Journal articles: 'Universal Soil Loss Equation'

1

B. Yu. "ACOMPARISON OF THE R-FACTOR IN THE UNIVERSAL SOIL LOSS EQUATION AND REVISED UNIVERSAL SOIL LOSS EQUATION." Transactions of the ASAE 42, no. 6 (1999): 1615–20. http://dx.doi.org/10.13031/2013.13327.

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Erol, A., Ö. Koşkan, and M. A. Başaran. "Socioeconomic modifications of the universal soil loss equation." Solid Earth 6, no. 3 (August 28, 2015): 1025–35. http://dx.doi.org/10.5194/se-6-1025-2015.

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Abstract. While social scientists have long focused on socioeconomic and demographic factors, physical modelers typically study soil loss using physical factors. In the current environment, it is becoming increasingly important to consider both approaches simultaneously for the conservation of soil and water, and the improvement of land use conditions. This study uses physical and socioeconomic factors to find a coefficient that evaluates the combination of these factors. It aims to determine the effect of socioeconomic factors on soil loss and, in turn, to modify the universal soil loss equation (USLE). The methodology employed in this study specifies that soil loss can be calculated and predicted by comparing the degree of soil loss in watersheds, with and without human influence, given the same overall conditions. A coefficient for socioeconomic factors, therefore, has been determined based on adjoining watersheds (WS I and II), employing simulation methods. Combinations of C and P factors were used in the USLE to find the impact of their contributions to soil loss. The results revealed that these combinations provided good estimation of soil loss amounts for the second watershed, i.e., WS II, from the adjoining watersheds studied in this work. This study shows that a coefficient of 0.008 modified the USLE to reflect the socioeconomic factors, such as settlement, influencing the amount of soil loss in the studied watersheds.

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Luvai, Allois, John Obiero, and Christian Omuto. "Soil Loss Assessment Using the Revised Universal Soil Loss Equation (RUSLE) Model." Applied and Environmental Soil Science 2022 (February 15, 2022): 1–14. http://dx.doi.org/10.1155/2022/2122554.

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Many catchment areas have suffered from exhaustive changes because of various land use activities over the recent past. These land use changes are associated with intensified environmental degradation witnessed in catchment areas. Such environmental problems include extreme soil erosion. Soil erosion is one of the most critical problems responsible for the degradation of land worldwide. This phenomenon occurs as a result of the complex interactions that exist between natural and human-induced factors. Most factors experience spatiotemporal variations, hence complicating the soil erosion phenomenon. This complexity in the erosion process makes it difficult to quantify soil loss. Without proper information on soil loss, it becomes quite hard for decision-makers and managers to manage catchment areas. However, the availability of soil erosion models has made it easy to estimate soil loss. Many models have been developed to consider these complexities in soil erosion studies. Empirical models such as RUSLE provide a simple and broad methodology through which soil erosion is assessed. The RUSLE model integrates well geographic information system (GIS) and above all remote sensing. This paper presents an overview of the developmental milestones in estimating soil loss using the RUSLE model. The parameterization of the RUSLE model has been adequately reviewed with much emphasis on challenges and successes in derivation of each individual factor. From the review, it was established that different equations have been developed by researchers for modeling the five factors for the RUSLE model. The development of such equations was found to take into account the different variations that depict the soil erosion process.

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Risse, L. M., M. A. Nearing, J. M. Laflen, and A. D. Nicks. "Error Assessment in the Universal Soil Loss Equation." Soil Science Society of America Journal 57, no. 3 (May 1993): 825–33. http://dx.doi.org/10.2136/sssaj1993.03615995005700030032x.

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Chandramohan, T., and Dilip G. Durbude. "Estimation of soil erosion potential using Universal Soil Loss Equation." Journal of the Indian Society of Remote Sensing 30, no. 4 (December 2002): 181–90. http://dx.doi.org/10.1007/bf03000361.

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Azaiez, Naima. "Improved Modelling of Soil Loss in El Badalah Basin: Comparing the Performance of the Universal Soil Loss Equation, Revised Universal Soil Loss Equation and Modified Universal Soil Loss Equation Models by Using the Magnetic and Gravimetric Prospection Outcomes." Journal of Geoscience and Environment Protection 09, no. 04 (2021): 50–73. http://dx.doi.org/10.4236/gep.2021.94005.

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Effendi Rahim, Supli, Ahmad Affandi Supli, and Nurhayati Damiri. "Soil Loss Prediction on Mobile Platform Using Universal Soil-Loss Equation (USLE) Model." MATEC Web of Conferences 97 (2017): 01066. http://dx.doi.org/10.1051/matecconf/20179701066.

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8

Erol, A., Ö. Koşkan, and M. A. Başaran. "Socio-economic modifications of the Universal Soil Loss Equation." Solid Earth Discussions 7, no. 2 (June 15, 2015): 1731–59. http://dx.doi.org/10.5194/sed-7-1731-2015.

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Abstract. While social scientists have long focused on socio-economic and demographic factors, physical modelers typically study soil loss using physical factors. In the current environment, it is becoming increasingly important to consider both approaches simultaneously for the conservation of soil and water, and the improvement of land use conditions. This study uses physical and socio-economic factors to find a coefficient that evaluates the combination of these factors. It aims to determine the effect of socio-economic factors on soil loss and, in turn, to modify the Universal Soil Loss Equation (USLE). The methodology employed in this study specifies that soil loss can be calculated and predicted by comparing the degree of soil loss in watersheds, with and without human influence, given the same overall conditions. A coefficient for socio-economic factors, therefore, has been determined based on adjoining watersheds (WS I and II), employing simulation methods. Combinations of C and P factors were used in the USLE to find the impact of their contributions on soil loss. The results revealed that these combinations provided good estimation of soil loss amounts for the second watershed, i.e. WS II, from the adjoining watersheds studied in this work. This study shows that a coefficient of 0.008 modified the USLE to reflect the socio-economic factors as settlement influencing the amount of soil loss in the watersheds studied.

