Vol. 04, Issue 06, June 2019 Available Online: www.ajeee.co.in/index.php/AJEEE ESTIMATION OF SOIL LOSS USING USLE (UNIVERSAL SOIL LOSS EQUATION) – A CASE STUDY OF TAVARJA LAKE CATCHMENT, CENTRAL MAHARASHTRA, INDIA
Atul M. Jethe1 and Sunil W. Gaikwad2
1*C.T.Bora College, Shirur, Dist. Pune-412210.
2* Associate Professor, S.P. College, Pune-30.
Abstract - During the last hundred years or so, deforestation, wind and water borne soil erosion have been steadily increasing. Today this has become a very serious worldwide problem. The Universal Soil Loss Equation (USLE) is a widely used field application model to calculate the rate of soil loss. The present study has been conducted for the Tavarja lake catchment in Latur district of Maharashtra. Overall 72 micro subbasins of the Tavarja Lake catchment (250.52km²) have been taken for the estimation of soil loss. The ‘R’ factor- average annual rainfall runoff erosivity factor (after Roose, 1975) calculated using R= P*0.5 formula. ‘R’ values are obtained from each registered grid cell in ARC-GIS 9. (R Values of TLC 422 to 430) .The soil erodibility factor (K) represents susceptibility of soil erosion. The
‘K’ values are obtained from soil erodibility nomograph suggested by Wishmeir et.el. 1971;
calculated by using soil texture class, organic matter and soil permeability (Infiltration and HC) grid wise ‘K’ values obtained (< 0.01bto >0.21).The LS; slope length and slope steepness were calculated from the contour map (1:50000) after generation of DEM (LS= <2 to 5.0).
The cover management factor (C) values calculated using LULC map of TLC. (C=<0.2 to
>0.5). Same grid method has been used for (P) conservation support practice factor for TLC (<0.2 to >0.8). The average annual soil loss obtained by multiplying five USLE factors (R, K, L, S, C and P). The average soil loss of the study area range in between 0.5754 to 15.75 kg/m2/year .Sub basin wise soil loss calculated for the entire study area and, it was found that the highest soil losses are confined to TLCW 8.11, TLCW 8.13, TLCW 8.16, TLCW 8.17 and TLCW 9.17 respectively. An attempt has been made to map erosion prone areas. This will help in planning appropriate soil and water conservation measures in the study area.
Keywords: USLE, Soil loss, nomograph, T factor, Sub basins, Tavarja Lake Catchment 1. INTRODUCTION
The soil covering the surface of the earth has taken millions of years to form. Soil is formed at a rate of only 1 inch every 1000 to 1200 years and it takes 3000 to 12000 years to build enough soil to form productive land. This means that, soil is a
‘nonrenewable resource’ and once destroyed it is gone forever. Soil erosion occurs when soil is removed through the action of wind and water at a greater rate than it is formed. [1] In its absence, the biospheric environment of the human would collapse with devastating effects on humanity. Judson [2] was one of the first geologists to assess the world soil erosion.
He estimated that, the amount of riverborne soil carried into the oceans had increased from 9.9 billion tonnes, a year before the introduction of agriculture, grazing and related activities to the present rate of 26.5 billion tonnes a year.
Hydrologists estimated that, one-fourth of the soil lost through erosion in a watershed actually makes it to the ocean as sediment. [3] The remaining three- fourth is deposited on foothill slopes, in
reservoirs, in river plains and other low laying areas or in the river bed itself, which often causes channel shifts. In an overview of global erosion and sedimentation, Pimental et.al [4] stated that, more than 50% of the worlds pasture land and about 80% of agricultural lands suffer from significant erosion.
In India, about 534 million tons (16.4 tons/ha) of soil is removed annually, 1577 million tons (29%) is carried away by rivers to sea and 480 million tons (9%) is deposited in dam or reservoirs (Narayana and Rambabu, 1983). The severity of this problem is more pronounced in arid and semiarid or Drought Prone Area (DPA), where high rainfall intensities of short duration, susceptibility of the soils to erosion mostly due to low organic matter content and human mismanagement of land through overgrazing, bush clearing and wrong ploughing method.
In Maharashtra soil erosion by water is a major factor for land degradation. It is greater in the regions
Vol. 04, Issue 06, June 2019 Available Online: www.ajeee.co.in/index.php/AJEEE receiving short periods of heavy rainfall
and is also accelerated by the absence of vegetation and undulating topography. It is highly deficient in nutrients when compared with the soils of other Indian states. They are lacking in phosphorous (P), potassium (K) and nitrogen (N), mainly because farmers in rain-fed areas use very little fertilizers. Further, excessive use of water for irrigation also leads to increasing salinity of soils.
