Vulnerability Assessment for Soil Erosion in Markanda Watershed-INTGC-


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Surjit Singh Saini Research Scholar, Department of Geography, Kurukshetra University Kurukshetra, Haryana Vulnerability Assessment for Soil Erosion Using Geospatial Techniques A Geographic Study of Upper Catchment of Markanda River 3 rd International Geography Congress 06-08 May, 2011 Kozhikode, Kerala, India Dr. S.P Kaushik Associate Professor, Department of Geography, Kurukshetra University Kurukshetra, Haryana

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In this study GIS and remote Sensing techniques is used to establish an geospatial data base to characterize a watershed and evaluated vulnerability to soil erosion using multi-criteria assessment based on weight assignment to each factor responsible for soil erosion. The model located potential erosion areas which need control and preventive measures according to the degree of erosion. Introduction Singh et al. (1992) have estimated that the annual rate of soil erosion is more than 15-20 tons /ha/year in Shiwalik region. Consequently, Shiwalik environment considered most fragile ecosystem in the country Soil erosion hazard is a major land degradation problem in Shivalik region of sub Himalayan mountainous environment. Singh et al. (1992) clearly indicated that Shiwalik are comprised of sandstone, grit, and conglomerates, with characters of fluvial deposits, but slopes near the foothills contain pebbles and boulders and these formations are geologically weak and unstable resultant these areas are highly vulnerable to soil erosion.


OBJECTIVES To Identify vulnerable area to soil erosion based on influencing factors such as rainfall, vegetation, soil type, slope, drainage density, land use and aspect of slope using GIS & Remote Sensing techniques. To Prepare the composite map showing level of vulnerability to soil erosion hazard.

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The Upper Catchment of Markanda River Part of Shivalik region of sub Himalayan mountainous environment Geographical Extent: 76 0 06′19” to 77 0 22′56”E 30 0 18′52” to 30 0 41′18”N Total area:593 km 2 53% in Himachal 47% in Haryana Study Area

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Remote Sensing Data TM Landsat imagery of year 2009, 30 m resolution (path 147 and row 039) Georeference Satellite Imageries in Erdas Imagine 2010 Rainfall SOI-Toposheet on 50k scale Contour Slope & Aspect Database and Methodology Soil Type Multi Criteria Evaluation using Rank Sum Methods and Weighted Overlay Analysis Using Raster Calculator in ArcGIS 9.2 Vegetation cover NDVI = ( NIR-VIS)/(NIR+VIS) Digital E levation Model (DEM) Land Use Drainage Network & Density Delineation of Area Vulnerable to Soil Erosion Weight Assign to Parameters According to Relative Importance with Respect to their contribution to Soil Erosion District wise Report on Ground Water prepared by CGWB Other Ancillary Maps & Data

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Factors & Weighting Scheme used for Assessment Area Vulnerable to Soil Erosion Erosion Parameters Sub-class of Parameters Sub-Class Weightages Per cent of Influence 1.Rainfall More than – 1200 mm 3 25.0 1101 mm – 1200 mm 2 1000 mm – 1100 mm 1 2. Vegetation cover Very less vegetation 5 21.4 Less vegetation 4 Moderate vegetation 3 High vegetation 2 Very high vegetation 1 3. Soil Eurtrochrepts / Udorthents (shallow & loamy) 3 17.9 Udipsamments/Udorthents (loamy sand to sandy loam 2 Udipsamments / Udorthents ( sandy loam to clayey loam) 1 4. Slope Very Steep (>40%) 5 14.3 Steep (30.1-40%) 4 Moderate (20.1-30%) 3 Gentle (10.1-20%) 2 Very Gentle (<10%) 1 5. Drainage Density >6 km/ 5 10.7 5.1-6.0 km/ 4 4.1-5.0 km/ 3 2.1-4.0 km/ 2 <2 km/ 1 6.Landuse Agriculture 5 7.1 Sparse vegetation 4 Forest 3 Water bodies 2 Settlement 1 7. Aspect of Slope West direction 5 3.6 East direction 4 Flat 3 South direction 2 North direction 1

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Analysis, Result and Discussion Weight Calculation Using Rank Sum Method Parameter of Criteria Straight Rank ( rj ) Weight (n-rj+1) Normalized Weight ( wj ) Weight (%) 1 Rainfall 1 7 0.25 25.0 2 Vegetation Cover 2 6 0.21 21.4 3 Soil 3 5 0.17 17.9 4 Slope 4 4 0.14 14.3 5 Drainage Density 5 3 0.10 10.7 6 Land use 6 2 0.07 7.1 7 Aspect of Slope 7 1 0.03 3.6 N=7 Sum 28 1.00 100 Where wj is the normalized weight for the j th criterion, n is the number of criteria under consideration (j=1, 2… n), rj is the rank position of the criterion. Each criterion is weighted (n-rj+1) and then normalized by the sum of weights, that is, Σ (n-rj+1).

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1 2 3 Weight Rainfall Factor

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Vegetation Factor Soil erosion potential is increased if the soil has no or very little vegetative cover of plants and/or crop residues. Plant and residue cover protects the soil from raindrop impact and splash, tends to slow down the movement of surface runoff and allows excess surface water to infiltrate. 1 2 3 4 5 Weight

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Soil Factor Soils differ in their resistance to erosion, which is a function of a range of soil properties such as texture, structure, soil moisture, roughness, and organic matter content. The susceptibility of soil to erosion agents is generally referred to as soil erodibility (Lal, 2001). Soil classifications are often used to account for spatial differences in erodibility. Important factors on the basis of which soils can be classified include soil properties, climate, vegetation, topography, and lithology.

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Slope Factor Very Steep Steep Moderate Gentle Very Gentle >40 30.1-40 20.1-30 10.1-20 <10 5 4 3 2 1 Slope (%) Weight Slope affects soil erosion rates in several different ways. As slope gradient increases, the impact angle of raindrops becomes more acute. The amount of soil loss from erosion by waters with the steeper the slope. As a result of gravity, a greater proportion of the mass and the momentum of the soil, water, and sediment particles act in the down slope direction. DEM

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Drainage Density

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Aspect of slope

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Land Use

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rainfall vegetation soils slope drainage density land use aspect Level of Vulnerability to Soil Erosion Weighted Overlay Analysis using Raster Calculator in ArcGIS 9.2

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Final Result Re-classified Composite Map Degree of Vulnerability to soil Erosion Area (Hectares) % to total 10904 18.39 13996 23.60 16061 27.09 14399 24.28 3939 6.64 Total 59299 100

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During monsoon Rain water with High Turbidness, Markanda Bridge at SH-1, Sadaura, Ambala

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This study demonstrates that geospatial techniques are indeed valuable tools in assessment and mapping of areas vulnerable to soil erosion hazard. Existing methods for identifying the erosion potential area is based on physical survey but in practices when erosion problem is very wide it is difficult task and time consuming. Therefore, Geospatial techniques based spatial modeling (weighted overlay) produce useful information for solving complex problems by identifying relationship among various geographic features clearly and logically. Such models based integrated maps are important in planning conservation and control measures for soil erosion as per the degree of vulnerability. Conclusion

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Thanks for your kind attention