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9

Joshi, Veena, Nilesh Susware, and Debasree Sinha. "Estimating soil loss from a watershed in Western Deccan, India, using Revised Universal Soil Loss Equation." Landscape & Environment 10, no. 1 (April 19, 2016): 13–25. http://dx.doi.org/10.21120/le/10/1/2.

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USLE (Universal Soil Loss Equation) is the original and the most widely accepted soil loss estimation technique till date which has evolved from a design tool for conservation planning to a research methodology all across the globe. The equation has been revised and modified over the years and became a foundation for several new soil loss models developed all around the world. The equation has been revised as RUSLE by Renard et al. (1991) and is computed in GIS environment. The Revised equation is landuse independent which makes it a useful technique to apply in a variety of environment. The present paper is an attempt to estimate soil loss from a semi-arid watershed in Western Deccan, India by employing RUSLE. The region is a rocky terrain and sediments are restricted to only a few localities. The result indicates that the region is at the threshold of soil tolerance limit.

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Jones, Bilal G., Buddhi R. Gyawali, Demetrio Zourarakis, Maheteme Gebremedhin, and George Antonious. "Soil Loss Analysis of an Eastern Kentucky Watershed Utilizing the Universal Soil Loss Equation." Environments 9, no. 10 (October 4, 2022): 126. http://dx.doi.org/10.3390/environments9100126.

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Soil erosion is the displacement of soil’s upper layer(s) triggered by a variation in topography, land use and soil types, and anthropogenic activities. This study selected the Marrowbone Creek-Russel Fork watershed in eastern Kentucky to estimate the mean annual soil loss over eight years (from 2013 to 2020) utilizing the Universal Soil Loss Equation (USLE). We included monthly precipitation, soil survey, digital elevation model (DEM), and land cover data to estimate the parameters of the USLE. The mean annual soil loss for the study area ranged from 1.77 to 2.91 Mg ha−1 yr−1 with an eight-year mean of 2.31 Mg ha−1 yr−1. In addition, we observed that developed land cover classes were less erosion-resistant than undeveloped land cover classes over the observation period. The results of this case study in our small watershed that has been historically impacted by upstream coal-mining activities are comparable to the results from similar studies in other geographic regions. However, we suggest other researchers conduct similar studies using robust data to determine the applicability of the USLE model and validate the results in developing measures to address soil loss issues.

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Hood, S. M., S. M. Zedaker,, W. M. Aust, and D. W. Smith. "Universal Soil Loss Equation (USLE)-Predicted Soil Loss for Harvesting Regimes in Appalachian Hardwoods." Northern Journal of Applied Forestry 19, no. 2 (June 1, 2002): 53–58. http://dx.doi.org/10.1093/njaf/19.2.53.

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Abstract Soil erosion from forest harvesting is a major environmental concern. While there has been research comparing soil erosion on clearcut regeneration harvests with that on uncut forests, there has been little focus on the differences among common silvicultural harvests. Forest certification standards that are currently being evaluated for adoption across the country often encourage uneven-aged systems over even-aged or two-aged systems. We estimated soil loss using the Universal Soil Loss Equation (USLE) for forest land on five harvested treatments in the southern Appalachians. Treatments included a clearcut, leave-tree harvest, shelterwood, group selection, and uncut control. Results predicted that the group selection would have approximately 10 tons/ac more soil loss over a 100 yr rotation than the other harvested treatments. The higher rate was primarily from skid trails when the treatment was reentered for harvesting. These results should be considered when weighing the benefits of uneven-aged silviculture over even-aged or two-aged silviculture.

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12

Benavidez, Rubianca, Bethanna Jackson, Deborah Maxwell, and Kevin Norton. "A review of the (Revised) Universal Soil Loss Equation ((R)USLE): with a view to increasing its global applicability and improving soil loss estimates." Hydrology and Earth System Sciences 22, no. 11 (November 27, 2018): 6059–86. http://dx.doi.org/10.5194/hess-22-6059-2018.

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Abstract. Soil erosion is a major problem around the world because of its effects on soil productivity, nutrient loss, siltation in water bodies, and degradation of water quality. By understanding the driving forces behind soil erosion, we can more easily identify erosion-prone areas within a landscape to address the problem strategically. Soil erosion models have been used to assist in this task. One of the most commonly used soil erosion models is the Universal Soil Loss Equation (USLE) and its family of models: the Revised Universal Soil Loss Equation (RUSLE), the Revised Universal Soil Loss Equation version 2 (RUSLE2), and the Modified Universal Soil Loss Equation (MUSLE). This paper reviews the different sub-factors of USLE and RUSLE, and analyses how different studies around the world have adapted the equations to local conditions. We compiled these studies and equations to serve as a reference for other researchers working with (R)USLE and related approaches. Within each sub-factor section, the strengths and limitations of the different equations are discussed, and guidance is given as to which equations may be most appropriate for particular climate types, spatial resolution, and temporal scale. We investigate some of the limitations of existing (R)USLE formulations, such as uncertainty issues given the simple empirical nature of the model and many of its sub-components; uncertainty issues around data availability; and its inability to account for soil loss from gully erosion, mass wasting events, or predicting potential sediment yields to streams. Recommendations on how to overcome some of the uncertainties associated with the model are given. Several key future directions to refine it are outlined: e.g. incorporating soil loss from other types of soil erosion, estimating soil loss at sub-annual temporal scales, and compiling consistent units for the future literature to reduce confusion and errors caused by mismatching units. The potential of combining (R)USLE with the Compound Topographic Index (CTI) and sediment delivery ratio (SDR) to account for gully erosion and sediment yield to streams respectively is discussed. Overall, the aim of this paper is to review the (R)USLE and its sub-factors, and to elucidate the caveats, limitations, and recommendations for future applications of these soil erosion models. We hope these recommendations will help researchers more robustly apply (R)USLE in a range of geoclimatic regions with varying data availability, and modelling different land cover scenarios at finer spatial and temporal scales (e.g. at the field scale with different cropping options).