(MSDR, 2005)
From the Central part of Maharashtra most of the silt comes from the free catchment area where the agriculture land is more. Because of regular tillage activities the soil cover becomes loose and gets eroded by the rains and is carried away by stream flows.
(MERI, 2010).
1.1 Objectives
1. The main objectives of this study were to predict long term soil loss in the sub basins of the Tavarja lake catchment using USLE and to find out main source areas contributing sediments.
2. To delineate and map the areas of severe soil loss as well as to suggest conservation planning measures to check soil erosion.
1.2 The Study Area
The area selected for present investigation is located at about 20 km to the west of
Latur, Maharashtra. It is 6th order channel basin and can be classified as macro watershed. It is roughly elongated/
bowl shaped and most of its area comes under agricultural land. Study area occupies its position in semi-arid track of Maharashtra and therefore exhibits different kind of geomorphological features and processes.
The Tavarja lake catchment extends between 18°14'00" N to 18°24'00"N latitude and 76°15'00" E to 76°27'00" E longitude. The study area is located in Latur and Ausa tahsil’s of Latur district and Osmanabad tahsil’s of Osmanabad district. Tavarja river basin is one of the sub basins of river Manjara.
(right bank tributary of river Godavari). It is a medium size dam having the catchment area is measures about 250.52 km.2
The average annual rainfall varies between 75 to 100 cm.with average annual temperature range between 27ºC to 28ºC. The average annual evaporation and evapotranspiration of the region lies between 6.60 to7.00 mm/day .Soils are black and dark black i.e. regure soils which contain high amount of humus.
About 60-to70 % of the region is under cultivation. The area is characterized by moderate sloping ground. The altitude of the region varies between 610M.to700M (Tavarjaheda at the foot of the dam). The location map of the study area is shown in the figure no.1
Fig.no.1. Location map of the study area (Tavarja lake catchment)
Vol. 04, Issue 06, June 2019 Available Online: www.ajeee.co.in/index.php/AJEEE 2. METHODOLOGY
The toposheets of the Tavarja lake catchment (56B/3, 56B/4, 56B/7 and 56B/8) having the scale of 1:50,000 published in 1972 were collected from Survey of India, Pune and observed that the total study area of TLC falls in Godavari drainage basin of Maharashtra.
In order to study the physical and chemical properties of the soil, around 72 soil samples have been collected from the catchment area using GPS and analyzed in the soil laboratory. Global mapper and Arc-GIS softwares have been used for generation of various thematic layers namely DEM, Rainfall distribution map, Soil erodibility map, LS map, land use/land cover map, C factor map and P factor map etc. These layers are then used as input parameters in USLE. After geo- referencing the toposheet, delineation of watershed along with sub – basins has been performed. Overall 72 micro sub basins of the Tavarja Lake catchment (250.52km²) have been taken for the estimation of soil loss. R- the rainfall factor was calculated from IMD data, using formula (after Roose, 1957). The textural analysis of soil samples were carried out by the sieving method, K- soil erodibility factor was estimated from the textural characteristics of the soil samples, their organic matter content, the soil structure and permeability , using nomograph, infiltration rate, hydraulic conductivity has been estimated from field experiments. The slope length–gradient factor obtained from DEM using formula of Morgan, 1986.Standerd tables (after Wischmeir et.al.1978) were used to estimate the crop and conversation factors. Finally the soil loss has been using Universal Soil Loss Equation (USLE).
2.1 THE USLE
The Universal Soil Loss Equation (USLE) is an empirical model developed by Wischmeir and Smith (1978) is used to assess average annual soil loss from study area. [5]
This equation is defined as followings
A= R*K*LS*C*P (1)
Where A= Annual soil loss from sheet and rill erosion in kg/m2 /year. R=
rainfall- runoff erosivity factor, K= soil erodibility factor, LS= slope length- steepness factor, C= Cover management factor, P= Support practice factor
2.2 Rainfall- runoff erosivity factor (R) Soil erosion is closely related to rainfall through the combined effect of detachment by rain drops striking the soil surface and by the runoff.[6] Wischmeir[7]
found that one hundredth of the product of kinetic energy of storm and the 30 minutes intensity (EI30) is the most reliable single estimate of rainfall erosion potential. Annual total of storm EI value is referred to as the rainfall erosion index.
