Estimation of soil erosion in a semi-arid watershed of Tamil Nadu (India) using revised universal soil loss equation (rusle) model through GIS

Balasubramani, K.; Veena, M.P.; Kumaraswamy, K.; Saravanabavan, V.

doi:10.1007/s40808-015-0015-4

Cited by 79 publications

(34 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The topographic factor is the expected ratio of soil loss per unit area, from a field slope to that from a 22.13 m length of uniform 9 % slope, under identical conditions (Shinde et al, 2010;Balasubramani et al, 2015). It represents the effect of topography on erosion.…”

Section: Topographic Factor (Ls)mentioning

confidence: 99%

“…Earlier researchers have used various models for estimating soil loss at catchment, regional and global scales such as Universal Soil Loss Equation (USLE), Revised Universal Soil Loss Equation (RUSLE), Water Erosion Prediction Project (WEPP), Soil and Water Assessment tool (SWAT), Agricultural Non-Point Source Pollution Model (AGNPS).The RUSLE has been widely adopted for soil loss estimation at the watershed scale, because of its convenience in computation and application (Balasubramani et al, 2015).Although, it is an empirical model, the combined use of remote sensing, Geographical Information System (GIS) and RUSLE techniques makes soil erosion estimation and its spatial distribution feasible within reasonable costs and better accuracy, in larger areas (Rejani et al, 2016).RUSLE computes the average annual soil loss from the catchment using factors, such as rainfall runoff erosivity (R), soil erodibility (K), topography(LS), cover management(C) and conservation practice(P).The present study focuses on the estimation of spatial and temporal variation of C-factor and soil erosion in a semi-arid watershed of Mahabubnagar district, using RUSLE coupled with GIS and its application, for the sustainable management of the watershed.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Spatial and Temporal Variation of C– Factor and Soil Erosion in a Semi-Arid Watershed: A Case Study in Mahabubnagar District

al.¹

2017

IJASR

View full text Add to dashboard Cite

show abstract

Section: Topographic Factor (Ls)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Spatial and Temporal Variation of C– Factor and Soil Erosion in a Semi-Arid Watershed: A Case Study in Mahabubnagar District

al.¹

2017

IJASR

View full text Add to dashboard Cite

show abstract

“…Each model has its own characteristics and application scopes (Boggs et al, 2001;Lu et al, 2004;Dabral et al, 2008;Tian et al, 2009). USLE, RUSLE, RUSLE2 & RUSLE-3D, model has been widely used for spatial prediction of soil loss and erosion risk potential, because of its convenience in application and compatibility with GIS (Millward and Mersey, 1999;Jain et al, 2001;Lu et al, 2004;Jasrotia and Singh, 2006;Dabral et al, 2008;Kouli et al, 2009;Pandey et al, 2009;Bonilla et al, 2010., Parket al2011, Kumar, S., Kushwaha, S. P. S., 2013and Balasubramani K et al, 2015.…”

Section: Multiple Modelling Approachmentioning

confidence: 99%

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

Rahaman

Aruchamy

Jegankumar

et al. 2015

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

ABSTRACT:Soil erosion is a widespread environmental challenge faced in Kallar watershed nowadays. Erosion is defined as the movement of soil by water and wind, and it occurs in Kallar watershed under a wide range of land uses. Erosion by water can be dramatic during storm events, resulting in wash-outs and gullies. It can also be insidious, occurring as sheet and rill erosion during heavy rains. Most of the soil lost by water erosion is by the processes of sheet and rill erosion. Land degradation and subsequent soil erosion and sedimentation play a significant role in impairing water resources within sub watersheds, watersheds and basins. Using conventional methods to assess soil erosion risk is expensive and time consuming. A comprehensive methodology that integrates Remote sensing and Geographic Information Systems (GIS), coupled with the use of an empirical model (Revised Universal Soil Loss Equation-RUSLE) to assess risk, can identify and assess soil erosion potential and estimate the value of soil loss. GIS data layers including, rainfall erosivity (R), soil erodability (K), slope length and steepness (LS), cover management (C) and conservation practice (P) factors were computed to determine their effects on average annual soil loss in the study area. The final map of annual soil erosion shows a maximum soil loss of 398.58 t/ h -1 / y -1 . Based on the result soil erosion was classified in to soil erosion severity map with five classes, very low, low, moderate, high and critical respectively. Further RUSLE factors has been broken into two categories, soil erosion susceptibility (A=RKLS), and soil erosion hazard (A=RKLSCP) have been computed. It is understood that functions of C and P are factors that can be controlled and thus can greatly reduce soil loss through management and conservational measures.