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13

Erskine, Wayne D., A. Mahmoudzadeh, C. M. Browning, and C. Myers. "Sediment yields and soil loss rates from different land uses on Triassic shales in western Sydney, NSW." Soil Research 41, no. 1 (2003): 127. http://dx.doi.org/10.1071/sr01078.

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Sedimentation surveys of small dams demonstrate that land use is the dominant factor generating high sediment yields in the ungullied shale catchments of western Sydney where rainfall erosivity and soil erodibility are relatively constant. A single urban catchment produced 6.5 t/ha.year and cropped catchments an average of 6.7 ± 1.99 t/ha.year, whereas grazed woodland/forest and grazed pasture exported averages of only 2.5 ± 0.57 and 2.9 ± 1.02 t/ha.year, respectively. These yields are high by Australian standards and the farm dam sediments are enriched in both clay and phosphorus, in comparison to catchment topsoils. Empirical soil loss equations based on the Universal Soil Loss Equation (USLE) [Modified Universal Soil Loss Equation (MUSLE), Soiloss and Revised Universal Soil Loss Equation (RUSLE)] accurately predicted the measured sediment yields, with Soiloss being the most accurate. Although Soiloss is the only empirical equation to use Australian data, it is only marginally better than MUSLE, a simplified version of the USLE used for teaching. RUSLE predictions of soil loss rates were closely correlated with measured sediment yields but required inputs for poorly defined parameters. European land uses in the South Creek catchment, the largest shale catchment in western Sydney, have probably increased mean annual sediment yield by 4.4 times over that in 1788. Further increases are likely with increasing urbanisation.

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Wang, Guangxing, George Gertner, Xianzhong Liu, and Alan Anderson. "Uncertainty assessment of soil erodibility factor for revised universal soil loss equation." CATENA 46, no. 1 (November 2001): 1–14. http://dx.doi.org/10.1016/s0341-8162(01)00158-8.

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15

Kinnell, P. I. A. "Event soil loss, runoff and the Universal Soil Loss Equation family of models: A review." Journal of Hydrology 385, no. 1-4 (May 2010): 384–97. http://dx.doi.org/10.1016/j.jhydrol.2010.01.024.

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16

D. K. McCool, G. R. Foster, C. K. Mutchler, and L. D. Meyer. "Revised Slope Length Factor for the Universal Soil Loss Equation." Transactions of the ASAE 32, no. 5 (1989): 1571–76. http://dx.doi.org/10.13031/2013.31192.

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D. K. McCool, L. C. Brown, G. R. Foster, C. K. Mutchler, and L. D. Meyer. "Revised Slope Steepness Factor for the Universal Soil Loss Equation." Transactions of the ASAE 30, no. 5 (1987): 1387–96. http://dx.doi.org/10.13031/2013.30576.

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Sonneveld, B. G. J. S., and M. A. Nearing. "A nonparametric/parametric analysis of the Universal Soil Loss Equation." CATENA 52, no. 1 (May 2003): 9–21. http://dx.doi.org/10.1016/s0341-8162(02)00150-9.

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Loch, RJ, and CJ Rosewell. "Laboratory methods for measurement of soil erodibilities (K-factors) for the universal soil loss equation." Soil Research 30, no. 2 (1992): 233. http://dx.doi.org/10.1071/sr9920233.

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This paper reports a comparison of several methods for estimating the K (erodibility) factor for the Universal Soil Loss Equation (USLE) on the basis of laboratory measurements of soil properties. All methods used the nomograph of Wischmeier et al. (J. Soil Water Cons., 1971, 26, 189-93) to calculate K on the basis of laboratory data, but the data inputs ranged from: dispersed particle sizes as originally used in the nomograph; non-dispersed particle size, measured after shaking in water; and equivalent sand size distributions, based on settling velocity distributions of particles (aggregates and sand grains) at the soil surface under rain. A further method tested with the use of aggregated particle sizes resulting from rainfall wetting was a correction of the calculated K based on average density of wet sediment >0.100 mm diameter. Estimated K factors were compared with K factors derived from field measurements of soil loss for five soils. Use of dispersed particle sizes gave poor prediction of field K values for the three clay soils, and size distributions measured after rainfall wetting gave poor predictions of field K values for the four soils that had sediment of low density. Predictions of K from the use of non-dispersed particle sizes, and from the use of particle size at the soil surface under rain combined with a correction for sediment density were good for three soils, reasonable for one, and little different to that from the nomograph for the remaining soil. These latter two methods gave similar results, and were successful in predicting field values of K. As well, both methods appear to be sensitive to effects of cropping management on soil structure and erodibility. Either method could be used, depending on laboratory resources available and possible other uses of the data obtained. Simpler methods for measuring size distributions after rainfall wetting and for estimating sediment density are suggested.

