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1
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.
Abstract:
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.
2
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.
Abstract:
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.
3
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.
Abstract:
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).
4
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.
Abstract:
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.
5
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.
Abstract:
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.
6
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.
Abstract:
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.
7
Oshunsanya, Suarau Odutola, and Nkem Joseph Nwosu. "Suitability of Universal Soil Loss Erodibility, Inter-rill and Rill Erodibility Models for Selected Tropical Soils." Agricultura Tropica et Subtropica 50, no. 4 (December 1, 2017): 191–98. http://dx.doi.org/10.1515/ats-2017-0020.
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AbstractThe universal soil loss equation (USLE) and water erosion prediction project (WEPP) (inter-rill and rill) erodibility factors are important indicators for land degradation assessment all over the world, which were primarily developed for the United States of America (USA). However, information on suitability of USLE and WEPP for tropical environment is scarce. Therefore, studies were carried out to investigate the suitability of USLE and WEPP for selected tropical soils of Southwestern Nigeria. Four pedons classified based on USDA soil taxonomy as Plinthic Petraquept (Adio series), Kanhaplic Haplaustalf (Oyo series), Typic Plinthustalf (Temidire series) and Typic Haplaustalf (Owutu series) were used for the study. Soil erodibility factor was determined using USLE and WEPP models. Origin-Pro. 8.1 software was employed to compare USLE and WEPP models for conformity and suitability. The results showed perfect agreement (R2= 1.0;P< 0.001) between the two WEPP (inter-rill and rill) erodibility models in all the four soil types investigated. In addition, WEPP models (inter-rill and rill erodibility) significantly (R2= 0.82;P< 0.001) related to USLE (El-Swaify and Dangler, 1977) erodibility model. There was a poor relationship (R2= 0.46;P< 0.06) between USLE (Wischmeier and Mannering, 1968) and the WEPP erodibility factors. The WEPP erodibility models with a perfect relationship with soil properties of the four soil types are more suitable than USLE models for predicting soil erodibility of the identified soil types in tropical environments.
8
Lane, LJ, KG Renard, GR Foster, and JM Laflen. "Development and application of modern soil erosion prediction technology - The USDA experience." Soil Research 30, no. 6 (1992): 893. http://dx.doi.org/10.1071/sr9920893.
Abstract:
Erosion prediction efforts are described to provide a synopsis of the USDA's experience in developing and applying soil erosion prediction technology in its research and development activities and its soil conservation programs. For almost five decades, equations to predict soil erosion by water have been useful m developing plans for controlling soil erosion and sedimentation. The Universal Soil Low Equation (USLE) is the most widely known and used of the erosion prediction equations. The USLE presents a simply understood and easily applied technology which has been of incalculable benefit to soil conservation and land management. The Chemicals, Runoff, and Erosion from Agricultural Management Systems Model (CREAMS) contains a sophisticated erosion component based, in part, on the USLE and on flow hydraulics and the processes of sediment detachment, transport, and deposition. In 1985, the USDA in cooperation with BLM and several universities initiated a national project called the Water Erosion Prediction Project (WEPP) to develop a next generation water erosion prediction technology. The Revised Universal Soil Loss Equation (RUSLE) is an update of the USLE to improve erosion prediction in the interim before WEPP is adopted and to provide and adjunct technology thereafter.
9
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.
Abstract:
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.
10
Karyadinata Putra, Abdurrohim. "PEMETAAN KAWASAN RAWAN EROSI MENGGUNAKAN METODE USLE (UNIVERSAL SOIL LOSS EQUATION)." Jurnal ARTESIS 1, no. 1 (May 25, 2021): 88–95. http://dx.doi.org/10.35814/artesis.v1i1.2871.
Abstract:
DAS Ciliwung Tengah terletak di Kabupaten Bogor Jawa Barat dan Administratif Depok, yang memiliki topografi datar hingga bergunung dengan kemiringan lereng yang bervariasi dan curah hujan tahunan hingga 4839.67 mm/tahun yang memungkinkan terjadinya bahaya erosi. Penelitian ini bertujuan untuk mengidentifikasi luas daerah yang memiliki tingkat bahaya dan menghitung nilai erosi (Ton/Ha/Tahun) pada Kawasan Ciliwung Tengah berdasarkan USLE menggunakan data penginderaan jauh berupa citra Landsat 8 OLI dan analisis SIG, serta menganalisis faktor yang dominan terhadap bahaya erosi tanah di Sub Ciliwung Tengah menggunakan analisis statistik. Metode USLE menggunakan lima parameter, yaitu indeks panjang dan kemiringan lereng (LS) diperoleh dari peta kemiringan lereng, indeks erosivitas hujan (R) diperoleh dari perhitungan erosivitas hujan, pengelolaan tanaman (C) dan indeks konservasi lahan (P) yang diperoleh dari interpretasi citra dan survei lapangan, serta indeks erodibilitas tanah (K) yang diperoleh dari peta jenis tanah. Pengolahan data dan analisis overlay parameter erosi dan perhitungan erosi menggunakan metode USLE. Hasil penelitian menunjukkan bahwa bahaya erosi di Sub DAS Ciliwung Tengah terdiri dari tiga kelas, yaitu rendah sebesar 18.29 – 773 Ton/Ha/Tahun seluas 14,476.22 Ha dengan persentase 92.17%, sedang sebesar 774 – 1,548 Ton/Ha/Tahun seluas 0.28% dengan luas 43.95 Ha, dan berat sebesar 1,549 – 2,340 Ton/Ha/Tahun seluas 1,185.57 Ha dengan persentase 7.55%. Pemetaan tingkat bahaya erosi ini akan sangat membantu dalam menentukan tindakan pengelolaan dan konservasi lahan yang baik dan sesuai di daerah penelitian
11
Manaouch, Mohamed, Anis Zouagui, and Imad Fenjiro. "A review of soil erosion modeling by R/USLE in Morocco: Achievements and limits." E3S Web of Conferences 234 (2021): 00067. http://dx.doi.org/10.1051/e3sconf/202123400067.