show abstract

“…The estimation of soil loss in sub watersheds were carried out using different prediction techniques (Shrestha, 1997;Dougals, 2006;Van De et al, 2008). Watershed forms a natural boundary to focus on runoff, and hence a systematic assessment of runoff and soil erosion within the watershed would provide reliable information to draw strategies for sustainable development of watershed resources (Balasubramani et al, 2015). The major models applied worldwide to estimate soil loss are Universal Soil Loss Equation (USLE), Revised Universal Soil Loss Equation (RUSLE), Water Erosion Prediction Project (WEPP), Soil Erosion Model for Mediterranean Regions (SEMMED), Soil and Water Assessment tool (SWAT), European Soil Erosion Model (EUROSEM), Agricultural Non-Point Source Pollution Model (AGNPS) etc.…”

Section: Introductionmentioning

confidence: 99%

“…RUSLE helps to predict the soil erosion from unguaged watersheds at reasonable cost and better accuracy by considering the hydro climatic conditions and spatial heterogeneity of the soil (Angima et al, 2003). The RUSLE has been widely adopted for soil loss estimation at watershed scale because of its convenience in computation and application (Balasubramani et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Estimation of Soil Loss Under Changing Climatic Scenarios in Semi-Arid Watersheds

Rao¹,

Rejani²,

Yogitha³

et al. 2016

Ind. Jour. of Dryl. Agri. Rese. and Deve.

View full text Add to dashboard Cite

Spatial and temporal estimation of soil loss is very essential for the sustainable planning and management of watersheds. In the present study, an attempt was made to estimate the soil loss spatially and temporally using RUSLE from a dry semi-arid watershed (Goparajpalli in Warangal District) and a wet semi-arid watershed (Seethagondi in Adilabad District) under changing climatic scenarios using PRECIS data. In the dry semi-arid watershed, the annual rainfall varied from 390 to 1181 mm with a mean value of 735 mm and a mean erosivity of 6260 MJ mm/ha/h/y. The mean annual rainfall during the base line period (1961-1990), mid (2021-2050) and end centuries in this watershed were 738, 835 and 777 mm, respectively. The mean erosivity during these periods were 5657, 9253 and 7170 MJ mm/ha/h/y and soil loss from crop land were 2.39, 4.02 and 3.14 t/ha/y, respectively. In the wet semi-arid watershed, the annual rainfall varied from 508 to 1351 mm with a mean value of 950 mm and a mean erosivity of 6789 MJ mm/ha/h/y. The mean annual rainfall during base line, mid and end centuries in this wet semi-arid watershed were 956,1088 and 1124 mm and erosivities were 10547,14437 and 14755 MJ mm/ha/h/y, respectively. Similarly, the soil loss from crop land during these periods were 9.18, 13.11 and 14.11 t/ha/y. Even though, the soil loss from the dry semi-arid watershed was relatively lower than the wet semi-arid watershed, it showed an increasing trend in the mid century and a decreasing trend in the end century whereas, in the wet semi-arid watershed, it showed an increasing trend in both mid and end centuries. Considerable spatial variation in the mean annual soil loss was observed in both the wet and dry semi-arid watersheds during base line period, mid and end centuries.

show abstract

Estimation of soil erosion in a semi-arid watershed of Tamil Nadu (India) using revised universal soil loss equation (rusle) model through GIS

Cited by 79 publications

References 19 publications

Spatial and Temporal Variation of C– Factor and Soil Erosion in a Semi-Arid Watershed: A Case Study in Mahabubnagar District

Spatial and Temporal Variation of C– Factor and Soil Erosion in a Semi-Arid Watershed: A Case Study in Mahabubnagar District

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

Estimation of Soil Loss Under Changing Climatic Scenarios in Semi-Arid Watersheds

Contact Info

Product

Resources

About