Soil erosion prediction using the Revised Universal Soil Loss Equation (RUSLE) in a GIS framework, Chania, Northwestern Crete, Greece

2008 ◽  
Vol 57 (3) ◽  
pp. 483-497 ◽  
Author(s):  
Maria Kouli ◽  
Pantelis Soupios ◽  
Filippos Vallianatos
Keyword(s):  
Soil Erosion ◽  
Soil Loss ◽  
Universal Soil Loss Equation ◽  
Erosion Prediction ◽  
Soil Loss Equation

scholarly journals PENDUGAAN EROSI TANAH PADA LAHAN BEKAS TAMBANG BATU BINTANG SUB DAS BATANG KALULUTAN DAN SUNGAI IPUH KABUPATEN PADANG PARIAMAN

Jurnal Solum ◽  
2008 ◽  
Vol 5 (2) ◽  
pp. 88
Author(s):  
Adrinal Adrinal ◽  
Utri Luki ◽  
Pedri Kasman
Keyword(s):  
Soil Erosion ◽  
Soil Loss ◽  
Universal Soil Loss Equation ◽  
Erosion Risk ◽  
Erosion Prediction ◽  
Risk Rate ◽  
Soil Loss Equation

An erosion prediction using Universal Soil Loss Equation (USLE) was conducted from August 2006 to February 2007.  The research was aimed to predict amount of soil erosion and erosion risk rate of ex-obsidian mining of Kalulutan and Ipuh River, Padang Pariaman District.  The result showed that the highest soil erosion was predicted under ex-obsidian mining (45% slope) namely 2.593 t/ha/y and the lowest was found under mixed farm (3-8% slope), 4,95 t/ha/y. Erosion risk rate of ex-obsidian mining was very heavy whilst for mixed farm varied from light to very heavy.   Keyword: erosion prediction, ex-obsidian mining, Sub-catchments area

scholarly journals SOIL EROSION PREDICTION AT THE BASIN SCALE USING THE REVISED UNIVERSAL SOIL LOSS EQUATION (RUSLE) IN A SONGBE’S BASIN

2011 ◽  
Vol 14 (4) ◽  
pp. 86-96
Author(s):  
Tu Tuan Tran
Keyword(s):  
Soil Erosion ◽  
Soil Loss ◽  
Measurement Data ◽  
Rain Gauge ◽  
Universal Soil Loss Equation ◽  
Agricultural Ecosystem ◽  
Erosion Prediction ◽  
Basin Scale ◽  
P Factor ◽  
Soil Loss Equation

Soil water erosion is a serious environmental problem affecting large areas of the agricultural ecosystem in Nambo Eastern. Soil erosion not only affects soil quality, in terms of agricultural productivity, but also reduces the availability of water in reservoirs. This study was conducted in the Song Be watershed in Nambo Eastern, to predict potential annual soil loss using the revised universal soil loss equation (RUSLE). The RUSLE factors were calculated for the Song Be watershed: using survey data and rain gauge measurement data. TheRfactor was calculated from annual precipitation data. The K-factor was calculated from soil map scale 1/100000. The LS topographic factor was calculated from a 90 m digital elevation model. The C-factor was calculated from Landsat image. P-factor in absence of detailed data, were set to 1.

scholarly journals 391 C-factor Research on Horticultural Crops for Erosion Prediction: Philosophy and Methodology of Data Collection

HortScience ◽  
2000 ◽  
Vol 35 (3) ◽  
pp. 460C-460
Author(s):  
G.K. Panicker ◽  
G.A. Weesies ◽  
A.H. Al-Humadi ◽  
C. Sims ◽  
L.C. Huam ◽  
...  
Keyword(s):  
Soil Erosion ◽  
Data Collection ◽  
Soil Loss ◽  
Prediction Models ◽  
C:N Ratio ◽  
Universal Soil Loss Equation ◽  
Area Index ◽  
Horticultural Crops ◽  
Erosion Prediction ◽  
Soil Loss Equation