Abstract:
Soil erosion is a major cause of land degradation. It can be estimated with several models, such as empirical, conceptual and physical based. One of the empirical models used worldwide nowadays for soil erosion assessment is the Universal Soil Loss Equation (USLE) and its updated form, the Revised Universal Soil Loss Equation (RUSLE). In Morocco, this model is being used to assess and quantify soil loss by water erosion. In spite of this, it was noted that limited studies employed correctly this important tool. The goal of this review paper was to identify potential usage of R/USLE models in Morocco. This was done by evaluating the conducted studies concerning these models and main gaps and challenges were determined accordingly. Improvement options and future requirements for using R/USLE models were recommended. In order to assess the statues of the R/USLE models applications, the 56 published documents related to R/USLE models conducted in Morocco during the first use till 2020 were collected and reviewed. These publications covered five main areas. The main benefits as well as gaps of the conducted studies were discussed for each area. Current concerns, need of future studies as well as related recommendations and suggestions were also presented.
12
Suryawanshi, Ashwini, Anupam Kumar Nema, Rahul Kumar Jaiswal, Sukant Jain, and Saswat Kumar Kar. "Identification of Soil Erosion Prone Areas of Madhya Pradesh using USLE/ RUSLE." Journal of Agricultural Engineering 58, no. 02 (June 30, 2021): 177–91. http://dx.doi.org/10.52151/jae2021581.1744.
Abstract:
Soil erosion is caused due to the dynamic action of erosive agents, mainly water, and is a major threat to the environment. Primary aim of the present study was to study the soil loss dynamics, and identify the environmental hotspots in Madhya Pradesh to aid decision-makers to plan and prioritize appropriate conservation measures. Universal Soil Loss Equation (USLE) and Revised Universal Soil Loss Equation (RUSLE) models were applied for erosion rate estimation by generating thematic maps of R (Rainfall erosivity factor), K (Soil erodibility factor), LS (Topographic factor), C (Cover and management factor), and P (Support practice factor) factors by using several input parameters in QGIS software. Subsequently, the different classes of soil erosion and percentage area under these classes were identified. The average annual soil erosion for the entire state as obtained from the USLE and RUSLE model were 5.80 t.ha-1.yr-1 and 6.64 t.ha-1.yr-1, respectively. The areas under severe risk were 1.09 % and 1.80 %, and very severe risk areas were 1.57 % and 1.83 % as estimated by USLE and RUSLE model, respectively. As compared to RUSLE model, USLE model underestimated rate of soil erosion for most river basins of the state as well as for the entire state
13
Arévalo, Diana, Ramón Bienes, and Marta Ruiz-Colmenero. "Application of three erosion prediction models in the center of the Iberian Peninsula: incorporation and evaluation of new parameters with influence on soil losses / Aplicación de tres modelos de predicción de la erosión en el centro de la Península Ibérica: incorporación y evaluación de nuevos parámetros con influencia en las pérdidas de suelo." Brazilian Journal of Animal and Environmental Research 5, no. 2 (April 26, 2022): 1829–43. http://dx.doi.org/10.34188/bjaerv5n2-030.
Abstract:
Erosion prediction models are useful tools for assessing the impact of land use practices on soil and water conservation. These models are often used by environmental protection authorities for the establishment of guidelines. This study examines the application of three erosion models of different complexity to predict soil loss in a small basin located in the center of the Iberian Peninsula, under a semi-arid Mediterranean climate. The models applied are: the Universal Soil Loss Equation (USLE), the Revised Universal Soil Loss Equation (RUSLE) and an adaptation of the USLE model, which we have called AUSLE, to include soil characteristics not included in the original model. The average value of erosion at the watershed level obtained by the AUSLE model does not show significant differences with respect to the value obtained with RUSLE 1.06c, while the USLE shows an overestimation with respect to the other two models. The AUSLE model allows obtaining values similar to the more complex model (RUSLE) in a simple way, which makes it interesting for its application in conservation plans.
14
Kinnell, P. I. A. "Event erosivity factor and errors in erosion predictions by some empirical models." Soil Research 41, no. 5 (2003): 991. http://dx.doi.org/10.1071/sr02123.
