Effect of slope length, aspect and phosphogypsum on runoff and erosion from steep slopes

Soil Research ◽  
1991 ◽  
Vol 29 (2) ◽  
pp. 197 ◽  
Author(s):  
M Agassi ◽  
M Ben-Hur
Keyword(s):  
Soil Loss ◽  
Slope Length ◽  
Erosivity Index ◽  
Steep Slopes

The efficiency of phsophogypsum as an amendment for controlling erosion on embankments was studied on a Typic Rhodoxeralf soil, with 48% slope and 10 and 1.5 m long plots, with western and northern aspects and a westerly dominant wind during rainstorms. Phosphogypsum reduced the runoff by 23%, and the erosion was 2-3 times less than on the control slope. The embarkment's aspect has no effect on the amounts of rainfall and runoff, but the erosion from the long plots with a western aspect compared with the long plots with a northern aspect was 1.4 and 2.5 times higher in the control and phosphogypsum treatments, respectively. The length of the plots has no effect on the runoff, however, soil loss was 6.4 times higher in the longer plots. High correlations were found between the amount of erosion and the erosivity index or the runoff amount.

Slope Length Effects on Soil Loss for Steep Slopes

2000 ◽  
Vol 64 (5) ◽  
pp. 1759-1763 ◽  
Author(s):  
B. Y. Liu ◽  
M. A. Nearing ◽  
P. J. Shi ◽  
Z. W. Jia
Keyword(s):  
Soil Loss ◽  
Slope Length ◽  
Steep Slopes ◽  
Length Effects

scholarly journals GIS Based Universal Soil Erosion Estimation in District Chakwal Punjab, Pakistan

2020 ◽  
Vol 11 (2) ◽  
pp. 30-36
Author(s):  
Saima Siddiqui ◽  
Mirza Wajid Ali Safi ◽  
Aqil Tariq ◽  
Naveed Ur Rehman ◽  
Syed Waseem Haider
Keyword(s):  
Soil Erosion ◽  
Soil Loss ◽  
Management Practices ◽  
Rainwater Harvesting ◽  
Landsat 8 ◽  
River Channels ◽  
Slope Length ◽  
Raster Calculator ◽  
Aster Gdem ◽  
Steep Slopes

Soil erosion is a serious environmental problem faced by district Chakwal. Unpredictable short term and high intensity rainfall, improper cultivation and deforestation have accelerated the soil erosion in the district. The agricultural productivity of the study area can be enhanced by understanding, estimating and controlling the root causes of soil erosion. This study was undertaken to estimate and spatially represent the rate of average annual soil erosion in Chakwal using GIS/RS techniques. The soil erosion was estimated using Universal Soil Loss Equation (USLE) model. To find out parameters of USLE, ASTER GDEM of 30 m resolution was used to estimate slope length and elevation of the study area. Landsat 8 satellite imagery of year 2019, was used to prepare land use map using supervised classification. Soil map with texture and geomorphology was used to identify soils of study area and rainfall data of last 7 years was also studied. Finally, the soil loss has been computed using raster calculator of ArcGIS 10.2 software. The average annual soil loss was predicted up to 268,619 tons/acre/year, of which maximum soil erosion was occurring near the steep slopes and river channels. It is necessary to adapt sustainable land management practices to reduce the risk of further soil erosion, by adopting rainwater harvesting and choosing right crops for suitable soil types.

scholarly journals Impact of mechanisation on soil loss in terraced vineyard landscapes

2019 ◽  
Vol 45 (1) ◽  
pp. 287 ◽  
Author(s):  
A. Pijl ◽  
P. Barneveld ◽  
L. Mauri ◽  
E. Borsato ◽  
S. Grigolato ◽  
...  
Keyword(s):  
Soil Compaction ◽  
Soil Loss ◽  
Agricultural Landscapes ◽  
Local Economies ◽  
Slope Length ◽  
Erosion Rates ◽  
North Eastern ◽  
Steep Slopes ◽  
The Impact ◽  
Soil Loss Rate

