

2283.45

60.17

2

Barren Land

0.5

10.73

60.17

3

Built up land

0.03

118.14

0.28

4

Forest

0.01

1051.54

3.11

5

Forest Plantation

0.016

0.84

27.71

6

Industrial/Mining

1

3.66

0.02

7

Scrub Land

0.014

194.67

0.09

8

Water Body

0

131.44

5.13

(Sources: researcher prepared based on Hurni, 1985; Asmamaw et al)

4.5. Support practices factors (P)

The support practice factor denotes the impact of support and practices such as strip cropping, stone walls, contour farming, tillage, etc. on the ratio of soil loss in the Maniyari basin. Land and soil management with support practices have the ability to reduce the risk of soil erosion by 3% (European Soil Data Center guidelines). Grass margins have the largest ability to reduce the soil risk of erosion. In this present study, very little land and soil support practice has been identified except for the piedmont areas. Somewhere, agricultural lands in hilly areas are supported by contour cropping. P factor values range from 0 to 1, with 0 denoting appropriate anthropogenic erosion and 1 denoting a non-anthropogenic anthropogenic erosion facility. P factor ranges varied from 0 to 1 based on the support practice factor shown in Table 7 and Fig. 7. P-values can be estimated either from landuse landcover using the RS GIS approach, from a review of the literature, or even from domain knowledge by experts. (Karydas et al., 2009)

 

Table 7

Maniyari Basin: P factor in based on landuse/landcover, 2020

Sl. No.

Classes

P

Area (Sq km)

Area (%)

1

Agriculture Land

0.28

2283.45

60.17

2

Barren Land

1

10.73

60.17

3

Built up land

-

118.14

0.28

4

Forest

1

1051.54

3.11

5

Forest Plantation

0.28

0.84

27.71

6

Industrial/Mining

1

3.66

0.02

7

Scrub Land

1

194.67

0.09

8

Water Body

-

131.44

5.13

(Sources: Researcher prepared based on Mahala, 2018)


Fig.7: C and P factor of the study area

 

4.6. Average annual soil loss (A)

After deriving all parameters, the RUSLE soil erosion risk map has been prepared. The extent of soil erosion risk class in Nun watershed can be seen in the soil risk map, and the extent of soil risk class-wise percentage is shown in the table. The formula to generate the soil erosion risk is as below:

A = R * K* L* S* C *P

The empirical Revised Universal Soil Loss Equation (RUSLE) has been estimated based on five major erosional risk potential factors. Rainfall erosivity factor, soil erodibility, slope length, cover management, and support practice factor of the entire Maniyari basin. The actual soil loss estimates in the basin range from 1 to 232 t ha−1 year−1 (Fig. 8 and 9). Weighted overlay methods have been adopted to estimate the soil erosion risk potential of the study area using the GIS platform. Kriging and inverse distance law interpolation techniques have been adopted to determine the value of the entire grid from the existing values according to the given database.

Table 8

Maniyari Basin: Average annual soil loss, 2020

Class

Soil Loss (Ton/Ha/Year)

Area (Sq Km)

Area (%)

Very Low

< 5.0

2769.5

72.97

Low

5 - 10

431.5

11.37

Moderate

10- 20

292.6

7.80

High

20 - 30

132.7

3.50

Very High

> 30

169.1

4.45

Total

3795.4

100

(Source: Computed Researcher by using Personal survey data, 2020)

Fig.8: Maniyari Basin: Average annual soil loss, 2020

Table 9

Maniyari Basin: Watershed wise average annual soil loss, 2020

 

 

Watershed

Class

Very Low

Low

Moderate

High

Very High

Total

Soil Loss (Ton/Ha/Year)

< 5.0

5 - 10

10 - 20

20 - 30

> 30

Agar

Area (Sq Km)

966.21

130.83

102.17

44.22

72.07

1315.5

Area (%)

73.45

9.95

7.77

3.36

5.48

100

Maniyari

Area (Sq Km)

678.02

160.48

142.88

56.92

56.05

1094.35

Area (%)

61.96

14.66

13.06

5.20

5.12

100

Ghonga

Area (Sq Km)

629.44

101.96

48.22

18.20

25.76

823.58

Area (%)

76.43

12.38

5.85

2.21

3.13

100

Teasua

Area (Sq Km)

496.90

38.25

15.61

4.22

7.06

562.04

Area (%)

88.41

6.81

2.78

0.75

1.26

100

(Source: Computed Researcher by using Personal survey data, 2020)


Fig. 9: Maniyari Basin: Watershed wise average annual soil loss, 2020


Fig. 10: Average annual soil loss and watershed wise soil loss of the study area, 2020

Watershed-wise soil erosion has been estimated and placed above Table 9, and Figs. 9 and 10 which show the annual total soil loss as well as the spatial distribution of soil loss in all four watersheds. Agar and Maniyari watersheds are affected too much compared to other watersheds due to their spatial location in hilly and dissected plateau areas where the runoff is maximum.

The finding of the study of the Maniyari Basin as follows:

Ø  The average annual soil loss/erosion of the Maniyari basin is 13.6 ton /ha/ year. Although the average annual soil loss of the dissected hilly and plateau area is very high approximately 39.6 ton/ ha/ year. Whereas the 3.5 ton/ha/year in plain areas.

Ø  Highest soil loss recorded from rocky barren, abounded mining, hilly and dissected land with sandy structure, scrub land, current fallow, and low intensity of cultivation.

Ø  Lowest soil loss from the dense forest and water body, highly vegetation cover area. In this present study. 27 % of the area comes under the forest but with in this 16 % are very dense. The forest with gentle slope recorded lowest degree of soil loss.

 6. Conclusion

The physiographic land system of a region is the basic physical parameter used to determine the quality and capability of land as well as the estimated land degradation. The soil erosion rate has been estimated using the imperial model RUSLE, which has been estimated at 13.6 tons/ha/per year as the average annual soil loss/erosion of the study area. The GIS platform and satellite technology help to determine soil erosion as well as identify the priority risk zone. Land resource management is one of the primary concerns for increasing land efficiency in terms of capability and productivity. Various land resource management techniques should be used to resolve the problem, such as continuous contour trenches, contour bunds, farm bunds, boulder gully plugs, etc.

 

Reference

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Author(s): Swarnlata Saraf

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Author(s): Ram Kumar Sahu; Pushpa Prasad; Shashikant Chandrakar; Amit Roy

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Author(s): A.K. Bansal; V.B. Sexena

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Author(s): R Verpoorte

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