Soil erosion modeling of a Himalayan watershed using RS and GIS

Pandey, Ashish; Mathur, Abhisekh; Mishra, Surendra Kumar; Mal, B. C.

doi:10.1007/s12665-009-0038-0

Cited by 115 publications

(60 citation statements)

References 29 publications

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“…Information on factors leading to soil erosion can be utilized as a guide for formulating appropriate soil conservation and land management plans. The Revised Universal Soil Loss Equation (RUSLE) is frequently used to estimate the magnitude of soil erosion loss from watershed areas, the spatial distribution of soil erosion severity, and delimiting sites vulnerable to soil erosion for both agricultural and forested watersheds [30,[42][43][44][45][46][47][48]. Finally, the RUSLE model has several advantages: 1) it is easy to implement and understand from a functional perspective [32], 2) is compatible with the Geographic Information System (GIS), and 3) the data requirements to implement the model are not too complex or unattainable especially in a developing country [49].…”

Section: Soil Loss Estimation Methodsmentioning

confidence: 99%

Spatial Estimation of Soil Erosion Risk Using RUSLE Approach, RS, and GIS Techniques: A Case Study of Kufranja Watershed, Northern Jordan

Farhan¹,

Zregat²,

Farhan³

2013

JWARP

111

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Wadi Kufranja catchment (126.3 km 2 ), northern Jordan, was selected to estimate annual soil loss using the Revised Universal Soil Loss Equation (RUSLE), remote sensing (RS), and geographic information system (GIS). RUSLE factors (R, K, LS, C and P) were computed and presented by raster layers in a GIS environment, then multiplied together to predict soil erosion rates, and to generate soil erosion risk categories and soil erosion severity maps. The estimated potential average annual soil loss is 10 ton·ha . Apart from the gentle slopes of the alluvial fan (Krayma town and surroundings), the lower and the middle reaches of the watershed suffer from severe to extreme erosion risk. High terrain, slope steepness, removal of vegetation, and poor conservation practices are the most prominent causes of soil erosion. This investigation demonstrates that remote sensing (RS) and GIS technologies are effective tools in modeling erosion, thus enabling extraction of significant information for implementing soil conservation plans in the north Jordan highlands.

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Section: Soil Loss Estimation Methodsmentioning

confidence: 99%

Spatial Estimation of Soil Erosion Risk Using RUSLE Approach, RS, and GIS Techniques: A Case Study of Kufranja Watershed, Northern Jordan

Farhan¹,

Zregat²,

Farhan³

2013

JWARP

111

View full text Add to dashboard Cite

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“…We used the default bootstrapping option of the function employing a sample size of 10 5 . [59]; (e) is based on the range of rooting depth from Canadell et al [60]; (f) is based on Saxton et al [61]; (g) is based on the hydraulic conductivity of semi-pervious soils from Irmay [62]; (h) is based on Morgan and Duzant [27]; (i) is based on Manning's n of bare soil in Table 3.6 from Morgan [3].…”

Section: Sensitivity Analysis Of the Modelmentioning

confidence: 99%

Daily Based Morgan–Morgan–Finney (DMMF) Model: A Spatially Distributed Conceptual Soil Erosion Model to Simulate Complex Soil Surface Configurations

Choi

Arnhold

Huwe

et al. 2017

Water

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Abstract:In this paper, we present the Daily based Morgan-Morgan-Finney model. The main processes in this model are based on the Morgan-Morgan-Finney soil erosion model, and it is suitable for estimating surface runoff and sediment redistribution patterns in seasonal climate regions with complex surface configurations. We achieved temporal flexibility by utilizing daily time steps, which is suitable for regions with concentrated seasonal rainfall. We introduce the proportion of impervious surface cover as a parameter to reflect its impacts on soil erosion through blocking water infiltration and protecting the soil from detachment. Also, several equations and sequences of sub-processes are modified from the previous model to better represent physical processes. From the sensitivity analysis using the Sobol' method, the DMMF model shows the rational response to the input parameters which is consistent with the result from the previous versions. To evaluate the model performance, we applied the model to two potato fields in South Korea that had complex surface configurations using plastic covered ridges at various temporal periods during the monsoon season. Our new model shows acceptable performance for runoff and the sediment loss estimation (NSE ≥ 0.63, |PBIAS| ≤ 17.00, and RSR ≤ 0.57). Our findings demonstrate that the DMMF model is able to predict the surface runoff and sediment redistribution patterns for cropland with complex surface configurations.

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“…The geo-spatial technology is cost-effective and timeefficient in this regard. Fewer studies have been carried out using different methods of GIS techniques and remotely sensed data are effective in identifying and mapping land degradation risks and modeling of soil loss (Pandey et al 2007(Pandey et al , 2009Rahman et al 2009;Nagaraju et al 2011;Sinha and Joshi 2012;Nasre et al 2013;Baroudy and Moghanm 2014;Jiang et al 2015). The present study focused on the prognostic modeling capabilities of geospatial technology based soil erosion potential model to delineate the probable soil erosion potential areas in subtropical region.…”

Section: Introductionmentioning

confidence: 99%

Soil erosion risk mapping using RUSLE model on jhargram sub-division at West Bengal in India

Shit¹,

Nandi

Bhunia³

2015

Model. Earth Syst. Environ.

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An appraisal of spatial and quantitative information on soil erosion is a major challenge for human sustainability. The present study demonstrates the prognostic modeling capabilities of geo-spatial technology based on soil erosion potential model to assess the effects of implementing land use changes within sub-tropical region in India. The Revised Universal Soil Loss Equation (RUSLE) integrated with geo-spatial technology was used to produce predictive soil erosion map. Rainfall pattern, soil type, topography, crop system and management practices and geo-spatial data were used to predict the soil erosion model. Results shows 74.77 % of the study area is marked as low potential zone for soil erosion, whereas 5.58 % ([10 t h -1 year -1 ) is very high erosion risk zone. The present information may help recognizing areas that are vulnerable to soil loss and the proposed method will be used for generalized planning and assessment purposes for supervision and preserve the soil erosion.

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Soil erosion modeling of a Himalayan watershed using RS and GIS

Cited by 115 publications

References 29 publications

Spatial Estimation of Soil Erosion Risk Using RUSLE Approach, RS, and GIS Techniques: A Case Study of Kufranja Watershed, Northern Jordan

Spatial Estimation of Soil Erosion Risk Using RUSLE Approach, RS, and GIS Techniques: A Case Study of Kufranja Watershed, Northern Jordan

Daily Based Morgan–Morgan–Finney (DMMF) Model: A Spatially Distributed Conceptual Soil Erosion Model to Simulate Complex Soil Surface Configurations

Soil erosion risk mapping using RUSLE model on jhargram sub-division at West Bengal in India

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