Soil loss poses a threat to hilly and mountainous areas, particularly where local economies strongly depend on agricultural production. Among agricultural landscapes, vineyards are responsible for the highest erosion rates, particularly in steep-slope landscapes. The impact of vineyard mechanisation on soil loss is only marginally explored in published literature. This study provides an estimation of the annual soil loss rate by application of the Revised Universal Soil Loss Equation (RUSLE) in 24 terraced vineyards located in north-eastern Italy. Field observations showed that 13 vineyards consisted of fully mechanised fields, 5 vineyards had no form of mechanisation, while in 6 vineyards a mixture of practices was found. Soil erodibility (K factor) was derived for these practices (based on soil characteristics and varying degrees of compaction), while slope length and steepness (LS factors) were calculated from a 1-m LiDAR-based DTM, and remaining factors were based on datasets by the European Soil Data Centre. Mechanised fields showed 29% higher erosion rates than non-mechanised fields (respectively 53.9 and 69.5 t ha-1 y-1), although this is not statistically significant. Still, the direct impact of mechanisation is underestimated in this comparison, due to the predominant steep slopes in the manually cultivated fields. Furthermore, estimated soil loss from mechanised fields in addition to mechanised paths and roads is significantly higher by 37% than non-mechanised fields. This study thus offers an indication of how machinery and related soil compaction and transformation of terraces and infrastructure, increases soil loss risk.

Estimating storm erosion with a rainfall simulator

1997 ◽  
Vol 77 (4) ◽  
pp. 669-676 ◽  
Author(s):  
S. C. Nolan ◽  
L. J. P. van Vliet ◽  
T. W. Goddard ◽  
T. K. Flesch
Keyword(s):  
Soil Loss ◽  
Conventional Tillage ◽  
Reduced Tillage ◽  
Natural Soil ◽  
Rainfall Erosivity ◽  
Slope Length ◽  
Rainfall Simulator ◽  
Rainfall Events ◽  
Plot Size ◽  
Natural Rainfall

Interpreting soil loss from rainfall simulators is complicated by the uncertain relationship between simulated and natural rainstorms. Our objective was to develop and test a method for estimating soil loss from natural rainfall using a portable rainfall simulator (1 m2 plot size). Soil loss from 12 rainstorms was measured on 144-m2 plots with barley residue in conventional tillage (CT), reduced tillage (RT) and zero tillage (ZT) conditions. A corresponding "simulated" soil loss was calculated by matching the simulator erosivity to each storm's erosivity. High (140 mm h−1) and low (60 mm h−1) simulation intensities were examined. The best agreement between simulated and natural soil loss occurred using the low intensity, after making three adjustments. The first was to compensate for the 38% lower kinetic energy of the simulator compared with natural rain. The second was for the smaller slope length of the simulator plot. The third was to begin calculating simulator erosivity only after runoff began. After these adjustments, the simulated soil loss over all storms was 99% of the natural soil loss for CT, 112% for RT and 95% for ZT. Our results show that rainfall simulators can successfully estimate soil loss from natural rainfall events. Key words: Natural rainfall events, simulated rainfall, erosivity, tillage

scholarly journals Soil Erosion Susceptibility Prediction in Railway Corridors Using RUSLE, Soil Degradation Index and the New Normalized Difference Railway Erosivity Index (NDReLI)

2022 ◽  
Vol 14 (2) ◽  
pp. 348
Author(s):  
Yashon O. Ouma ◽  
Lone Lottering ◽  
Ryutaro Tateishi
Keyword(s):  
Soil Erosion ◽  
Soil Loss ◽  
Soil Degradation ◽  
Bare Soil ◽  
Landsat 8 ◽  
Arid Environments ◽  
Degradation Index ◽  
Erosivity Index ◽  
Loss Index ◽  
Very High

This study presents a remote sensing-based index for the prediction of soil erosion susceptibility within railway corridors. The empirically derived index, Normalized Difference Railway Erosivity Index (NDReLI), is based on the Landsat-8 SWIR spectral reflectances and takes into account the bare soil and vegetation reflectances especially in semi-arid environments. For the case study of the Botswana Railway Corridor (BRC), the NDReLI results are compared with the RUSLE and the Soil Degradation Index (SDI). The RUSLE model showed that within the BRC, the mean annual soil loss index was at 0.139 ton ha−1 year−1, and only about 1% of the corridor area is susceptible to high (1.423–3.053 ton ha−1 year−1) and very high (3.053–5.854 ton ha−1 year−1) soil loss, while SDI estimated 19.4% of the railway corridor as vulnerable to soil degradation. NDReLI results based on SWIR1 (1.57–1.65 μm) predicted the most vulnerable areas, with a very high erosivity index (0.36–0.95), while SWIR2 (2.11–2.29 μm) predicted the same regions at a high erosivity index (0.13–0.36). From empirical validation using previous soil erosion events within the BRC, the proposed NDReLI performed better that the RUSLE and SDI models in the prediction of the spatial locations and extents of susceptibility to soil erosion within the BRC.