Abstract:
Analyses undertaken in this paper show that the Universal Soil Loss Equation (USLE) tends to overestimate low values of soil loss when the soil surface has a high capacity to infiltrate rainfall, but the degree of overestimation falls as the capacity of the soil to produce runoff increases. The USLE-M, a version of the USLE that uses the product of the runoff ratio and the EI30 as the event erosivity index, is more efficient in estimating soil loss because runoff is considered explicitly in the event erosivity index, whereas it is not in the USLE. The results show clearly that the problem of the USLE and the RUSLE overpredicting observed erosion losses, when erosion losses are low, is related to a large degree to model formula. In addition, the removal of restrictions to what constitutes a valid EI30 value increases the capacity of the RUSLE to overpredict low soil losses. As the USLE is an empirical model, values of USLE K, C, and P can only be used when the event erosivity parameter is EI30. Models like EPIC ignore this fact.
15
Le, Trung Van, Hoang Thi Kim Nguyen, and Anh Thi Ngoc Nguyen. "GIS and Remote Sensing solution for Dalat city’s soil erosion mapping." Science and Technology Development Journal 19, no. 2 (June 30, 2016): 46–54. http://dx.doi.org/10.32508/stdj.v19i2.700.
Abstract:
This paper introduces the solution for Dalat city’s soil erosion mapping using the integration of GIS, Remote Sensing and the Universal Soil Loss Equation (USLE). Each of the USLE factors with associated attribute data are dicussed and the soil erosion parameters were selected and encoded in a GIS database to produce thematic layers. The result demonstrates the soil erosion map that indicates the potential annual soil loss located in each area of land. This map is used to confirm the severe level of soil erosion risk need immediate attention from soil conservation point of view.
16
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.
Abstract:
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.
17
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.
Abstract:
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.
18
Kathwas, Amar Kumar, and Nilanchal Patel. "Geomorphic Control on Soil Erosion – a Case Study in the Subarnarekha Basin, India." Polish Journal of Soil Science 54, no. 1 (June 29, 2021): 1. http://dx.doi.org/10.17951/pjss.2021.54.1.1-24.
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<p>Geomorphology depicts the qualitative and quantitative characteristics of both terrain and landscape features combined with the processes responsible for its evolution. Soil erosion by water involves processes, which removes soil particles and organic matter from the upper sheet of the soil surface, and then transports the eroded material to distant location under the action of water. Very few studies have been conducted on the nature and dynamics of soil erosion in the different geomorphologic features. In the present investigation, an attempt has been made to assess the control of geomorphologic features on the soil loss. Universal Soil Loss Equation (USLE) was used to determine soil loss from the various geomorphological landforms. Principal component analysis (PCA) was implemented on the USLE parameters to determine the degree of association between the individual principal components and the USLE-derived soil loss. Results obtained from the investigation signify the influence of the various landforms on soil erosion. PC5 is found to be significantly correlated with the USLE-derived soil loss. The results ascertained significant association between the soil loss and geomorphological landforms, and therefore, suitable strategies can be implemented to alleviate soil loss in the individual landforms.</p>
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Pampalone, Vincenzo, Alessio Nicosia, Vincenzo Palmeri, Maria Angela Serio, and Vito Ferro. "Rill and Interrill Soil Loss Estimations Using the USLE-MB Equation at the Sparacia Experimental Site (South Italy)." Water 15, no. 13 (June 28, 2023): 2396. http://dx.doi.org/10.3390/w15132396.
Abstract:
A reliable prediction of event soil loss at the plot scale can be obtained by Universal Soil Loss Equation (USLE)-type models. For the Sparacia site (South Italy), the USLE-MB model was recently developed, in which the effect of the erosive agent is modeled using the rainfall erosivity index of the USLE by a power b1 > 1 of the runoff coefficient QR. In this investigation, the model is parameterized separately using plot data collected for rill and interrill events that occurred in the Sparacia experimental area. The values b1 = 1.406 and b1 = 1.012 were obtained for the interrill and rill databases, respectively, which revealed a different effect of the runoff coefficient on soil loss due to the two erosive processes. Different relationships expressive of topographic factors were also deduced. The USLE-MB estimation performance significantly improved when operating the distinction between the two databases compared with the model parameterized on the complete database. The model was particularly reliable in estimating the highest event soil loss values, which were associated with the occurrence of rills. Finally, the proposed parameterization procedure lends itself to being tested in the framework of empirical soil loss modeling.
20
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.
Abstract:
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.
21
Suryawanshi, Ashwini, Snehil Dubey, and Mahima Sharma. "Evaluating Soil Erosion through Geospatial Techniques: Difficulties and Prospects in the Context of the Central Indian Chambal River Basin." International Journal of Environment and Climate Change 13, no. 11 (December 8, 2023): 4518–33. http://dx.doi.org/10.9734/ijecc/2023/v13i113632.
