General Principles of Intellectual Property: Concepts of Intellectual Proper...
T Gopal Krishnan.ppt
1. Soil Erosion Modelling using
GIS and Remote Sensing
(Case study of Barakar River basin, Jharkhand, India)
Presented to
Bharat Ratna Indira Gandhi College of Engineering Solapur
Dr. Gopikrishnan T, Assistant Professor
(Mr. Akhil Mehrotra, PG Student)
Department of Civil Engineering, National Institute of Technology Patna
Faculty Development Program
3. SIGNIFICANCE
• Soil erosion impacts the agricultural industry as well as the natural environment. When
the topsoil is eroded from an area, that area loses its most nutrient-rich layer, and
therefore soil quality is reduced.
• The construction of reservoir block the flow of sedimentation downstream leads to
increase sediment deposit in the reservoir.
• Comparing the various models of soil erosion for understanding the efficacy of models
for different landscape conditions
4. INTRODUCTION
• Soil is the top layer of the earth’s surface that is capable of sustaining life.
• Soil erosion is a process of detachment and transport of soil particles by erosive agents like
water or wind. Particles are eventually deposited to form new soils or to fill lake and
reservoir.
• Worldwide, each year, about 75 billion tons of soil is eroded from the land.
• The fact that data collections on soil erosion is usually capital intensive as well as a time
consuming exercise. Hence, global extrapolation of a few data leads to gross error.
• Remote Sensing (RS) and Geographic Information System (GIS) enable manipulation of
spatial data of various types.
• The ability to extract overlay and delineate any land characteristics make GIS suitable for soil
erosion modeling.
6. • Length 225 Km
• Catchment area 18000 Km2
• Coordinates 23° 42′ 0″ N, 86° 48′ 0″ E
The soil in the Barakar river basin is mainly:
I. red soil
II. red loamy soil
III. Loose sandy soil
IV. lateritic soil
Fig 2 Topography of Barakar
catchment area
STUDY AREA
9. METHODOLOGY
Revised Universal Soil Loss Equation (RUSLE)
• The RUSLE equation can be expressed as:-
A = R*K*LS*C*P
Where,
o A = average soil loss (ton/hac/yr)
o R = rainfall erosivity factor (MJ.mm / ha.hr.yr)
o K is the soil erodbility factor (ton/ ha /hr /ha-1 / MJ-1 mm-1)
o LS is the slope length-steepness factor (dimensionless)
o C is the cover management factor (dimensionless)
o P is the conservation practices factor (dimensionless)
10. • Rainfall Erosivity Factor (R)-
(2)
• Soil erodibility factor (K)-
(3)
- parameter provides low erodibility for soil with high coarse sand content and high
erodibility for soil with low sand content.
- parameter provides low erodibility for soils with high clay to silt ratio.
-parameter provides low erodibility for high organic carbon content.
-parameter reduces soil erodibility for very high sand content.
11. Topographic factor (LS)-
(4)
Crop management factor (C)-
Table 1 C factors for different land use (USDA 1951; David 1988)
Land Use C factor
Row Crops 0.24
Pasture/Hay 0.050
Water/Wet Areas 0.000
Urban Low Density 0.030
Urban High Density 0.000
Deciduous Forest 0.009
Evergreen/Coniferous Forest 0.004
Mixed Forest 0.003
12. Conservation support practice factor (P)
Table 2 Assumed P factors for different types of management practices (David 1988)
Tillage and Residue Management P-factor
Conventional Tillage 1
Zoned Tillage 0.25
Mulch Tillage 0.26
Minimum Tillage 0.50
14. Fig 5 K-factor
The K factor quantifies the significant soil loss per unit energy of precipitation that causes soil
erosion Brady and Weil (2012). The highest K factor value observed in this study is 0.15 t ha h
ha−1 MJ−1 mm−1 and the lowest value is 0.13 t ha h ha−1 MJ−1. The result of K factor ranges
obtained after analysis in Arc Map is provided in Fig. 6.
15. The hefty precipitation erodes the soil particles swiftly that causes high runoff. High runoff flow
results in significant sheet or rill erosion. The highest R factor value observed in this study is
1000 MJ mm ha−1 h−1 year−1 and the lowest value is 260 MJ mm ha−1 h−1 year−1. The result of R
factor ranges obtained after analysis in Arc Map is provided in Fig. 5.
16. Fig 7 LS Factor
The topographic factor (LS) comprises of the slope length and gradient that influences the surface
runoff velocity. While the surface runoff velocity increases simultaneously the soil erosion
increases. The topographic factor includes the slope length factors (L) and slope gradient factors
(S). The highest LS factor value observed in this study is 55% and the lowest value is 0%. The
result of LS factor ranges obtained after analysis in Arc Map is provided in Fig. 7.
17. The Crop management factor (C) is plotted as a map in Arc Map using the values in the Table 1.
The land uses given in the Table 1 are used to reclassify the land use map of the study area. The
map obtained by reclassification is shown in Fig. 8. For this study the values of C-factors adapted
by (David 1988) is used to signify the effect of cropping and management practices on soil loss.
18. The conservation support practice factor (P) is plotted as a map in Arc Map using the values in
the Table 2. The tillage and residue management values given in the Table 2 are used to reclassify
the slope map of the study area. The map obtained by slope reclassification is shown in Fig. 9.
For this study the values of P-factors adapted by (David 1988) is used to signify the effect of
conservation support practices on soil erosion.
19. The soil erosion map was obtained using RUSLE model in Arc Map. The soil erosion map
shows predicted erosion from the range of 0 t ha-1 yr-1 to 700 t ha-1 yr-1.The average estimated
soil erosion is 6.19 t ha-1yr-1 in the study area.