The location value of this index is the rainfall factor ‘R’ in the USLE.
The Kind of data required to compute ‘R’ factor (storm rainfall intensity) for the study area was not avaible. Linear interpolation of ‘R’ factor from the isoerodent map of India [8] for such a small area would have been an over generalization. To resolve this practical problem the following formula was used;
R=P* 0.5 (after Roose, 1975) (2) Where, ‘R’ is the rainfall erosivity factor and ‘P’ is the mean annual precipitation in mm.
In many countries insufficient rainfall records are available to calculate nationwide rainfall erosivity. [9] The mean annual EI30 values can be approximated by the mean annual rainfall totals (mm) multiplied by 30 for India. So he proposed the above formula for computation of ‘R’
factor use in USLE. He has used 0.5 as the general constant for multiplying the mean annual rainfall. [10]
Therefore, in the present it is preferred to use this equation for the determination of ‘R’ value. Hence each grid cells of mean annual rainfall were calculated based on this equation to get the ‘R’ value using GIS software, ARCGIS- 9 .The ‘R’ values for the present study area varies from<422 to >430 .
Vol. 04, Issue 06, June 2019 Available Online: www.ajeee.co.in/index.php/AJEEE
Fig.2. Rainfall Erosivity Map of the Tavarja lake catchment 2.3 Soil erodibility factor (K)
The soil erodibility factor (K) represents both susceptibility of soil to erosion and the amount and rate of runoff as measured under standared plot condition.
Soil erodibility defines the resistance of
soil to both detachment and transport depending on the physical and chemical properties of soil. The soil erodibility factor was determined for each soil units using soil erodibility nomograph (Wischmeier et. al. 1971)
Fig 3: Soil Erodibility “K” Factor Nomograph (Source: Foster et al., 1981)
Essential data for estimation of soil erodibility were obtained by collecting soil samples from representative major slope units in the study area and further it is analysed in laboratory. Almost 72 soil samples data were used for preparation of various soils. The soil erodibility factor (k) for the present watershed is computed by using the relationship between soil texture class and organic matter content as proposed by [11]The soil type for present
area was determined in four different classes using ‘K’ factor values. Table 1 and 2 ‘K’ factor map was prepared in Arc view 3.1. The figure shows that ‘K’ factor values for the present study area ranges from <0.01 to >0.21 based on soil texture class. Based on percentage gravel and sand, permeability class, soil structure and texture a composite index map has been prepared giving the clear picture of the erodibility (fig. 4)
Vol. 04, Issue 06, June 2019 Available Online: www.ajeee.co.in/index.php/AJEEE Table No.1. Soil structure code and permeability code from nomograph (after
Wischimeier, Jonson and Cross, 1971)
% gravel + sand
Soil permeability
class Code from
nomograph Soil
structure Code from
nomograph Texture
<15 very slow 6 very fine
granular 1 silty clay, clay
15-30 slow 5 fine
granular 2 silty clay loam, sandy clay 30-45 slow to moderate 4 coarse
granular 3 sandy clay loam, clay loam
45-60 moderate 3 blocky,
platy 4 loam, silt loam
60-75 moderate to rapid 2 -- 5 Loam, silt loam
>75 rapid 1 -- 6 sand
Fig.4: Soil erodibility map of the study area
In this study, soil erodibility (K) of the study area can be defined using relationship between soil texture class and organic matter content proposed by [12] Table no.2 presents the soil erodibility factor (K) based on the soil texture class.
Table No.2. Soil erodibility factor ‘K’ (by Schwab et.al.1981) Textural class Organic matter content % P am (%)
<0.5 2 4
sand 0.05 0.03 0.02
fine sand 0.16 0.14 0.10
very fine sand 0.42 0.36 0.28
loamy sand 0.12 0.10 0.08
loamy fine sand 0.24 0.20 0.16
loamy very fine sand 0.44 0.38 0.30
sandy loam 0.27 0.24 0.19
fine sandy loam 0.35 0.30 0.24
very fine sandy loam 0.47 0.41 0.33
Vol. 04, Issue 06, June 2019 Available Online: www.ajeee.co.in/index.php/AJEEE
loam 0.38 0.34 0.29
silt loam 0.48 0.42 0.33
silt 0.60 0.52 0.42
sandy clay loam 0.27 0.25 0.21
clay loam 0.28 0.25 0.21
silt clay loam 0.37 0.32 0.26
sandy clay 0.14 0.13 0.12
silty clay 0.25 0.23 0.19
clay -- 0.13-0.2 --
(Source: by Schwab et.al.1981)
2.3.1 Percentage of Silt (Si) fine sand (fs) and sand (s)
To determine the value of silt (si) fine sand (fs) and sand (s) content sieve analysis using IS sieves (>5µm -4.75 µm) and the results are tabulated.