Abstract:
Soil erosion is the greatest threat to the ecosystem which gets accelerated due to environmental agents such as water and wind as well as anthropogenic activities. Effective estimation of soil degradation plays an important role in planning preventive measures and conserving the soil. This study was carried out to provide decision-makers with a picture of soil erosion in Madhya Pradesh's Chambal basin and to identify environmentally hot areas to assist in planning effective conservation measures. By using a few input parameters to create raster maps of the Rainfall erosivity factor (R), Soil erodibility factor (K), Topographic factor (LS), Cover and management factor (C), and Support practice factor (P), the Universal Soil Loss Equation (USLE) and Revised Universal Soil Loss Equation (RUSLE) models were applied. The classification of soil erosion and the area portion in each class was then acknowledged. According to the USLE and RUSLE models, the average soil loss for the entire basin is 2.00 t ha-1 yr-1 and 3.04 t ha-1 yr-1, respectively. According to the USLE and RUSLE models, the ranges under severe risk are 0.33% and 0.76%, while the ranges under extremely severe risk are 0.45% and 0.78%, respectively. The land use/land cover (LULC) map for the study area was acquired from satellite data in the USLE, and the Normalized Difference Vegetation Index (NDVI) map was incorporated into the RUSLE model to enhance the comprehension and identification of vegetation. This integration is crucial for capturing detailed information in the RUSLE model. Consequently, RUSLE yields superior results compared to the USLE model, underscoring the significance of incorporating finer details, especially those related to vegetation, for more accurate outcomes
22
Marques, Valter, Marcos Ceddia, Mauro Antunes, Daniel Carvalho, Jamil Anache, Dulce Rodrigues, and Paulo Oliveira. "USLE K-Factor Method Selection for a Tropical Catchment." Sustainability 11, no. 7 (March 27, 2019): 1840. http://dx.doi.org/10.3390/su11071840.
Abstract:
The use of the Universal Soil Loss Equation (USLE) and the Sediment Delivery Ratio (SDR) facilitates sediment yield (SY) estimates in watersheds. However, the soil loss predictions are frequently unrealistic because of the methods used to estimate the USLE’s factors. Here, we evaluated the performance of methods to estimate the soil erodibility (K-factor) and the influence of its estimation in the SY predictions. K-factor values were obtained from three widely used equations and using a portable rainfall simulator. These values were used to compute annual average soil loss and SY in a tropical watershed. We compared SY estimates with a 15-month observed sediment discharge dataset sampled in the catchment outlet. The most reliable method for the K-factor estimating was the USLE nomograph. Furthermore, our results indicate that the use of a portable rainfall simulator to estimate the K-factor tends to underestimate soil loss and sediment delivery.
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Noor, Hamze, Seyed Khalagh Mirnia, Somaye Fazli, Mohamad bagher Raisi, and Mahdi Vafakhah. "Application of MUSLE for the prediction of phosphorus losses." Water Science and Technology 62, no. 4 (August 1, 2010): 809–15. http://dx.doi.org/10.2166/wst.2010.092.
Abstract:
Soil erosion in forestlands affects not only land productivity but also the water body down stream. The Universal Soil Loss Equation (USLE) has been applied broadly for the prediction of soil loss from upland fields. However, there are few reports concerning the prediction of nutrient (P) losses based on the USLE and its versions. The present study was conducted to evaluate the applicability of the deterministic model Modified Universal Soil Loss Equation (MUSLE) to estimation of phosphorus losses in the Kojor forest watershed, northern Iran. The model was tested and calibrated using accurate continuous P loss data collected during seven storm events in 2008. Results of the original model simulations for storm-wise P loss did not match the observed data, while the revised version of the model could imitate the observed values well. The results of the study approved the efficient application of the revised MUSLE in estimating storm-wise P losses in the study area with a high level of agreement of beyond 93%, an acceptable estimation error of some 35%.
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Schürz, Christoph, Bano Mehdi, Jens Kiesel, Karsten Schulz, and Mathew Herrnegger. "A systematic assessment of uncertainties in large-scale soil loss estimation from different representations of USLE input factors – a case study for Kenya and Uganda." Hydrology and Earth System Sciences 24, no. 9 (September 15, 2020): 4463–89. http://dx.doi.org/10.5194/hess-24-4463-2020.
Abstract:
Abstract. The Universal Soil Loss Equation (USLE) is the most commonly used model to assess soil erosion by water. The model equation quantifies long-term average annual soil loss as a product of the rainfall erosivity R, soil erodibility K, slope length and steepness LS, soil cover C, and support measures P. A large variety of methods exist to derive these model inputs from readily available data. However, the estimated values of a respective model input can strongly differ when employing different methods and can eventually introduce large uncertainties in the estimated soil loss. The potential to evaluate soil loss estimates at a large scale is very limited due to scarce in-field observations and their comparability to long-term soil estimates. In this work we addressed (i) the uncertainties in the soil loss estimates that can potentially be introduced by different representations of the USLE input factors and (ii) challenges that can arise in the evaluation of uncertain soil loss estimates with observed data. In a systematic analysis we developed different representations of USLE inputs for the study domain of Kenya and Uganda. All combinations of the generated USLE inputs resulted in 972 USLE model setups. We assessed the resulting distributions in soil loss, both spatially distributed and on the administrative level for Kenya and Uganda. In a sensitivity analysis we analyzed the contributions of the USLE model inputs to the ranges in soil loss and analyzed their spatial patterns. We compared the calculated USLE ensemble soil estimates to available in-field data and other study results and addressed possibilities and limitations of the USLE model evaluation. The USLE model ensemble resulted in wide ranges of estimated soil loss, exceeding the mean soil loss by over an order of magnitude, particularly in hilly topographies. The study implies that a soil loss assessment with the USLE is highly uncertain and strongly depends on the realizations of the model input factors. The employed sensitivity analysis enabled us to identify spatial patterns in the importance of the USLE input factors. The C and K factors showed large-scale patterns of importance in the densely vegetated part of Uganda and the dry north of Kenya, respectively, while LS was relevant in small-scale heterogeneous patterns. Major challenges for the evaluation of the estimated soil losses with in-field data were due to spatial and temporal limitations of the observation data but also due to measured soil losses describing processes that are different to the ones that are represented by the USLE.