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Tavares, André Silva, Velibor Spalevic, Junior Cesar Avanzi, Denismar Alves Nogueira, Marx Leandro Naves Silva, and Ronaldo Luiz Mincato. "Modeling of water erosion by the erosion potential method in a pilot subbasin in southern Minas Gerais." Semina: Ciências Agrárias 40, no. 2 (April 15, 2019): 555. http://dx.doi.org/10.5433/1679-0359.2019v40n2p555.

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Soil losses due to water erosion threaten the sustainability of agriculture and the food security of current and future generations. This study estimated potential soil losses and sediment production under different types of land uses in a subbasin in the Municipality of Alfenas, southern Minas Gerais, southeastern Brazil. The objective of this research was to evaluate the application of the Potential Erosion Method by the Intensity of Erosion and Drainage program and correlate the findings with the results obtained by the Revised Universal Soil Loss Equation as well as geoprocessing techniques and statistical analyses. In the Potential Erosion Method, the coefficient indicating the mean erosion intensity was 0.37, which corresponded to erosion category IV and indicated weak laminar erosion processes, and the total soil loss was 649.31 Mg year-1 and the mean was 1.46 Mg ha-1 year-1. These results were consistent in magnitude with those obtained in the Revised Universal Soil Loss Equation, which estimated a mean soil loss of 1.52 Mg ha-1 year-1 and a total soil loss of 668.26 Mg year-1. The Potential Erosion Method suggests that 1.5% of the area presents potential soil losses above the soil loss tolerance limit, which ranged from 5.19 to 5.90 Mg ha-1 year-1, while the Revised Universal Soil Loss Equation indicated that 7.3% of the area has potential soil losses above the limit. The maximum sediment discharge was 60 Mg year-1, meaning that 9.3% of the total soil loss reached the depositional areas of the river plains or watercourses. The Potential Erosion Method was efficient in the evaluation of water erosion in tropical soils, and the results were consistent with models widely employed in the estimation of soil losses. Thus, the model can support the evaluation of soil losses in Brazil and is a robust tool for evaluating the sustainability of agricultural activities.

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Fleming, Kim L., William L. Powers, Alice J. Jones, and Glenn A. Helmers. "Alternative production systems' effects on the K-factor of the Revised Universal Soil Loss Equation." American Journal of Alternative Agriculture 12, no. 2 (June 1997): 55–58. http://dx.doi.org/10.1017/s0889189300007244.

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AbstractThe soil erodibility factor (K) of the Revised Universal Soil Loss Equation is currently considered a constant for all soils in the same type, regardless of production practice. To examine the effect of alternative production systems on the K-factor we compared pairs of alternatively and conventionally farmed fields on a Judson silt loam (Fine-silty, mixed, mesic Cumulic Hapludolls), a Yutan silty clay loam (Fine-silty, mixed, mesic Mollic Hapludalf), and a Wann fine sandy loam (Coarse-loamy, mixed, mesic Fluvaquentic Haplustolls). Soil cores were taken from the surface 10 cm and analyzed for organic matter, permeability, structure, and texture. These data were used to estimate K-factors from a nomograph. All soils in the study had a fine granular structure. Organic matter content and permeability were significantly higher for the alternatively managed field at every location, except for no difference in permeability on the Judson soil. However, the K-factor was significantly lower for the alternative system on the Judson soil. Of all the parameters, texture has the greatest influence in determining K-factors within the nomograph, with soils higher in silt being more erodible than soils higher in sand or clay. Thus, the influences of alternative production systems affected the Judson soil to a greater degree than other textures because of its higher inherent susceptibility to erosion. This study shows that alternative production systems affect the K-factor of some soil types and can reduce soil erodibility, and therefore should be considered when developing conservation plans.

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Zonunsanga, R. "Estimation of Soil Loss in Teirei Watershed of Mizoram by using Universal Soil Loss Equation Model." Science & Technology Journal 4, no. 1 (January 1, 2016): 43–47. http://dx.doi.org/10.22232/stj.2016.04.01.06.

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Montgomery, J. A., A. J. Busacca, B. E. Frazier, and D. K. McCool. "Evaluating Soil Movement Using Cesium-137 and the Revised Universal Soil Loss Equation." Soil Science Society of America Journal 61, no. 2 (1997): 571. http://dx.doi.org/10.2136/sssaj1997.03615995006100020029x.

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Schnitzer, S., F. Seitz, A. Eicker, A. Güntner, M. Wattenbach, and A. Menzel. "Estimation of soil loss by water erosion in the Chinese Loess Plateau using Universal Soil Loss Equation and GRACE." Geophysical Journal International 193, no. 3 (March 9, 2013): 1283–90. http://dx.doi.org/10.1093/gji/ggt023.

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Goes, Jaime Carvalho, and Eder Silva de Oliveira. "Relationship between soil management and rainfall erosion: A case study in the Guajará-Mirim river watershed, Vigia - Pará." International Journal of Advanced Engineering Research and Science 9, no. 5 (2022): 174–83. http://dx.doi.org/10.22161/ijaers.95.16.

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This study aims to evaluate the relationship between soil management and its loss by rainfall erosion in the watershed of the Guajará-Mirim river. The methodology used was essentially carried out by remote sensing, literature review and the application of the Universal Soil Loss Equation (USLE), with the geoprocessing of matrix and vector files in the QGIS software. The results show that, in relation to soil management in the study region, considering that it is a plain and that there were no measures to control soil erosion, its loss by rainwater transport is aggravated the greater the exposure of the soil, that is, the removal of vegetation and the revolving soil horizons. This assertion is evidenced by the greater soil loss in the areas mapped for sand extraction and deforestation by calculating the Universal Soil Loss Equation.

