scholarly journals Assessment of soil erosion risk in Komering watershed, South Sumatera, using SWAT model

2017 ◽  
Author(s):  
A. Salsabilla ◽  
E. Kusratmoko
Keyword(s):  
Soil Erosion ◽  
Swat Model ◽  
Erosion Risk ◽  
Soil Erosion Risk

Assessment of soil erosion risk using SWAT model

2012 ◽  
Vol 6 (10) ◽  
pp. 4011-4019 ◽  
Author(s):  
Manel Mosbahi ◽  
Sihem Benabdallah ◽  
Mohamed Rached Boussema
Keyword(s):  
Soil Erosion ◽  
Swat Model ◽  
Erosion Risk ◽  
Soil Erosion Risk

Soil erosion risk and its spatial pattern in upstream area of Guanting reservoir

2011 ◽  
Vol 65 (1) ◽  
pp. 221-229 ◽  
Author(s):  
Xi Wang Zhang ◽  
Bing Fang Wu ◽  
Xiao Song Li ◽  
Shan Long Lu
Keyword(s):  
Soil Erosion ◽  
Spatial Pattern ◽  
Erosion Risk ◽  
Guanting Reservoir ◽  
Soil Erosion Risk

scholarly journals Soil Erosion Estimation Using Remote Sensing Techniques in Wadi Yalamlam Basin, Saudi Arabia

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Jarbou A. Bahrawi ◽  
Mohamed Elhag ◽  
Amal Y. Aldhebiani ◽  
Hanaa K. Galal ◽  
Ahmad K. Hegazy ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Saudi Arabia ◽  
Soil Erosion ◽  
Decision Support Tool ◽  
Data Availability ◽  
Productive Capacity ◽  
Support Tool ◽  
Erosion Risk ◽  
Soil Erosion Risk ◽  
Key Aspects

Soil erosion is one of the major environmental problems in terms of soil degradation in Saudi Arabia. Soil erosion leads to significant on- and off-site impacts such as significant decrease in the productive capacity of the land and sedimentation. The key aspects influencing the quantity of soil erosion mainly rely on the vegetation cover, topography, soil type, and climate. This research studies the quantification of soil erosion under different levels of data availability in Wadi Yalamlam. Remote Sensing (RS) and Geographic Information Systems (GIS) techniques have been implemented for the assessment of the data, applying the Revised Universal Soil Loss Equation (RUSLE) for the calculation of the risk of erosion. Thirty-four soil samples were randomly selected for the calculation of the erodibility factor, based on calculating theK-factor values derived from soil property surfaces after interpolating soil sampling points. Soil erosion risk map was reclassified into five erosion risk classes and 19.3% of the Wadi Yalamlam is under very severe risk (37,740 ha). GIS and RS proved to be powerful instruments for mapping soil erosion risk, providing sufficient tools for the analytical part of this research. The mapping results certified the role of RUSLE as a decision support tool.

scholarly journals Soil erosion risk, Sicilian Region (1:250,000 scale)

2014 ◽  
Vol 11 (2) ◽  
pp. 323-341 ◽  
Author(s):  
M. Fantappiè ◽  
S. Priori ◽  
E.A.C. Costantini
Keyword(s):  
Soil Erosion ◽  
Erosion Risk ◽  
Soil Erosion Risk

Intersecting RUSLE modelled and farmers perceived soil erosion risk in the conservation domain on mountain Elgon in Uganda

2021 ◽  
Vol 126 ◽  
pp. 102366
Author(s):  
Yazidhi Bamutaze ◽  
Paul Mukwaya ◽  
Shuichi Oyama ◽  
Daniel Nadhomi ◽  
Peter Nsemire
Keyword(s):  
Soil Erosion ◽  
Erosion Risk ◽  
Soil Erosion Risk
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