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Andriyani, Idah, and Yaumil Zahro Fadila. "The Influence of Soil Characteristic Changes on Erosion Rates Based on the Universal Soil Loss Equation (USLE) Method." Jurnal Teknik Pertanian Lampung (Journal of Agricultural Engineering) 13, no. 1 (March 13, 2024): 278. http://dx.doi.org/10.23960/jtep-l.v13i1.278-287.
Abstract:
Soil erodibility is a major factor contributing to soil erosion as well as the intensity of erosion rates. This study aims to validate soil erodibility values based on soil type maps through field measurements of erosion hazard level (EHL) within the Antrokan Sub-watershed area, Jember. Input data included digital maps comprising rainfall data (from 2004 to 2019), soil types, land use allocation, and Digital Elevation Model (DEM). Erosion rate was calculated using the USLE model, which was executed in two steps: (1) processing and interpreting erosion variables (R, K, LS, CP), and (2) calculating and classifying soil EHL. Field measurements indicated that soil erodibility value (K) is higher as compared to the value derived from the soil type maps. This discrepancy impacts the predicted erosion rate, where using measured K values resulted in the severe EHL category, with erosion rate of 1131 t.ha–1.y–1, while using K values based on soil type maps produced erosion rate of 432.2 t.ha–1.y–1, categorized as moderate level. In this sense, validation of soil erodibility data is important for predicting erosion rate using USLE method. In conclusion, the soil conservation implementation to reduce K values is necessary in the Antrokan Sub-watershed area Keywords: Erodibility, Erosion hazard level, Nomograph, Soil type, USLE.
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Zhang, Hong Ming, Qin Ke Yang, Shu Qin Li, Mei Li Wang, Ming Ying, Huan Lang, and Xue Wen Dong. "Design and Implementation of Regional LS Factor Computing Tool Based on GIS and Array Operation." Applied Mechanics and Materials 394 (September 2013): 509–14. http://dx.doi.org/10.4028/www.scientific.net/amm.394.509.
Abstract:
For over 40 years, the universal soil loss equation (USLE) and its revised version the revised universal soil loss equation (RUSLE) have been used all over the world for soil mean annual loss per area unit. Because of the watershed erosion models are under developing, many researchers applied the USLE and RUSLE to estimate soil loss in watershed estimations. However, a major limitation is the difficulty in extracting the LS factor. The geographic information system-based (GIS-based) methods which have been developed for estimating the slope length for USLE and RUSLE model also have limitations. A series of ARC/INFO AML program was created that can calculate LS factor for the USLE, however the program need a very long time to run in wide-ranging areas. The flowpath and cumulative cell length-based method (FCL) overcomes this disadvantage but does not consider the following questions: (1) Some original AML program functions are not achieved, so results are different. (2) Using USLE to calculate LS factor that do not adapt to the erosion environment of China. (3) There isnt a friendly graphic user interface. The purpose of this research was to overcome these limitations and extend the FCL method through Integrating CSLE equation. We developed a LS calculation tool (LS-TOOL) in Microsofts .NET environment using C# with a user-friendly interface. Comparing the LS factor calculated with the FCL method and AML method, LS factor values generated by using LS-TOOL method delivers improved results. The LS-TOOL algorithm can automatically calculate slope length, slope steepness, L factor, S factor, and LS factors, providing the results as ASCII files which can be easily used in some GIS software. This study is an important step forward in conducting fast large-scale erosion evaluation.
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Faisol, Arif, and Mashudi Mashudi. "Estimation of Erosion Potentials through Utilization of Remote Sensing Data and The Universal Soil Loss Equation Model." Jurnal Teknik Pertanian Lampung (Journal of Agricultural Engineering) 12, no. 1 (March 20, 2023): 223. http://dx.doi.org/10.23960/jtep-l.v12i1.223-235.
Abstract:
Remote sensing data and USLE models have been used widely for erosion analysis. In Indonesia, the USLE model is a reference in erosion analysis to assess land suitability for agricultural crop development. Erosion analysis using remote sensing data provides various advantages, including good accuracy, lower costs, and can analyze erosion rates quickly compared to direct measurement methods. The aim of this study was to analyze the potential erosion in the Arui watershed - Manokwari Regency – West Papua Province using remote sensing data and USLE models. The research was conducted from April to July 2022, with three main stages i.e data inventory, data analysis, and erosion rate estimation. The research shows that the potential erosion rate in the Arui watershed is 15 tons/ha/year or 3.480 tons/year, thus exceeding the tolerable soil loss (TSL) erosion rate threshold of 9.6 tons/ha/year. Therefore, a conservation and restoration program is needed to control the erosion rate in the Arui watershed. Keywords: Erosion rate, Remote sensing, Tolerable soil loss, USLE, Watershed
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Silburn, D. M. "Hillslope runoff and erosion on duplex soils in grazing lands in semi-arid central Queensland. III. USLE erodibility (K factors) and cover - soil loss relationships." Soil Research 49, no. 2 (2011): 127. http://dx.doi.org/10.1071/sr09070.