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Kadam, Ajaykumar, B. N. Umrikar, and R. N. Sankhua. "Assessment of Soil Loss using Revised Universal Soil Loss Equation (RUSLE): A Remote Sensing and GIS Approach." Remote Sensing of Land 2, no. 1 (December 31, 2018): 65–75. http://dx.doi.org/10.21523/gcj1.18020105.

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A comprehensive methodology that combines Revised Universal Soil Loss Equation (RUSLE), Remote Sensing data and Geographic Information System (GIS) techniques was used to determine the soil loss vulnerability of an agriculture mountainous watershed in Maharashtra, India. The spatial variation in rate of annual soil loss was obtained by integrating raster derived parameter in GIS environment. The thematic layers such as TRMM [Tropical Rainfall Measuring Mission] derived rainfall erosivity (R), soil erodibility (K), GDEM based slope length and steepness (LS), land cover management (C) and factors of conservation practices (P) were calculated to identify their effects on average annual soil loss. The highest potential of estimated soil loss was 688.397 t/ha/yr. The mean annual soil loss is 1.26 t/ha/yr and highest soil loss occurs on the main watercourse, since high slope length and steepness. The spatial soil loss maps prepared with RUSLE method using remote sensing and GIS can be helpful as a lead idea in arising plans for land use development and administration in the ecologically sensitive hilly areas.

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Lu and Chiang. "Assessment of Sediment Transport Functions with the Modified SWAT-Twn Model for a Taiwanese Small Mountainous Watershed." Water 11, no. 9 (August 22, 2019): 1749. http://dx.doi.org/10.3390/w11091749.

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In Taiwan, the steep landscape and highly vulnerable geology make it difficult to predict soil erosion and sediment transportation via variable transport conditions. In this study, we integrated the Taiwan universal soil loss equation (TUSLE) and slope stability conditions in the soil and water assessment tool (SWAT) as the SWAT-Twn model to improve sediment simulation and assess the sediment transport functions in the Chenyulan watershed, a small mountainous catchment. The results showed that the simulation of streamflow was satisfactory for calibration and validation. Before model calibration and validation for sediment, SWAT-Twn with default sediment transport method performed better in sediment simulation than the official SWAT model (version 664). The SWAT-Twn model coupled with the simplified Bagnold equation could estimate sediment export more accurately and significantly reduce the overestimated sediment yield by 65.7%, especially in highly steep areas. Furthermore, five different sediment transport methods (simplified Bagnold equation with/without routing by particle size, Kodoatie equation, Molinas and Wu equation, and Yang sand and gravel equation) were evaluated. It is suggested that modelers who conduct sediment studies in the mountainous watersheds with extreme rainfall conditions should adjust the modified universal soil loss equation (MUSLE) factors and carefully evaluate the sediment transportation equations in SWAT.

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A.H, Johari, Law P.L., Taib S.N.L., and Yong L.K. "ASSESSMENT OF SOIL EROSION BY SIMULATING RAINFALL ON AN EQUATORIAL ORGANIC SOIL." Journal of Civil Engineering, Science and Technology 8, no. 2 (October 5, 2017): 72–81. http://dx.doi.org/10.33736/jcest.440.2017.

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Soil erosion occurs on construction sites partly due to site clearing that exposes the land to the erosive power of rainfall. A proposed construction project requires the submission of an Environmental Impact Assessment EIA) to assess the impact of the project on the environment. Assessment of soil erosion is included in the EIA, but the equation to estimate soil erosion known as the Universal Soil Loss Equation (USLE) is only applicable to a soil containing up to four percent organic matter. This limitation of USLE requires an alternative that can predict soil erosion on an organic soil. This study attempts to assess erosion that occurs on an organic soil by simulated rainfall. Field soil samples were reconstructed into three shapes and exposed to simulated rainfall. Results indicate that the amount of organic soil loss decreases with increasing duration of rainfall. Particle size distribution shows that particles with sizes finer than coarse sand (1.7 mm) remained on the slopes. Equations were developed from the graphs of soil loss versus duration of simulated rainfall to estimate soil loss occurring on slopes covered by an organic soil. The outcome of this study can be a precursor to developing an equation to estimate soil erodibility of a slope overlain by an organic soil.

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Keller, Boglárka, Csaba Centeri, Judit Alexandra Szabó, Zoltán Szalai, and Gergely Jakab. "Comparison of the Applicability of Different Soil Erosion Models to Predict Soil Erodibility Factor and Event Soil Losses on Loess Slopes in Hungary." Water 13, no. 24 (December 9, 2021): 3517. http://dx.doi.org/10.3390/w13243517.

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Climate change induces more extreme precipitation events, which increase the amount of soil loss. There are continuous requests from the decision-makers in the European Union to provide data on soil loss; the question is, which ones should we use? The paper presents the results of USLE (Universal Soil Loss Equation), RUSLE (Revised USLE), USLE-M (USLE-Modified) and EPIC (Erosion-Productivity Impact Calculator) modelling, based on rainfall simulations performed in the Koppány Valley, Hungary. Soil losses were measured during low-, moderate- and high-intensity rainfalls on cultivated soils formed on loess. The soil erodibility values were calculated by the equations of the applied soil erosion models and ranged from 0.0028 to 0.0087 t ha h ha−1 MJ−1 mm−1 for the USLE-related models. EPIC produced larger values. The coefficient of determination resulted in an acceptable correlation between the measured and calculated values only in the case of USLE-M. Based on other statistical indicators (e.g., NSEI, RMSE, PBIAS and relative error), RUSLE, USLE and USLE-M resulted in the best performance. Overall, regardless of being non-physically based models, USLE-type models seem to produce accurate soil erodibility values, thus modelling outputs.