2.3.2 Percentage of Organic Matter (OM)
The Organic Matter (OM) in the soil was determined by chemical analysis of the soil samples collected during the field work.
2.3.3 Soil Structure (A) (Classes 1-4) Soil structure is the shape that soil takes based on its physical properties. The three general types of soil structure are granular, blocky, columnar and platy. As per soil erodibility nomograph
(Wischmeier et. al. 1971) the for classes of soil structure is as below;
1. Very fine granular 2. Fine granular
3. Medium or coarse granular 4. Blocky, platy or massive
To determine the soil structure class, the sieve analysis results were taken into consideration and also soil samples were examined under the microscope.
2.3.4 Permeability class (P) (classes 1-6) Permeability refers to the rate at which water and air move through the subsoil.
To determine the permeability class (P) of the soil, variable head permeability test was conducted on samples collected. The six permeability classes are given as shown in fallowing table no.3.
Table No.3.Estimation of Permeability Sr.No./Class % Gravel +
Sand Permeability description Code from
nomograph
I <15 Very slow 6
II 15-30 Slow 5
III 30-45 Slow to moderate 4
IV 45-60 Moderate 3
V 60-75 Moderate to rapid 2
VI >75 Rapid 1
(Source: Wischmeier and Smith et. al. 1971) This permeability class table has been checked with the permeability of soil samples from the study area. From the tests conducted out of four soil type
regions in the study area slow to moderate permeability founds in maximum part of the study area.
Vol. 04, Issue 06, June 2019 Available Online: www.ajeee.co.in/index.php/AJEEE
0 5 10 15 20 25 30 35 40
Very low Low Medium High Very high 17.76
36.44
31.16
11.65 Area in Percentage 3
Permeability classes
Tavarja Lake Catchment : Soil Erodibility ( Based on Composit Index)
Very low Low Medium High Very high
Table No.4. Data for the Soil erodibility factor K
Sample long lat OM CS FS sand Silt clay soil
code % % % % % tex
1.A1.IIe 76.4086 18.3378 0.53 25.10 66.40 91.50 4.80 3.70 sand 2.A2.IIIe 76.4268 18.3487 0.51 30.50 62.90 93.40 2.70 3.90 sand 3.A3.Ive 76.4106 18.3723 0.29 13.50 76.70 90.20 1.30 8.50 sand 4.A4.IIe 76.3727 18.3425 0.21 22.80 69.50 92.30 3.30 4.40 sand 5.A5.IIe 76.3896 18.3759 0.51 25.30 64.50 89.80 2.60 7.60 LS 6.A6.IIIe 76.3980 18.3668 0.35 12.10 82.90 95.00 4.00 1.00 sand 7. A7. IIIe 76.3996 18.3553 0.13 11.90 80.00 91.90 2.20 5.90 sand 8.A8.IIIe 76.3645 18.3648 0.61 22.10 72.90 95.00 1.20 3.80 sand 9.A9.IIIe 76.3693 18.3780 0.21 27.50 70.00 97.50 2.00 0.50 sand 10.A10.IIIe 76.3590 18.3817 0.32 20.50 70.00 90.50 2.00 7.50 sand 11.A11.IIe 76.3456 18.3476 0.35 7.10 83.00 90.10 1.90 8.00 sand 12.A12 IIe 76.3392 18.3600 0.37 23.10 69.00 92.10 2.00 5.90 sand 13.A13.IIIe 76.3424 18.3758 0.32 24.00 70.00 94.00 2.60 3.40 sand 14.A14.IIIe 76.3384 18.3851 0.32 45.70 50.00 95.70 1.40 2.90 sand 15.A15 IIIe 76.3304 18.3830 0.51 23.40 74.60 98.00 1.90 0.10 sand 16.A16. IIIe 76.3334 18.3774 0.05 23.10 69.80 92.90 4.30 2.80 sand 17.A17 IIIe 76.3108 18.3729 0.27 26.20 70.00 96.20 2.60 1.20 sand 18 A18 IIIe 76.3184 18.3843 0.61 45.50 50.00 95.50 2.80 1.70 sand 19.A19.IIIe 76.2948 18.3809 0.11 26.00 67.40 93.40 2.90 3.70 sand 20.A20.IIIe 76.2822 18.3903 0.19 26.20 70.10 96.30 1.90 1.80 sand 21.A21.VIe 76.2509 18.3736 0.37 19.70 75.00 94.70 3.40 1.90 sand 22.A22 VIe 76.2733 18.3736 0.13 26.00 71.20 97.20 1.00 1.80 sand 23.A23.Iie 76.3904 18.3217 0.48 37.50 59.20 96.70 2.20 1.10 sand 24.B24.IIe 76.3491 18.3291 0.32 20.60 75.40 96.00 2.90 1.10 sand