20. The soil erosion range 0 t ha-1 yr-1 to 5.7 t ha-1 yr-1 represents pasture land where soil erosion is
less. The soil erosion range 5.8 t ha-1yr-1 to 29 t ha-1 yr-1 represents grassland where soil erosion
is slightly more. The soil erosion range 30 t ha-1 yr-1 to 88 t ha-1 yr-1 represents mostly orchards
where soil erosion is in the intermediate range. The soil erosion range 89 t ha-1 yr-1 to 290 t ha-1
yr-1 represents Indian grassland where soil erosion is high due to the high presence of sandy
soil. The soil erosion range 300 t ha-1 yr-1 to 700 t ha-1 yr-1 represents range and Indian
grassland where soil erosion is very high due to cultivation of range grasses for fodder. The
range grasses are cultivated in sandy soil that is also suitable for Indian grasses which will result
in very high vulnerability towards soil erosion. Therefore, the value of soil erosion ranges from 0
to 700 t ha-1 yr-1.
21. • Babu, R. (2004). Assessment of Erodibility status and Refined Iso- Erodent Map of India.
Indian Journal of Soil Conservation. 32(2): 171-177.
• Biswas, S. S., & Pani, P. (2015). Estimation of soil erosion using RUSLE and GIS
techniques: A case study of Barakar River basin, Jharkhand. India. Modeling Earth
Systems and Environment,1(4), 1–13.
• Brady, N.C., Weil, R.C. (2012). The nature and properties of soils. Pearson Education,
New Delhi
• Briak, H., Mrabet, R., Moussadek, R., Aboumaria, K. (2019). Use of a calibrated SWAT
model to evaluate the effects of agricultural BMPs on sediments of the Kalaya river basin
(North of Morocco). International Soil and Water Conservation Research, 7, 76–183.
• David, W.P. (1988). Soil and water conservation planning: policy issues and
recommendations. Journal of Philippine Development No. 26 vol. 15.
• Foteh, R., Garg, V., Nikam, B.R. et al. (2018). Reservoir Sedimentation Assessment
through Remote Sensing and Hydrological Modeling. J Indian Soc Remote Sens., 46:
1893. https://doi.org/10.1007/s12524-018-0843-6
• Ghosal, K. & Das, S. (2020). A Review of RUSLE Model. J Indian Soc Remote Sens.,
https://doi.org/10.1007/s12524-019-01097-0
REFERENCES-
22. Jha, M.K., Paudel, R.C. (2010). Erosion Predictions by Empirical Models in a
Mountainous Watershed in Nepal. Journal of Spatial Hydrology Vol.10, No.1
Spring 2010.
Lorup J.K., Styczen M. (1990). Soil Erosion Modelling. In: Abbott M.B.,
Refsgaard J.C. (eds) Distributed Hydrological Modelling. Water Science and
Technology Library 22. Dordrecht: Springer.
•Misra, K. (1999). Watershed management activities in Damodar Valley
Corporation at a glance. Soil Conservation Department, Damodar Valley
Corporation, Hazaribagh.
•Moumen, Z., Nabih, S., Lahrach, A., Elhassnaoui, I. (1960). Hydrologic
Modeling Using SWAT: Test the Capacity of SWAT Model to Simulate the
Hydrological Behavior of Watershed in Semi-Arid Climate. Decision Support
Methods for Assessing Flood Risk and Vulnerability (pp. 162-166).
Pennsylvania: IGI Global.
•Pandey A, Chowdary VM, Mal BC (2007) Identification of critical erosion
prone areas in the small agricultural watershed using USLE, GIS and remote
sensing. Water Resour Manag 21:729–746
23. •Renard, K.G., G.R. Foster, D.C. Yoder, and D.K. McCool. (1994). RUSLE
revisited: Status, questions,answers, and the future. J. Soil Water Conserv .49(3):213-
220.
•Shinde, V., Sharma, A., Tiwari, K.N. et al. (2011). Quantitative Determination of
Soil Erosion and Prioritization of Micro-Watersheds Using Remote Sensing and GIS.
J Indian Soc Remote Sens., 39: 181. https://doi.org/10.1007/s12524-011-0064-8
•Srinivas, C.V., Maji, A.K., Reddy, G..O. et al. (2002). Assessment of soil erosion
using remote sensing and GIS in Nagpur district, Maharashtra for prioritisation and
delineation of conservation units. J Indian Soc Remote Sens., 30: 197.
https://doi.org/10.1007/BF03000363
•Shrivastava, P.K., Tripathi, M.P. & Das, S.N. (2004). Hydrological modelling of a
small watershed using satellite data and gis technique. J Indian Soc Remote Sens 32:
145. https://doi.org/10.1007/BF03030871
•USDA, (1951). Soil survey manual. In Soil Conservation Service, Soil Survey Staff,
U.S. Dept. of Agricultural handbook 18. (p. 503). Washington D.C., USA: U.S. Govt.
Print Office.
24. •USDA-SCS. (1972). ‘Hydrology’ in SCS national engineering handbook, section 4.
Washington DC: US Department of Agriculture.
•Williams, J.R. (1975). Sediment routing for agricultural watersheds. Water Res Bull
11:965–974
•Wischmeier, W. & Smith, D. (1965). Predicting rainfall-erosion losses from cropland east
of the Rocky Mountains: Guide for selection of practices for soil and water conservation.
Agriculture Handbook No.282. pp. 41-42.
•Wischmeier, W. & Smith, D., (1978). Predicting rainfall erosion losses—A guide to
conservation planning. Agriculture Handbook No.537, pp. 3–4.