Abstract:
Measured Universal Soil Loss Equation (USLE) soil erodibility (K) values are not available for soils in grazing lands in northern Australia. The K values extrapolated from croplands are used in national and river-basin scale assessments of hillslope erosion, using an assumption that the cover factor (C) equals 0.45 for undisturbed (uncultivated) bare soil. Thus, the K needed for input into the models is the measured K for undisturbed soil (KU) divided by 0.45. Runoff and erosion data were available for 7 years on 12 hillslope plots with cover of 10–80%, with and without grazing, with and without tree canopy cover, on a variety of soils according to various soil classification systems. Soils were grouped into those derived from sandstone (SS), mudstone (MS), and eroded mudstone (MSe). These data were used to determine USLE KU, K, and C factor–cover relationships. Methods used to fit the parameters affected the results; minimising the sum of squares of errors in soil losses gave better results than fitting an exponential equation. The USLE LS (length–slope) factor explained the increase in measured average annual soil loss with slope, for plots with low cover. Erodibility (K) was 0.042 for SS and MS soils, irrespective of Australian Soil Classification (Chromosol, Kandosol, Rudosol, Sodosol, Tenosol); K was 0.062 for exposed, decomposing mudstone (MSe Leptic Rudosol). The measured K factor for SS and MS soils was close to that used in catchment-wide soil loss estimation for the site (0.039). This indicates that the method used for estimating K from soil properties (derived from cultivated soils) gave a reasonable estimate of K for the main duplex soils at the study site, as long as the correction for undisturbed soil is used in deriving K from measured data and in applying the USLE model. A 20% increase in K (0.050) for SS and MS soils may be warranted for heavy grazing by cattle. The C factor–cover relationship was different from the standard revised USLE (RUSLE) relationship, requiring a greater exponent (‘bcov’) of 0.075, rather than the default for cropland of 0.035. Increasing cover is therefore more effective at the site than suggested by the USLE. Parameters of USLE were also derived for bedload, allowing suspended load to be calculated by subtracting bedload from total soil loss.
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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.
Abstract:
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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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.
Abstract:
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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WILSON, JOHN P. "SOIL EROSION FROM AGRICULTURAL LAND IN THE LAKE SEMCOE–COUCHICHING BASIN, 1800–1981." Canadian Journal of Soil Science 69, no. 1 (February 1, 1989): 137–51. http://dx.doi.org/10.4141/cjss89-013.
Abstract:
Soil losses from agricultural land in the Lake Simcoe–Couchiching Basin between 1800 and 1981 are estimated with the universal soil loss equation (USLE). Existing methods and data are used to estimate and combine the six USLE factors with one exception. A new method that is consistent with the USLE slope definitions was developed and used to estimate the topographic factor. The results of the present study add to earlier erosion assessments because they describe the erosion hazard throughout the basin from the first days of forest clearance and agricultural settlement to the present. In particular, they indicate that soil loss rates on agricultural land have gone almost full circle from high to low and back to high rates in the past 130 yr. This result indicates the need to continue our search for permanent and more viable systems of land use. Key words: Soil erosion, potential soil loss, regional assessment
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Delgado Bejarano, Laura, Hugo González Sanchez, and Dario Castañeda Sánchez. "SOIL EROSION BY HAND TOOLS FOR SMALL-SCALE TILLAGE ON HILLSLOPES ASSESSED THROUGH THE UNIVERSAL SOIL LOSS EQUATION." Chilean journal of agricultural & animal sciences 39, no. 1 (2023): 75–89. http://dx.doi.org/10.29393/chjaa39-7seld30007.
Abstract:
The objective of this study was to evaluate and compare the erosion rates generated by two types of hand tools for small-scale tillage on a hillslope, using experimental tests and the Universal Soil Loss Equation (USLE). The hand tools evaluated were a conventional hoe and a redesigned furrowing hoe. The experimental work was conducted in a 145 m2 plot with an average slope of 45% in Colombia. Three treatments were evaluated: a) Zero tillage and no herbicide (control); b) tillage with a conventional hoe plus herbicide; c) tillage with a furrowing hoe plus herbicide. Each treatment was represented by a sedimentation plot, using three repetitions in blocks (lower, middle, and upper parts of each plot), according to the maximum slope gradient. Both hand tillage tools generated high to extremely high erosion rates with differences of up to 8.1 times between them. Both types of tools accelerated soil erosion rates, being higher in furrowing hoe tillage. The USLE method showed no differences in erosion rates between the tillage methods, while differences were found in the experimental tests. This is explained by the lower sensitivity of the USLE to detect small-scale changes in factors such as soil type, cover, and slope.
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Kinnell. "A Review of the Science and Logic Associated with Approach Used in the Universal Soil Loss Equation Family of Models." Soil Systems 3, no. 4 (September 24, 2019): 62. http://dx.doi.org/10.3390/soilsystems3040062.