Vol. 04, Issue 06, June 2019 Available Online: www.ajeee.co.in/index.php/AJEEE 25.B25.IIe 76.3415 18.3161 0.61 25.50 68.00 93.50 2.90 3.60 sand 26.B26.IIe 76.3263 18.3288 0.37 23.30 74.00 97.30 1.70 1.00 sand 27.B27.IIe 76.3141 18.3379 0.13 11.10 68.10 79.20 1.80 19.00 SL 28.B28.IIe 76.3131 18.3488 0.08 12.10 85.90 98.00 0.40 1.60 sand 29.B29.IIe 76.3322 18.3450 0.48 33.60 61.40 95.00 1.50 3.50 sand 30.B30.IIIe 76.3053 18.3464 0.05 31.30 65.00 96.30 1.50 2.20 sand 31.B31.Ive 76.2951 18.3265 0.21 15.60 83.00 98.60 1.30 0.10 sand 32.B32.Vie 76.2862 18.3167 0.37 26.10 70.00 96.10 2.20 1.70 sand 33.B33. VIe 76.2819 18.3389 0.61 7.60 88.20 95.80 1.50 2.70 sand 34.A34.IIe 76.4402 18.3194 0.29 29.00 56.10 85.10 2.40 12.50 LS 35.A35.VIe 76.4170 18.3748 0.11 6.80 88.60 95.40 1.90 2.70 sand 36.B36VIe 76.2774 18.3617 0.61 24.60 69.50 94.10 2.30 3.60 sand 37.B37.Vie 76.2697 18.3633 0.64 26.40 70.00 96.40 1.50 2.10 sand 38.B38.VIe 76.2676 18.3484 0.45 26.20 70.10 96.30 3.40 0.30 sand 39.B39.VIe 76.2685 18.3349 0.11 20.20 61.50 81.70 2.30 16.00 SL 40.B40VIe 76.2665 18.3249 0.29 4.50 88.00 92.50 5.70 1.80 sand 41.B41.VIe 76.2639 18.3130 0.19 19.80 75.60 95.40 2.00 2.60 sand 42.B42.VIe 76.2676 18.3064 0.29 20.10 75.40 95.50 3.30 1.20 sand 43.B43.Vie 76.2704 18.3007 0.19 22.50 75.00 97.50 2.50 0.00 sand 44.B44VIe 76.2699 18.2931 0.32 8.90 84.30 93.20 1.20 5.60 sand 45.B45VIe 76.2722 18.2862 0.27 4.70 93.40 98.10 1.50 0.40 sand 46.B46.IIIe 76.3421 18.3056 0.16 18.00 78.40 96.40 2.00 1.60 sand 47.B47.IIIe 76.3421 18.3056 0.24 11.60 83.70 95.30 3.90 0.80 sand 48.B4.IIIe 76.3120 18.2961 0.37 12.60 83.60 96.20 3.30 0.50 sand 49.B49IIIe 76.2956 18.3028 0.45 19.90 70.30 90.20 3.00 6.80 sand 50.B50.VIe. 76.2948 18.2794 0.13 33.30 63.00 96.30 2.70 1.00 sand 51.B51.VIe 76.3067 18.2704 0.40 16.30 74.20 90.50 2.00 7.50 sand 52.B52.VIe 76.3216 18.2727 0.45 30.70 67.00 97.70 1.50 0.80 sand 53.B53.Ive 76.3247 18.2836 0.21 14.30 82.00 96.30 3.50 0.20 sand 54.B54.VIe 76.3315 18.2616 0.45 24.90 72.00 96.90 2.80 0.30 sand 55.B55.VIe 76.3457 18.2650 0.48 22.40 66.30 88.70 1.10 10.20 LS 56.B56.IIIe 76.3482 18.2867 0.35 19.50 76.60 96.10 1.90 2.00 sand 57.B57.IIIe 76.3614 18.2858 0.37 11.60 83.50 95.10 3.30 1.60 sand 58.B58.IIe 76.3785 18.3109 0.43 17.90 74.90 92.80 1.60 5.60 sand 59 B59.IIe 76.3901 18.3029 0.35 14.80 66.50 81.30 1.90 16.80 SL 60.B60.IIe 76.3962 18.2857 0.48 26.70 60.70 87.40 4.10 8.50 LS 61.B61.IIIe 76.3847 18.2834 0.35 16.60 79.40 96.00 0.70 3.30 sand 62.B62.IIIe 76.3654 18.2701 0.19 30.50 64.20 94.70 1.30 4.00 sand 63.B63.IIIe 76.3568 18.2536 0.11 12.50 85.30 97.80 2.00 0.20 sand 64.B64.IIIe 76.3588 18.2433 0.37 18.30 77.00 95.30 1.30 3.40 sand 65.B65.IIIe 76.3812 18.2523 0.24 19.30 69.20 88.50 2.30 9.20 LS