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Park, Soyoung, Cheyoung Oh, Seongwoo Jeon, Huicheul Jung, and Chuluong Choi. "Soil erosion risk in Korean watersheds, assessed using the revised universal soil loss equation." Journal of Hydrology 399, no. 3-4 (March 2011): 263–73. http://dx.doi.org/10.1016/j.jhydrol.2011.01.004.

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CHOW, T. L., H. CORMIER, J. L. DAIGLE, and I. GHANEM. "EFFECTS OF POTATO CROPPING PRACTICES ON WATER RUNOFF AND SOIL EROSION." Canadian Journal of Soil Science 70, no. 2 (May 1, 1990): 137–48. http://dx.doi.org/10.4141/cjss90-016.

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Using runoff-erosion plots (10 m wide × 30 m long), the effects of cropping practices on surface runoff and soil loss were examined on a Hommesville gravelly loam soil to evaluate the applicability of the Universal Soil Loss Equation in New Brunswick. The amount of water runoff and soil loss from continuous fallow, up-and-down slope planting of potatoes (Solanum tuberosum), and clover (Trifolium pratense) on 8 and 11% slopes were measured from 1983 to 1985. In addition, runoff and soil loss from contour planting of potatoes were measured on the 11% slope. Slope planting of potatoes resulted in higher runoff and soil loss than on fallow plots. There was considerable reduction in runoff and soil loss when potatoes were planted along the contour. Runoff and soil loss under clover were negligible. Rainfall erosion index (R) and slope length and steepness (LS) correlated well with the measured soil losses. However, both the measured soil credibility factor (K) and the cover and management factor (C) deviated markedly from the current values used for conservation planning. Key words: Universal Soil Loss Equation, rainfall erosion index, topographic factor, soil erodibility factor, cover and management factor, support practice factor

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Y.W., Oon, Chin N.J., and Law P.L. "Soil Erosian and Sediment Yield of a Sanitary Landfill Site - A Case Study." Journal of Civil Engineering, Science and Technology 2, no. 2 (December 1, 2011): 23–34. http://dx.doi.org/10.33736/jcest.91.2011.

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This research presents the results of a study on soil erosion rates and sediment yields of a proposed Level 4 Sanitary Landfill construction site located in Sibu, Sarawak. Assessments on potential soil erosion rates and sediment yields during pre-construction, construction and operation stages were carried out using the Revised Universal Soil Loss Equation (RUSLE) and Modified Universal Soil Loss Equation (MUSLE), respectively. It was found that soil erosion rates during construction and operation stages fell under "Moderately High" category, whereby highest sediment yield occurred during construction and operation stages. Comparative analysis on with and without Best Management Practices (BMPs) during construction stage demonstrated that BMPs could significantly reduce the rate of soil erosion, and thus sediment yields.

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Freebairn, DM, DM Silburn, and RJ Loch. "Evaluation of three soil erosion models for clay soils." Soil Research 27, no. 1 (1989): 199. http://dx.doi.org/10.1071/sr9890199.

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The Universal Soil Loss Equation (USLE) and two modified USLE models were assessed for their ability to predict soil erosion on contour bay catchments on the Darling Downs, Queensland. The models were applied using USLE handbook values as well as optimized values determined by fitting the models to the experimental data. All three models explained greater than 80% of the variance in measured soil loss with no single model being consistently superior to the others. Cover reduced erosion more than that predicted by the USLE.

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Di Stefano, Costanza, Vincenzo Pampalone, Francesca Todisco, Lorenzo Vergni, and Vito Ferro. "Testing the Universal Soil Loss Equation‐MB equation in plots in Central and South Italy." Hydrological Processes 33, no. 18 (June 13, 2019): 2422–33. http://dx.doi.org/10.1002/hyp.13478.

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W. C. Hession, D. E. Storm, and C. T. Haan. "Two-phase Uncertainty Analysis: An Example Using the Universal Soil Loss Equation." Transactions of the ASAE 39, no. 4 (1996): 1309–19. http://dx.doi.org/10.13031/2013.27622.

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Falk, M. G., R. J. Denham, and K. L. Mengersen. "Estimating Uncertainty in the Revised Universal Soil Loss Equation via Bayesian Melding." Journal of Agricultural, Biological, and Environmental Statistics 15, no. 1 (January 28, 2010): 20–37. http://dx.doi.org/10.1007/s13253-009-0005-y.

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Kitahara, Hikaru, Yoichi Okura, Toshiaki Sammori, and Akiko Kawanami. "Application of Universal Soil Loss Equation (USLE) to Mountainous Forests in Japan." Journal of Forest Research 5, no. 4 (November 2000): 231–36. http://dx.doi.org/10.1007/bf02767115.

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Auliyani, Diah, and Wahyu Wisnu Wijaya. "PERBANDINGAN PREDIKSI HASIL SEDIMEN MENGGUNAKAN PENDEKATAN MODEL UNIVERSAL SOIL LOSS EQUATION DENGAN PENGUKURAN LANGSUNG (Comparison of sediment yield from prediction using Universal Soil Loss Equation with direct measurement)." Jurnal Penelitian Pengelolaan Daerah Aliran Sungai 1, no. 1 (April 2017): 61–71. http://dx.doi.org/10.20886/jppdas.2017.1.1.61-71.

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Auliyani, Diah, and Wahyu Wisnu Wijaya. "PERBANDINGAN PREDIKSI HASIL SEDIMEN MENGGUNAKAN PENDEKATAN MODEL UNIVERSAL SOIL LOSS EQUATION DENGAN PENGUKURAN LANGSUNG (Comparison of sediment yield from prediction using Universal Soil Loss Equation with direct measurement)." Jurnal Penelitian Pengelolaan Daerah Aliran Sungai 1, no. 1 (April 28, 2017): 61–71. http://dx.doi.org/10.20886/jppdas.v1i1.2570.g2078.