Abstract:
Soil erosion caused by rain is a major factor in degrading agricultural land, and agricultural practices that conserve soil should be used to maintain the long-term sustainability of agricultural land. The Universal Soil Loss Equation (USLE) was developed in the 1960s and 1970s to predict the long-term average annual soil loss from sheet and rill erosion on field-sized areas as an aid to making management decisions to conserve soil. The USLE uses six factors to take account of the effects of climate, soil, topography, crops, and crop management, and specific actions designed to conserve soil. Although initially developed as an empirical model based on data from more than 10,000 plot years of data collected in plot experiments in the USA, the selection of the independent factors used in the model was made taking account of scientific understanding of the drivers involved in rainfall erosion. In addition, assumptions and approximations were needed to make an operational model that met the needs of the decision makers at that time. Those needs have changed over time, leading to the development of the Revised USLE (RUSLE) and a second version of that, the Revised USLE, Version 2 (RUSLE2). While the original USLE model was not designed to predict short-term variations in erosion well, these developments have involved more use of conceptualization in order to deal with the time-variant impacts of the drivers involved in rainfall erosion. The USLE family of models is based on the concept that the “unit” plot, a bare fallow area 22.1 m long on a 9% slope gradient with cultivation up and down the slope, provides a physical situation where the effect of climate and soil on rainfall erosion can be determined without the need to consider the impact of the four other factors. The science and logic associated with this approach is reviewed. The manner by which the soil erodibility factor is determined from plot data ensures that the long-term average annual soil loss for the unit plot is predicted well, even when the assumption that event soil loss is directly related to the product of event rainfall energy, and the maximum 30-min intensity is not wholly appropriate. RUSLE2 has a capacity to use CLIGEN, the weather generator used in WEPP, and so can predict soil losses based on individual storms in a similar way to WEPP. Including a direct consideration of runoff in determining event erosivity enhances the ability to predict event soil losses when runoff is known or predicted well, but similar to more process-based models, this ability is offset by the difficulty in predicting runoff well.
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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.
Abstract:
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.
35
Hrabalíková, M., and M. Janeček. "Comparison of different approaches to LS factor calculations based on a measured soil loss under simulated rainfall." Soil and Water Research 12, No. 2 (April 10, 2017): 69–77. http://dx.doi.org/10.17221/222/2015-swr.
Abstract:
Geographic Information Systems (GIS) in combination with soil loss models can enhance evaluation of soil erosion estimation. SAGA and ARC/INFO geographic information systems were used to estimate the topographic (LS) factor of the Universal Soil Loss Equation (USLE) that in turn was used to calculate the soil erosion on a long-term experimental plot near Prague in the Czech Republic. To determine the influence of a chosen algorithm on the soil erosion estimates a digital elevation model with high accuracy (1 × 1 m) and a measured soil loss under simulated rainfall were used. These then provided input for five GIS-based and two manual procedures of computing the combined slope length and steepness factor in the (R)USLE. The results of GIS-based (R)USLE erosion estimates from the seven procedures were compared to the measured soil loss from the 11 m long experimental plot and from 38 rainfall simulations performed here during 15 years. The results indicate that the GIS-based (R)USLE soil loss estimates from five different approaches to calculation of LS factor are lower than the measured average annual soil loss. The two remaining approaches over-predicted the measured soil loss. The best method for LS factor estimation on field scale is the original manual method of the USLE, which predicted the average soil loss with 6% difference from the measured soil loss. The second method is the GIS-based method that concluded a difference of 8%. The results of this study show the need for further work in the area of soil erosion estimation (with particular focus on the rill/interrill ratio) using the GIS and USLE. The study also revealed the need for an application of the same approach to catchment area as it might bring different outcomes.
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Songara, Jaysukh C., Fenil R. Gandhi, Jayantilal N. Patel, and Indra Prakash. "Integrated Universal Soil Loss Equation (USLE) and Geospatial Approach to Assess Soil Erosion in Machhu Sub-watershed, Morbi, India." Journal of the Geological Society of India 100, no. 3 (March 1, 2024): 391–98. http://dx.doi.org/10.17491/jgsi/2024/173845.
Abstract:
Abstract Soil erosion is a severe and rapidly rising issue in many parts of the world due to human activities such as farming practices, land excavation for development and deforestation. Moreover, it can negatively impact water availability, agricultural growth, and ultimately, countries’ long-term economy. A quantitative and consistent land degradation assessment is vital for proper planning of soil conservation activities in a catchment or watershed. The Universal Soil Loss Equation (USLE) model is applied in this study to address the issue of soil erosion in the Machhu-sub watershed (24769.63 ha), located in Saurashtra, India. The landscape feature of the study area includes basalt type rock and water body. In this USLE model study we have used input parameters such as rainfall erosivity (R), soil erosivity (K), cover management (C), slope length and steepness (LS), and conservation practice (P) integrated with Geographical Information System (GIS) to analyze and obtain the estimated annual soil loss. Results indicated that the overall soil loss in the study area can be classified into five categories: Very Low (0-1), Low (1-3), Low moderate (3-5), Moderate (5-10), and High ( >10 tons/ha/year). The finding includes the overall soil potential loss of the Machhu sub-watershed is 14.90 tons/ha/year. Furthermore, 60.86 % of the agricultural area is affected by soil erosion. Therefore, the necessary soil conservation methods can be planned in the Machu-sub watershed area based on the USLE analysis. These findings may assist researchers, scientists, and policymakers in building a concrete strategy for sustainable development of not only study area but other catchments also.