Vol. 04, Issue 06, June 2019 Available Online: www.ajeee.co.in/index.php/AJEEE 66.B66.Ive 76.3829 18.2352 0.16 31.20 65.50 96.70 2.40 0.90 sand 67.B67.Ive 76.3871 18.2514 0.27 31.00 66.20 97.20 1.20 1.60 sand 68.B68 IIIe 76.4027 18.2675 0.19 12.70 77.70 90.40 4.80 4.80 sand 69.B69.IIIe 76.4093 18.2752 0.21 15.40 75.00 90.40 5.40 4.20 sand 70.B70.IIe 76.4171 18.2971 0.37 2.50 80.60 83.10 4.30 12.60 sand 71.B71.IIe 76.4112 18.3014 0.32 13.90 77.50 91.40 2.50 6.10 sand 72.B72.IIe 76.4378 18.3036 0.43 16.60 75.50 92.10 1.10 6.80 sand 2.4 Slope length and steepness factor
(LS)
The slope length and steepness factor (LS) consists of slope length factor (L) and slope steepness factor (S). The L-Factor is defined as the horizontal distance from the point that the overlandflow starts to the point that the overlandflow starts to the point that deposition occurs or merges into the channel (Wischmeier and Smith, 1978) The S- factor is the effect of slope gradient on erosion of the location (Renard et. al. 1957).
For the present study area these two factors were calculated after the generation of Digital Elevation Model (DEM) for the study area from the toposheet. The contour data were extracted from the SOI toposheet (1:50000) [13] of the study area through scanning and manual digitization. The
DEM generated was converted to a raster file. Total area has been devided into 244 grid cell and each grid cover 1.5 km2 area/grid. According to the cell code slope length and slope gradient calculated for entire study area and then processed into Microsoft excel programme. The value of LS can be obtained from the equation (after Morgan, 1988).
LS= L/22.1(0.065 + 0.045S+
0.0065S2)……….. (3)
Where, LS is the slope-length gradient factor, S is the slope steepness in percent and L is the length of the slope in meters.
After calculating the ‘L’ and ‘S’ for each grid cell, the ‘LS’ factor was then manipulated by multiplying the L and S values and finally map of LS factor was created.
Fig.5.LS- Slope length and steepness factor map of the Tavarja lake catchment
In the present study area ‘LS’ values
ranges in between <2 to >5. As the slope values increases SL value decreases LS factor appear to be the most sensitive
Vol. 04, Issue 06, June 2019 Available Online: www.ajeee.co.in/index.php/AJEEE factor in carrying out soil loss in study
area.
2.5 Cover Management factor (C)
The ‘C’ factor represents the effect of cropping management practice on soil losses. It is based on standardized area under “clean- hilled continuous fallow conditions” related to the Soil Loss Ratio (SLR) on estimate of the ratio of actual soil loss. [14] The landuse and land cover (LULC) map of the study area was used for analyzing the ‘C’ value of the map was
prepared from the satellite image and toposheet and then processed in Global Mapper software. The study area has been classified using Dhruva & Narayana (1978), method 1. roads, 2.agriculture, 3.settlement, 4.reserved forest, 5.fallow land, 6.water bodies, 7.under construction area. Finally cover management factor was assigned for different landuse pattern using ‘C’ values.