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BARBOSA, A. F., E. F. OLIVEIRA, C. L. MIOTO, and A. C. PARANHOS FILHO. "The Application of the Universal Soil Loss Equation by Using Free and Available Softwares." Anuário do Instituto de Geociências - UFRJ 38, no. 1 (August 24, 2015): 170. http://dx.doi.org/10.11137/2015_1_170_179.

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Mulya, S. A., and N. Khotimah. "Assessment of Soil Erosion Hazard in Prambanan District Using RUSLE (Revised Universal Soil Loss Equation)." IOP Conference Series: Earth and Environmental Science 884, no. 1 (November 1, 2021): 012010. http://dx.doi.org/10.1088/1755-1315/884/1/012010.

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Abstract Prambanan District which located in Daerah Istimewa Yogyakarta Province has the potential for land degradation due to erosion processes. With the characteristics of annual rainfall more than 2000 mm / year, topography with a slope of more than 20% in upland areas, as well as the conversion of upland to dryland agriculture are factors that can trigger the erosion process more quickly. If the rate of erosion speed exceeds the ability of the soil to regenerate the soil body, its productivity will be disrupted and accelerate the formation of critical soil. Therefore, it is necessary to know the estimated rate of erosion, tolerable distribution of erosion, and the potential danger of erosion that occurs. The purpose of this study was to (1) predict the rate of erosion, (2) calculate the permissible erosion value, (3) identify the rate & index of erosion hazard. Data were collected using field surveys and soil sampling using stratified random sampling techniques with land units as the unit of analysis. The value of erosion was predicted using the Revised Universal Soil Loss Equation (RUSLE) method. The RUSLE method is described by the following equation, A=R*K*L*S*C*P, where; A as estimated averages annual loss of soil, R is the rainfall erosivity factor, K is the soil erodibility factor, LS is the slope length factor, C is the cover management factor, & P is the conservation practice factor. The results showed that the erosion value ranged from 0.39 - 268.55 tons/ha/year. Permissible erosion ranges from 8.4 – 15 tons/ha/year for Latosol and 27.4 ton/ha/year for Regosol. The Rate of Erosion Hazard is dominated by moderate erosion, covering an area of 1330.7 ha or 31.8% of the total area. The Erosion Hazard Index is dominated by the low class (<1.0) which is covered over 2703.1 ha or 64.61% of the total area.

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Nur, Ruslinda, Krisdayanti a, and Rusnianti Nur. "RATE ANALYSIS OF SOIL EROSION USING UNIVERSAL SOIL LOSS EQUATION (USLE) METHOD IN JENEBERANG WATERSHED." International Journal of Advanced Research 10, no. 02 (February 28, 2022): 529–37. http://dx.doi.org/10.21474/ijar01/14235.

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Erosion in a watershed is a complex phenomenon that affects the quality of land resources due to either natural or human influence. The purpose of this study to determine the rate of erosion in the Jeneberang watershed and produce a recommendation of soil conservation to reduce the rate of erosion. This study uses some parameter maps, such as Rain Erosivitas Index (R) map, Land Erodibility Index (K) map, Length and Slopes Declivity Factor (LS) map, and Plants Management Factor and Soil Conservation (CP). All parameters were analysed using USLE map to determine the rate of erosion. The analysis results the rate of erosion by USLE method indicates the hazard level of the erosion in the Jeneberang watershed are dominantly at the very low levels with an area 55.068,39 ha or 71.6% of the research area. However, the erosion in the study area can not be ignored because there is a very high level of danger to the extensive erosion 11.681,55 or 15% of the research area. Therefore, generated a recommendation of soil conservation by soil cover planting and terrace construction repairs. By this conservation recommendation, the area with a high level of the rate of erosion reduce in the amount of 10,6%.

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Gogoi, Bharati. "Estimation of Potential Soil Erosion in Majuli Island using Revised Universal Soil Loss Equation Model." Annals of the National Association of Geographers India 42, no. 2 (December 6, 2022): 338–54. http://dx.doi.org/10.32381/atnagi.2022.42.02.7.

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Ajibade, Fidelis Odedishemi, Nathaniel Azubuike Nwogwu, Bashir Adelodun, Taofeeq Sholagberu Abdulkadir, Temitope Fausat Ajibade, Kayode Hassan Lasisi, Olaolu George Fadugba, Titilayo Abimbola Owolabi, and Olabanji Olatona Olajire. "Application of RUSLE integrated with GIS and remote sensing techniques to assess soil erosion in Anambra State, South-Eastern Nigeria." Journal of Water and Climate Change 11, S1 (July 3, 2020): 407–22. http://dx.doi.org/10.2166/wcc.2020.222.

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Abstract Soil erosion and mass movement processes spread across Anambra State in Nigeria, therefore making management and conservation techniques expensive and difficult in execution across the entire state. This study employed the Revised Universal Soil Loss Equation (RUSLE) model with the integration of geographic information system (GIS) and remote sensing techniques to assess the risk of soil erosion and hotspots in the area. Remotely sensed data such as Landsat 8 imagery, Shuttle Radar Topography Mission (SRTM) imagery, Era-Interim coupled with world soil database were used as digital data sources for land use map, digital elevation model, rainfall and soil data, respectively, to generate the Universal Soil Loss Equation (USLE) parameters. The results indicated vulnerability levels in low, medium and high cover areas of 4,143.62 (91%), 332.29 (7%) and 84.06 (2%) km2, respectively, with a total soil loss between 0 and 181.237 ton/ha/yr (metric ton per hectare per year). This study revealed that high rainfall erosivity, steep and long slopes, and low vegetation cover were the main factors promoting soil loss in the area. Thus, the amount of soil loss in Anambra State is expected to increase with climate change and anthropogenic activities.