Even though research and education systems have transformed agriculture from a traditional to a high-technology sector, soil erosion still remains as a major universal problem to agricultural productivity. The Universal Soil Loss Equation (USLE) and its replacement, the Revised Universal Soil Loss Equation (RUSLE) are the most widely used of all soil erosion prediction models. Of the five factors in RUSLE, the cover and management (C) factor is the most important one from the standpoint of conservation planning because land use changes meant to reduce erosion are represented here. Even though the RUSLE is based on the USLE, this modern erosion prediction model is highly improved and updated. Alcorn State Univ. entered into a cooperative agreement with the NRCS of the USDA in 1988 to conduct C-factor research on vegetable and fruit crops. The main objective of this research is to collect plant growth and residue data that are used to populated databases needed to develop C-factors in RUSLE, and used in databases for other erosion prediction and natural resource models. The enormous data collected on leaf area index (LAI), canopy cover, lower and upper biomass, rate of residue decomposition, C:N ratio of samples of residues and destructive harvest and other gorwth parameters of canopy and rhizosphere made the project the largest data bank on horticultural crops. The philosophy and methodology of data collection will be presented.

scholarly journals Estimation of annual soil erosion dynamics (2005 - 2015) in Pakistan using Revised Universal Soil Loss Equation (RUSLE)

2021 ◽  
Author(s):  
Hammad Gilani ◽  
Adeel Ahmad ◽  
Isma Younes ◽  
Sawaid Abbas
Keyword(s):  
Soil Erosion ◽  
Soil Loss ◽  
Management Practices ◽  
Climatic Factors ◽  
Universal Soil Loss Equation ◽  
Rainfall Erosivity ◽  
Slope Length ◽  
Conservation Practice ◽  
Administrative Units ◽  
Soil Loss Equation

Abrupt changes in climatic factors, exploitation of natural resources, and land degradation contribute to soil erosion. This study provides the first comprehensive analysis of annual soil erosion dynamics in Pakistan for 2005 and 2015 using publically available climatic, topographic, soil type, and land cover geospatial datasets at 1 km spatial resolution. A well-accepted and widely applied Revised Universal Soil Loss Equation (RUSLE) was implemented for the annual soil erosion estimations and mapping by incorporating six factors; rainfall erosivity (R), soil erodibility (K), slope-length (L), slope-steepness (S), cover management (C) and conservation practice (P). We used a cross tabular or change matrix method to assess the annual soil erosion (ton/ha/year) changes (2005-2015) in terms of areas and spatial distriburtions in four soil erosion classes; i.e. Low (<1), Medium (1–5], High (5-20], and Very high (>20). Major findings of this paper indicated that, at the national scale, an estimated annual soil erosion of 1.79 ± 11.52 ton/ha/year (mean ± standard deviation) was observed in 2005, which increased to 2.47 ±18.14 ton/ha/year in 2015. Among seven administrative units of Pakistan, in Azad Jammu & Kashmir, the average soil erosion doubled from 14.44 ± 35.70 ton/ha/year in 2005 to 28.03 ± 68.24 ton/ha/year in 2015. Spatially explicit and temporal annual analysis of soil erosion provided in this study is essential for various purposes, including the soil conservation and management practices, environmental impact assessment studies, among others.

scholarly journals Modeling Soil Erosion and Landscape Metric Analysis of River Catchments in Pulau Pinang, Malaysia

2020 ◽  
Vol 55 (3) ◽  
Author(s):  
Sumayyah Aimi Mohd Najib
Keyword(s):  
Land Use ◽  
Soil Erosion ◽  
Soil Loss ◽  
Diversity Index ◽  
Land Use Changes ◽  
Shannon Diversity Index ◽  
Universal Soil Loss Equation ◽  
Data Sampling ◽  
Shannon Diversity ◽  
Soil Loss Equation

To determine the soil erosion in ungauged catchments, the author used 2 methods: Universal Soil Loss Equation model and sampling data. Sampling data were used to verify and validate data from model. Changing land use due to human activities will affect soil erosion. Land use has changed significantly during the last century in Pulau Pinang. The main rapid changes are related to agriculture, settlement, and urbanization. Because soil erosion depends on surface runoff, which is regulated by the structure of land use and brought about through changes in slope length, land-use changes are one of many factors influencing land degradation caused by erosion. The Universal Soil Loss Equation was used to estimate past soil erosion based on land uses from 1974 to 2012. Results indicated a significant increase in three land-use categories: forestry, built-up areas, and agriculture. Another method to evaluate land use changes in this study was by using landscape metrics analysis. The mean patch size of built-up area and forest increased, while agriculture land use decreased from 48.82 patches in 1974 to 22.46 patches in 2012. Soil erosion increased from an estimated 110.18 ton/km2/year in 1974 to an estimated 122.44 ton/km2/year in 2012. Soil erosion is highly related (R2 = 0.97) to the Shannon Diversity Index, which describes the diversity in land-use composition in river basins. The Shannon Diversity Index also increased between 1974 and 2012. The findings from this study can be used for future reference and for ungauged catchment research studies.

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