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 Sediment Yield and Soil Loss Estimation Using GIS Based Soil Erosion Model: A Case Study in the MAN Catchment, Madhya Pradesh, India

2021 ◽  
Vol 8 (1) ◽  
pp. 26
Author(s):  
Manti Patil ◽  
Radheshyam Patel ◽  
Arnab Saha
Keyword(s):  
Spatial Distribution ◽  
Soil Erosion ◽  
Soil Loss ◽  
Crop Production ◽  
Erosion Rate ◽  
Agricultural Land ◽  
Reservoir Sedimentation ◽  
Reservoir Water ◽  
Slope Length ◽  
Soil Erosion Rate

Soil erosion is one of the most critical environmental hazards of recent times. It broadly affects to agricultural land and reservoir sedimentation and its consequences are very harmful. In agricultural land, soil erosion affects the fertility of soil and its composition, crop production, soil quality and land quality, yield and crop quality, infiltration rate and water holding capacity, organic matter and plant nutrient and groundwater regimes. In reservoir sedimentation process the consequences of soil erosion process are reduction of the reservoir capacity, life of reservoir, water supply, power generation etc. Based on these two aspects, an attempt has been made to the present study utilizing Revised Universal Soil Loss Equation (RUSLE) has been used in integration with remote sensing and GIS techniques to assess the spatial pattern of annual rate of soil erosion, average annual soil erosion rate and erosion prone areas in the MAN catchment. The RUSLE considers several factors such as rainfall, soil erodibility, slope length and steepness, land use and land cover and erosion control practice for soil erosion prediction. In the present study, it is found that average annual soil erosion rate for the MAN catchment is 13.01-tons/ha/year, which is higher than that of adopted and recommended values for the project. It has been found that 53% area of the MAN catchment has negligible soil erosion rate (less than 2-tons/ha/year). Its spatial distribution found on flat land of upper MAN catchment. It has been detected that 26% area of MAN catchment has moderate to extremely severe soil erosion rate (greater than 10-tons/ha/year). Its spatial distribution has been found on undulated topography of the middle MAN catchment. It is proposed to treat this area by catchment area treatment activity.

Runooff ratio as a factor in the empirical modelling of soil erosion by individual rainstorms

Soil Research ◽  
1997 ◽  
Vol 35 (1) ◽  
pp. 1 ◽  
Author(s):  
P. I. A. Kinnell
Keyword(s):  
Soil Loss ◽  
Sediment Concentration ◽  
Rill Erosion ◽  
Local Conditions ◽  
Empirical Modelling ◽  
Control Practice ◽  
Number Of Factors ◽  
Erosion Models ◽  
Erosivity Index ◽  
Bare Fallow

A number of factors that influence erosion have separate and differing effects on flow discharge and sediment concentration, depending on local conditions. Empirical erosion models that do not have mechanisms to help account for these separate and differing effects often lack the capacity to predict event erosion adequately in many locations. In this paper, the product of the EI30 index, the erosivity index used in the Universal Soil Loss Equation (USLE) and the revised version (RUSLE), and the runoff ratio (QR) is discussed in relation to its capacity to act as an event erosivity index where sheet and rill erosion occur either separately or together in a rainstorm. An analysis of runoff and soil loss data shows the index to be superior to the EI30 index as an event erosivity index for storms on bare fallow plots at Holly Springs, Mississippi. The inclusion of runoff as an independent term in the USLE/RUSLE results in a need to determine new values for the soil erodibility factor, K. Existing USLE/RUSLE equations for determining L and S (topographic factors), C (a crop and crop management factor), and P (an erosion control practice factor) may be used as first approximations provided that the values of the new index are determined for the unit plot condition. Since many of the factors that determine L, S, C, and P influence runoff, new methods to determine these parameters need to be developed in the future if the new index is to be used most effectively.