It ranges in between < 0.2 to > 0.5 for the present study area.
Fig. No.6. Cover management factor map of the Tavarja lake catchment Table No.5. ‘C’ values
Sr.No. Land use / Land cover ‘C’ Value
1. stony/ rocky 0.0336
2. forest 0.04
3. built up land 0.024
4. cropped land 0.58
5. fallow land 0.6
6. water body 0.009
7. scrub 0.55
2.6 Support/ Conservation practice factor (P)
The support practice factor ‘P’ explains the effect of contour tillage on the soil losses. ‘P’ factor accounts for control practices that reduce the erosion potential of the runoff by their influence on drainage patterns, runoff velocity and hydraulic forces exerted by runoff on soil.
The supporting mechanical practices
include the effects of contouring, strip cropping or terracing. The lower the ‘P’
value, the more effective the conservation practice is deemed to be at reducing soil erosion. If there are no support practices, the P factor is 1.00. The P factor depends on the conservation measures applied to the study area. The study area has been classified into different landuse classes.
on the basis of C value estimation same
Vol. 04, Issue 06, June 2019 Available Online: www.ajeee.co.in/index.php/AJEEE grid method was used for estimation of P value in the study area.[15,16]
Table No. 6. ‘P’ Values Sr. No Land use/ Land cover ‘P’value
1 Stony/rocky 0.0336
2 forest 0.04
3 built up land 0.024
4 cropped land 0.58
5 fallow land 0.6
6 water body 0.009
7 scrub 0.55
Fig.No.7.Support practice 'P' factor map of the Tavarja lake catchment 2.7 Soil loss Tolerance factor (T)
The soil loss tolerance factor (T) is not included in the six factors of the USLE.
However, it is generally used to be compared with the predicted soil losses (A) so that soil erosion controls to be the areas of excess soil erosion can be
implemented. Soil loss tolerance (T), means the maximum rate of soil erosion that allows crop productivity to be sustained economically, and usually ranges from 1 to 5 ton/acre/year (Renard
et.al. 1997
)Table No.7. Sub basin wise extent of soil loss (area in Km2)
Vol. 04, Issue 06, June 2019 Available Online: www.ajeee.co.in/index.php/AJEEE 3. RESULT AND DISCUSSION
The average soil loss of the present study area computed by multiplying five USLE
factors: (R, K, L, S, C& P). The average soil loss of the study area ranges in between 0.5754 kg/m2/year. (fig 8.)
Fig.8.Tavarja lake catchment- Soil loss variation map; Based on USLE method
Fig.9.Tavarja lake catchment: Soil loss Area in kg/Km2/year Sub basin wise extent of soil loss
were calculated for the present study area and it was found that the highest soil loss were found in TLCW 8.11, TLCW 8.13, TLCW 8.16, TLCW 8.17 and TLCW 9.19.Maximum soil loss area lies in 5 to 15 kg/Km2/year i.e.38.65%.
Thus the cover management factor (C) plays relatively important role in influencing the magnitude of variation in soil loss in the study area. Soil loss and slope length and slope steepness factors are less correlated. R factor and P factor and K factor are constant for the area.
Maximum part of the study area is occupied by agricultural activities. This factor is causing soil loss in the study area.
4. CONCLUSION
The most significant findings of the present study is soil loss in the study area is well beyond the tolerance limit as well as crop management factor, soil erodibility and the climatic variation etc.
are seems to be sensitive in influencing soil loss. The very fact that, soil loss has crossed the 80% of the study area is under agriculture. Thus high rate of soil loss resulted a significant role in siltation process in the Tavarja lake. In the present study area maximum sub basins lies less than 5 to 15 kg/km2/year rate of soil loss.
It is conformed from USLE that, the site of erodible soils in the catchment is around 168.438 km2. (67.23%) subjected to land degradation
Vol. 04, Issue 06, June 2019 Available Online: www.ajeee.co.in/index.php/AJEEE and is an area contributing the maximum
silt load.
It is also revealed from the LU/LC analysis that, the area is characterized by very low density of vegetation, undulating topography and erodible nature of soil seems to be the major cause of soil loss in the study area.
It is noticed that, according to USLE technique 41 sub basins observed average soil loss greater than 10 kg/m2/Year.
