Estimation of Annual Average Soil Loss, Based on Rusle Model in Kallar Watershed, Bhavani Basin, Tamil Nadu, India

Rahaman, S. Abdul; Aruchamy, S.; Jegankumar, R.; Ajeez, S. Abdul

doi:10.5194/isprsannals-ii-2-w2-207-2015

Cited by 40 publications

(19 citation statements)

References 29 publications

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“…This study found that lack of vegetation cover and type of land use (cultivation and grazing) were the main contributors to the severe and high rate of soil loss in the study area. RUSLE factors can be classified into two categories; environmental and management with the former remaining relatively constant over time while the latter varies considerably [9]. This implies that the management factors affecting both land cover and land use can be manipulated considerably to reduce the rate of the soil loss.…”

Section: Discussionmentioning

confidence: 99%

“…Severe erosion prone areas can be delineated and prioritized for erosion mitigation [8]. Effective soil erosion control requires prediction of the amount of soil loss [5] [9].…”

Section: Introductionmentioning

confidence: 99%

“…RUSLE constitutes the main factors causing soil erosion: rainfall erosivity, soil erodibility, slope length, steepness, cover management and support practice [12]. The choice and use of RUSLE model in this study to quantify soil was informed by its all-inclusive representation of the main factors that cause soil erosion [9]. RUSLE in GIS allows use and analysis of vast amounts of data that would otherwise not be feasible manually [13].…”

Section: Introductionmentioning

confidence: 99%

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Modelling Soil Erosion for Land Management in Ungauged Golole Catchment in Marsabit County, Kenya

Njiru¹,

Kariuki²,

Mwetu³

2018

OJSS

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This study modeled soil erosion between January 2016 and September 2018 for land management in Golole catchment. The Revised Universal Soil Loss Equation (RUSLE) constituting the main agents of soil erosion was modeled in a Geographical Information System (GIS) environment. The objective of this study was to model soil erosion for land management in the ungauged Golole catchment. The Golole catchment soil erosion map reveals that within the catchment the soil loss was not homogeneous and erosion risk was not the same. The catchment experiences an annual mean score soil loss rate of 279 t/ha that is above the recommended maximum allowable annual soil loss rate of 4 t/ha. The catchment's soil loss rate is described as high and severe representing 70% and 30% of landmass respectively. This study found the need to decelerate the above soil loss rates to moderate and low levels by adopting soil erosion mitigation measures such as stone contour ridges, manure, strip cropping, and terracing in the cultivated areas and controlled grazing in the lowland rangeland. The study strongly felt the need to protect the forest reserve from tree cutting and further human encroachment. This study concludes that there is the need for further research 1) in the forest reserve areas that showed the greatest rates of soil erosion menace to determine the underlying causes, and 2) to assess the temporal trends of the soil erosion hazard using high-resolution data.

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Section: Discussionmentioning

confidence: 99%

“…Severe erosion prone areas can be delineated and prioritized for erosion mitigation [8]. Effective soil erosion control requires prediction of the amount of soil loss [5] [9].…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Modelling Soil Erosion for Land Management in Ungauged Golole Catchment in Marsabit County, Kenya

Njiru¹,

Kariuki²,

Mwetu³

2018

OJSS

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“…It is thus used to quantify the effect of raindrop and induced runoff on bare soil [Efthimiou, 2014]. It was calculated using the daily rainfall values summarized as monthly and annual rainfall based on similar approaches used or reported by [Efthimiou, 2014;Okorafor, 2017;Rahaman, 2015;Sholagberu, 2016;Ufoegbune, 2011]. A rainfall erosivity index was calculated from the summaries using the modified Fournier index [Essel, 2016] and the resulting values were used to prepare a map.…”

Section: Methodsmentioning

confidence: 99%

Assessing the Threat of Erosion to Nature-Based Interventions for Stormwater Management and Flood Control in the Greater Accra Metropolitan Area, Ghana

Asiedu¹

2018

J. Ecol. Eng.

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Perennial flooding has become a major feature in urban areas in developing economies generating research interest towards finding alternative approaches to stormwater management which could complement the existing systems and help address the challenge of flooding. One of such alternative approaches is nature-based stormwater management and flood control, the implementation of which could be affected by soil erosion. This paper, as part of a wider research, was developed to determine the extent of the threat of soil erosion to stormwater management in an urban area on the example of Greater Accra Metropolitan Area, Accra Ghana as the focus of the research. Landsat 8 images (2014) were used in the research to prepare the Landcover maps. Daily rainfall data from 6 raingauge stations from 1972 to 2014 were utilized to prepare the rainfall erosivity factor maps, whereas DEM was used to prepare the slope and slope length (SL) factor maps. The land cover map with an overall accuracy of 73.6 and Kappa 0.7122 was combined with literature sources to prepare the vegetative cover factor map, and conservation practice factor map. A soil series map, prepared and updated with literature sources and data from the Harmonized World Soil Database on physical parameters, was used to calculate the soil erodibility factor (K factor) for each soil series. These were integrated into RUSLE model as 30 m raster maps to generate a soil loss map at tons/ha/yr. The results produced rainfall erosivity index values based on the modified Fournier index ranging between 0.058 and 23.197 which is classified as low. Low soil erodibility factor (K) ranging between 2.9×10 -5 and 8.5×10 -2 t ha/MJ mm indicated low susceptibility to erosion, SL factor value showing areas of low to almost flat relief with a few isolated areas of moderate slope length were generated. A soil loss of 69,5918 tons/ha/yr classified the soils as having high potential soil loss. The results showed a very low soil loss threat of 0-5.1853 tons/Ha/yr for more than 90% of the study area. Targeted intervention for source areas with high potential soil loss will contain any threat of erosion and sediment yield to the implementation of an infiltration-based stormwater management and flood control system.

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“…Cropping in the area is paddy (grain corn). The crop type factor (C) is taken as 0.4 Table - [37] ., …”

Section: The Cover Management Factor (C)mentioning

confidence: 99%

Management of Soil Losses in South Mahanadi Delta, India

Mishra¹,

Das²

2017

IJEE

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Estimation of Annual Average Soil Loss, Based on Rusle Model in Kallar Watershed, Bhavani Basin, Tamil Nadu, India

Cited by 40 publications

References 29 publications

Modelling Soil Erosion for Land Management in Ungauged Golole Catchment in Marsabit County, Kenya

Modelling Soil Erosion for Land Management in Ungauged Golole Catchment in Marsabit County, Kenya

Assessing the Threat of Erosion to Nature-Based Interventions for Stormwater Management and Flood Control in the Greater Accra Metropolitan Area, Ghana

Management of Soil Losses in South Mahanadi Delta, India

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