GIS estimation of annual average soil loss rate from Hangar River watershed using RUSLE

2019 ◽  
Vol 11 (2) ◽  
pp. 529-539 ◽  
Author(s):  
Mahmud Mustefa ◽  
Fekadu Fufa ◽  
Wakjira Takala
Keyword(s):  
Soil Erosion ◽  
Soil Loss ◽  
Loss Rate ◽  
Study Finding ◽  
Mitigation Measures ◽  
Rainfall Erosivity ◽  
Vegetative Cover ◽  
Annual Average ◽  
Slope Length ◽  
Soil Loss Rate

Abstract Currently, soil erosion is the major environmental problem in the Blue Nile, Hangar watershed in particular. This study aimed to estimate the spatially distributed mean annual soil erosion and map the most vulnerable areas in Hangar watershed using the revised universal soil loss equation. In this model, rainfall erosivity (R-factor), soil erodibility (K-factor), slope steepness and slope length (LS-factor), vegetative cover (C-factor), and conservation practice (P-factor) were considered as the influencing factors. Maps of these factors were generated and integrated in ArcGIS and then the annual average soil erosion rate was determined. The result of the analysis showed that the amount of soil loss from the study area ranges from 1 to 500 tha−1 yr−1 with an average annual soil loss rate of 32 tha−1 yr−1. Considering contour ploughing with terracing as a fully developed watershed management, the resulting soil loss rate was reduced from 32 to 19.2 tha−1 yr−1. Hence, applying contour ploughing with terracing effectively reduces the vulnerability of the watershed by 40%. Based on the spatial vulnerability of the watershed, most critical soil erosion areas were situated in the steepest part of the watershed. The result of the study finding is helpful for stakeholders to take appropriate mitigation measures.

scholarly journals Quantification of Soil Losses along the Coastal Protected Areas in Kenya

Land ◽  
2020 ◽  
Vol 9 (5) ◽  
pp. 137 ◽  
Author(s):  
Yves Hategekimana ◽  
Mona Allam ◽  
Qingyan Meng ◽  
Yueping Nie ◽  
Elhag Mohamed
Keyword(s):  
Soil Erosion ◽  
Protected Areas ◽  
Coastal Area ◽  
Soil Loss ◽  
Meteorological Data ◽  
Coastal Areas ◽  
Slope Length ◽  
Input Parameters ◽  
Slope Factor ◽  
Factor C

Monitoring of improper soil erosion empowered by water is constantly adding more risk to the natural resource mitigation scenarios, especially in developing countries. The demographical pattern and the rate of growth, in addition to the impairments of the rainfall pattern, are consequently disposed to adverse environmental disturbances. The current research goal is to evaluate soil erosion triggered by water in the coastal area of Kenya on the district level, and also in protected areas. The Revised Universal Soil Loss Equation (RUSLE) model was exercised to estimate the soil loss in the designated study area. RUSLE input parameters were functionally realized in terms of rainfall and runoff erosivity factor (R), soil erodibility factor (K), slope length and gradient factor (LS), land cover management factor (C) and slope factor (P). The realization of RUSLE input parameters was carried out using different dataset sources, including meteorological data, soil/geology maps, the Digital Elevation Model (DEM) and processing of satellite imagery. Out of 26 districts in coastal area, eight districts were projected to have mean annual soil loss rates of >10 t·ha−1·y−1: Kololenli (19.709 t·ha−1·y−1), Kubo (14.36 t·ha−1·y−1), Matuga (19.32 t·ha−1·y−1), Changamwe (26.7 t·ha−1·y−1), Kisauni (16.23 t·ha−1·y−1), Likoni (27.9 t·ha−1·y−1), Mwatate (15.9 t·ha−1·y−1) and Wundanyi (26.51 t·ha−1·y−1). Out of 34 protected areas at the coastal areas, only four were projected to have high soil loss estimation rates >10 t·ha−1·y−1: Taita Hills (11.12 t·ha−1·y−1), Gonja (18.52 t·ha−1·y−1), Mailuganji (13.75.74 t·ha−1·y−1), and Shimba Hills (15.06 t·ha−1·y−1). In order to mitigate soil erosion in Kenya’s coastal areas, it is crucial to regulate the anthropogenic disturbances embedded mainly in deforestation of the timberlands, in addition to the natural deforestation process caused by the wildfires.

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