On the basis of USLE TLCW 4 (1.05 kg/m2/Year. and 18.57 km2) TLCW 8.1 (13.60 kg/m2/ year and 1.5 km2)
TLCW 8.6 (12.15 kg/m2/Year. and 1.16 km2) TLCW 8.13(15.67 kg/m2/Year. and 1.93 km2) TLCW 8.16 (15.60 kg/m2/Year.
and 3.83 km2) TLCW 8.17 (15.75 kg/m2/Year. and 11.08km2) TLCW 8.18 (12.98 kg/m2/Year. and 5.93 km2) TLCW 8.19 ( 13.93 kg/m2/Year. and 0.30 km2) TLCW 8.20 ( 14.87 kg/m2/Year. and 0.63 km2) and TLCW 26, TLCW 27 and TLCW 8.32 ( 13.58 kg/m2/Year. and 1.16 km2) etc. Sub basins lies in severe category of soil loss. Therefore it requires high priority of conservation planning (area 46.06 Km2 and 18.38%)
The results of the study area clearly indicate that soil erosion in the study area should be cause of concern. Under such circumstances it becomes increasingly relevant to adopt necessary soil conservation measures so as to ensure the prolonged fertility of the land and prevention of degradation of land quality.
Thus it can be said that the USLE is a very useful tool for predicting soil loss under Indian conditions of such Drought Prone Area (DPA) of Maharashtra. It’s main advantage being its simplicity.
5. ACKNOWLEDGEMENTS
Authors wish to express their heartfelt thanks to Dept. of Geography S.P.College,
Pune for providing the necessary laboratory facilities for the analysis of soil samples.
REFERENCES
1. Directorate Agriculture and Land Resource management (2008); Department of Agriculture, Republic of South Africa (www.nda.agric.za/ Publications)
2. Judson S. Physical Geology, Printice Hall, NJ, USA, 1965. 3rd edn. pp. 143-144
3. Methodology for Assessing Soil degradation, FAO/UNEP, New York, 1978, pp 143-144 4. Centre for Science and Environment, the
state of India environment- citizen’s report, New Delhi- 1981.
5. Dhruva V.V. Narayana and Ram Babu (2001);
‘Estimation of soil erosion in India” Journal of irrigation and drainage engineering, American society of civil engineering, 2001 pp419-434.
Vol. 04, Issue 06, June 2019 Available Online: www.ajeee.co.in/index.php/AJEEE 6. W.H. Wischmeier and D.D.Smith (1978);
“Predicting Rainfall Erosion Loss: A guide to conservation planning” agriculture handbook, No 537 US development of agriculture, Agricultural Research Service, Washington 1978.
7. Wischmeier W.H. (1959): A rainfall erosion index for a Universal Soil Loss Equation (USLE)” soil sci.sec. am. proc.23, 246-249.
8. Babu, R, Tejaswini K.G.Agarwal M.P. and Bhushan L.S. (1978) ‘Distribution of erosion index and SOI erosion map of India’ Indian Journal of soil conservation 6(1)1-12.
9. Roose E.J. (1975): Erosion at ruisellment on afrique del’ oust vingt annees de measures on petites parcells experimental” cyclo ORSTOM, Adiopodoume, Ivory coast.
10. Morgon R. P.C. (1986): “Soil erosion and conservation” Longman group Ltd. Essex.
11. Stewart B.A., Woolniser, D.A. Wischmeier W.H. Caro, J. H. & Treere M.H. (1975):
Control of water pollution from cropland.
12. G.O. Schwab, R.K.Frevent. T.W. Edminister &
K.K.Barnes “Soil water conservation engineering” 3rd Edition, Willey, New York 1981.
13. Degmet P.J. and Govers G. A. GIS procedure for automatically calculating the USLE LS factor on topographically complex landscape units. Soil water conservation 1971.26- 189- 193.
14. Renard K.G. Foster G.R. Weesies G.A. Mccool D.K. and Yoder D.C. (1997) “Predicting soil erosion by water: A guide to conservation planning with the Revised Universal Soil loss Equation (RUSLE) Agriculture handbook no 703 USDA , Washington USA.
15. Williams J.R. (1975): “Sediment yield prediction with universal Soil Loss equation using runoff energy factor” Agricultural Research service report ARS- 5-40, U.S. Dept.
of Agriculture.
16. Wischmeier W.H. & D.D. Smith (1978)
“Predicting rainfall erosion losses” USDA Agricultural Research service handbook.
