Evaluation of soil erosion potential of a hilly terrain using hypsometry and E30 model: A case study of Kynshi Basin, Meghalaya

Author(s):  
N. Sinha ◽  
D. Deb ◽  
K. Pathak
Keyword(s):  
1995 ◽  
Vol 22 (1) ◽  
pp. 20-30 ◽  
Author(s):  
Nandini Chatterjee

Social Forestry (SF) schemes have been implemented in India since the 1980s to combat deforestation, increase the supply of fuel-wood and fodder, and provide minor forest products for the rural populaton. The relevance of such Schemes in the Mayurakshi River Basin is basically due to its environmentally degraded state. Latterly the Basin has been brought under the Mayurakshi River Valley Project, but unless measures are undertaken to mitigate problems of soil erosion, the efficiency of the Project will be hampered.


2010 ◽  
Vol 42 (4) ◽  
pp. 412-421 ◽  
Author(s):  
Michele Freppaz ◽  
Danilo Godone ◽  
Gianluca Filippa ◽  
Margherita Maggioni ◽  
Stefano Lunardi ◽  
...  

2012 ◽  
Vol 16 (8) ◽  
pp. 2739-2748 ◽  
Author(s):  
W. W. Zhao ◽  
B. J. Fu ◽  
L. D. Chen

Abstract. Land use and land cover are most important in quantifying soil erosion. Based on the C-factor of the popular soil erosion model, Revised Universal Soil Loss Equation (RUSLE) and a scale-pattern-process theory in landscape ecology, we proposed a multi-scale soil loss evaluation index (SL) to evaluate the effects of land use patterns on soil erosion. We examined the advantages and shortcomings of SL for small watershed (SLsw) by comparing to the C-factor used in RUSLE. We used the Yanhe watershed located on China's Loess Plateau as a case study to demonstrate the utilities of SLsw. The SLsw calculation involves the delineations of the drainage network and sub-watershed boundaries, the calculations of soil loss horizontal distance index, the soil loss vertical distance index, slope steepness, rainfall-runoff erosivity, soil erodibility, and cover and management practice. We used several extensions within the geographic information system (GIS), and AVSWAT2000 hydrological model to derive all the required GIS layers. We compared the SLsw with the C-factor to identify spatial patterns to understand the causes for the differences. The SLsw values for the Yanhe watershed are in the range of 0.15 to 0.45, and there are 593 sub-watersheds with SLsw values that are lower than the C-factor values (LOW) and 227 sub-watersheds with SLsw values higher than the C-factor values (HIGH). The HIGH area have greater rainfall-runoff erosivity than LOW area for all land use types. The cultivated land is located on the steeper slope or is closer to the drainage network in the horizontal direction in HIGH area in comparison to LOW area. The results imply that SLsw can be used to identify the effect of land use distribution on soil loss, whereas the C-factor has less power to do it. Both HIGH and LOW areas have similar soil erodibility values for all land use types. The average vertical distances of forest land and sparse forest land to the drainage network are shorter in LOW area than that in HIGH area. Other land use types have shorter average vertical distances in HIGH area than that LOW area. SLsw has advantages over C-factor in its ability to specify the subwatersheds that require the land use patterns optimization by adjusting the locations of land uses to minimize soil loss.


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