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Siddiqui, Saima, Mirza Wajid Ali Safi, Aqil Tariq, Naveed Ur Rehman, and Syed Waseem Haider. "GIS Based Universal Soil Erosion Estimation in District Chakwal Punjab, Pakistan." International Journal of Economic and Environmental Geology 11, no. 2 (September 25, 2020): 30–36. http://dx.doi.org/10.46660/ijeeg.vol11.iss2.2020.443.

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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.

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Maqsoom, Ahsen, Bilal Aslam, Usman Hassan, Zaheer Abbas Kazmi, Mahmoud Sodangi, Rana Faisal Tufail, and Danish Farooq. "Geospatial Assessment of Soil Erosion Intensity and Sediment Yield Using the Revised Universal Soil Loss Equation (RUSLE) Model." ISPRS International Journal of Geo-Information 9, no. 6 (May 27, 2020): 356. http://dx.doi.org/10.3390/ijgi9060356.

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Land degradation caused by soil erosion is considered among the most severe problems of the 21stcentury. It poses serious threats to soil fertility, food availability, human health, and the world ecosystem. The purpose of the study is to make a quantitative mapping of soil loss in the Chitral district, Pakistan. For the estimation of soil loss in the study area, the Revised Universal Soil Loss Equation (RUSLE) model was used in combination with Remote Sensing (RS) and Geographic Information System (GIS). Topographical features of the study area show that the area is more vulnerable to soil loss, having the highest average annual soil loss of 78 ton/ha/year. Maps generated in the study show that the area has the highest sediment yield of 258 tons/ha/year and higher average annual soil loss of 450 tons/ha/year. The very high severity class represents 8%, 16% under high, 21% under moderate, 12% under low, and 13% under very low soil loss in the Chitral district. The above study is helpful to researchers and planners for better planning to control the loss of soil in the high severity zones. Plantation of trees and structures should be built like check dams, which effectively control the soil erosion process.

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Lense, Guilherme Henrique Expedito, Rodrigo Santos Moreira, Taya Cristo Parreiras, Derielsen Brandão Santana, Talyson De Melo Bolelli, and Ronaldo Luiz Mincato. "Water erosion modeling by the Erosion Potential Method and the Revised Universal Soil Loss Equation: a comparative analysis." Ambiente e Agua - An Interdisciplinary Journal of Applied Science 15, no. 4 (July 8, 2020): 1. http://dx.doi.org/10.4136/ambi-agua.2501.

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Water erosion is the principal degradation process of tropical soils, and its effects can be measured by modeling techniques. Erosion models provide a diagnosis of the soil loss intensity and can support the planning of soil conservation practices. Models with low data requirements, such as the Revised Universal Soil Loss Equation (RUSLE) and, more recently, the Erosion Potential Method (EPM), are mainly applied in Brazil. Thus, the objective of this work was to estimate water erosion soil-loss rates using the EPM and RUSLE models on a tropical subbasin, followed by a comparison of their outcomes. The models’ application considered soil physical parameters, edaphoclimatic conditions of the area, land use, and subbasin management practices. The accuracy of the methods was verified using total transported sediment and water discharge data. We compared the models using Pearson's correlation analyses, considering a 5% of significance. We found a predominance of moderate-intensity erosion with average soil loss of 1.17 and 1.46 Mg ha-1 year-1, measured by EPM and RUSLE, respectively. The EPM model underestimated soil losses by 15.27%, and RUSLE overestimated by 19.08%, indicating a higher percentage of areas with high erosion rates (4.60%). The models presented results with a different order of magnitude, but with significant correlations, indicating that both methods pointed out similar zones of intense and light-erosion rates.

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Mohammed, Issamaldin, Hatim Nuh, and Ahmed Abdalla. "Coupling Universal Soil Loss Equation and GIS Techniques for Estimation of Soil Loss and Sediment Yield in Algash Basin." International Journal of Advanced Remote Sensing and GIS 6, no. 1 (March 21, 2017): 2050–67. http://dx.doi.org/10.23953/cloud.ijarsg.36.

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Moore, Ian D., and Gordon J. Burch. "Physical Basis of the Length-slope Factor in the Universal Soil Loss Equation." Soil Science Society of America Journal 50, no. 5 (September 1986): 1294–98. http://dx.doi.org/10.2136/sssaj1986.03615995005000050042x.

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Diodato, N. "Estimating RUSLE’s rainfall factor in the part of Italy with a Mediterranean rainfall regime." Hydrology and Earth System Sciences 8, no. 1 (February 29, 2004): 103–7. http://dx.doi.org/10.5194/hess-8-103-2004.

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Abstract. The computation of the erosion index (EI), which is basic to the determination of the rainfall-runoff erosivity factor R of the Revised Universal Soil Loss Equation (RUSLE), is tedious and time-consuming and requires a continuous record of rainfall intensity. In this study, a power equation(r2 = 0.867) involving annual erosion index (EI30-annual) in the Mediterranean part of Italy is obtained. Data from 12 raingauge stations are used to derive and then test a regional relationship for estimating the erosion index from only three rainfall parameters. Erosivity rainfall data derived from 5 additional stations are used for validation and critical examination. The empirical procedures give results which compare satisfactorily with relationships calibrated elsewhere. Keywords: erosion index, rainfall, erosivity, Revised Universal Soil Loss Equation

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Journal articles on the topic 'Universal Soil Loss Equation'

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