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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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Wang, Ai Juan, and Zhi Guang Li. "Spatial Distribution of Soil Erodibility in Upper Yangtze River Region." Advanced Materials Research 610-613 (December 2012): 2944–47. http://dx.doi.org/10.4028/www.scientific.net/amr.610-613.2944.
Abstract:
Soil erodibility is the K factor in the universal soil loss equation (USLE). It is generally characterized through soil physical properties-based indices. The soil erodibility of the Upper Yangtze River basin was determined by the national second soil survey data of China. The results shown that the formula calculated K in USLE model has its limitation in the area when the soil organic matter content is bigger than 12 %. An updating relation was developed by 26 sample points’ properties data. The mean value of soil erodibility in study area is 0.0344, and the biggest value distributed in Sichan basin due to the influence of terrain. Soil erodibility in the study area distributes evenly.
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M, MUTHAMIL SELVAN, SUBRAMANIAN K, RAJENDRAN V, and RANGASAMY K. "Estimation of runoff and soil loss for a hilly watershed." Madras Agricultural Journal 88, june (2001): 284–86. http://dx.doi.org/10.29321/maj.10.a00339.
Abstract:
Estimation and analysis of rainfall-runoff-discharge data for a watershed would be of great use. for optimum planning and managing the watershed. Universal Soil Loss Equation (USLE) was used to estimate the annual soil loss from a hilly watershed in the Nilgiris. A set of linear regression models were developed by relating (1) rainfall and runoff, (2) rainfall and soil loss and they can be used for prediction of the runoff and soil loss for the watershed. Packages of soil and water conservation measures were recommendd for the watershed as the present rinoff and soil loss wre found to be enormous.
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Oda, Masato, Yin Yin Nwe, and Hide Omae. "Use of the K factor from the Universal Soil Loss Equation can show arable land in Palau." F1000Research 9 (June 24, 2020): 89. http://dx.doi.org/10.12688/f1000research.22229.2.
Abstract:
From the viewpoint of sustainability, the annual soil erosion must be controlled below an erosion level. Palau is an island in the Micronesia region of the western Pacific Ocean. The island receives heavy rainfall and has steep slopes, so 80% of the land is categorized within the most fragile rank, with at most 1 ton per acre per year (T factor = 1). We tested several methods of preventing soil erosion on the land, with a slope of 15.4° (13.4°–17.3°), cultivated the land, planted sweet potatoes, and compared the amount of soil erosion. Surprisingly, there was no erosion at all in all plots (including control plots), although there were 24 rainfall events and the USLE equation predicted 32 ton per ha of the soil erosion in the cropping period. For the parameters of the USLE equation used in the present study, only the K factor was not actually measured. This means the K factor was larger than the actual value. Land at low risk of soil erosion and suitable for agriculture can be found by measuring K factor locally, even if the area is categorized as unsuitable.
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Ruppenthal, M., D. E. Leihner, T. H. Hilger, and J. A. Castillo F. "Rainfall Erosivity and Erodibility of Inceptisols in the Southwest Colombian Andes." Experimental Agriculture 32, no. 1 (January 1996): 91–101. http://dx.doi.org/10.1017/s0014479700025904.
Abstract:
SUMMARYThe rainfall erosivity (R) and soil erodibility (K) factors of the Universal Soil Loss Equation (USLE) were determined on two sites in the Colombian Cauca Department over a five year period when rainfall was mostly lower than average. The results showed that the high erosion potential of the soils can be attributed more to high rain erosivity than soil erodibility. The R factor explained between 59 and 81% of the variation in soil loss recorded on continuously clean-tilled fallow plots. The erodibility of Inceptisols in the study region is classified as low. Values for soil erodibility (K) ranged from 0.012 to 0.015 (measured in SI units) in the fifth year of permanent bare fallowing. K factors were higher in the rainy than in the dry season. Soils, previously under grass vegetation, were very resistant to erosion in the first two years of bare fallowing. In the third year erodibility increased sharply and continued to increase steadily until the sixth year. K factors predicted by the USLE nomograph underestimated the empirically-determined erodibility of these highly aggregated clay soils.
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Janeček, M., V. Květoň, E. Kubátová, and D. Kobzová. "Differentiation and regionalization of rainfall erosivity factor values in the Czech Republic." Soil and Water Research 7, No. 1 (March 15, 2012): 1–9. http://dx.doi.org/10.17221/2/2011-swr.
Abstract:
The rain erosivity R-factor is one of the main parameters in the Universal Soil Loss Equation (USLE). This paper describes the procedure used to update, differentiate and regionalize the rainfall erosivity R-factor. For the Czech Republic it is recommended to use the average value R = 40.
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Mejía-Marcacuzco, Jesús, Edwin Pino-Vargas, Edilberto Guevara-Pérez, Víctor Olivos-Alvites, and Milagros Condori-Ventura. "Predicción espacial de la erosión del suelo en zonas áridas mediante teledetección. Estudio de caso: Quebrada del Diablo, Tacna, Perú." Revista Ingeniería UC 28, no. 2 (September 1, 2021): 252–64. http://dx.doi.org/10.54139/revinguc.v28i2.24.
Abstract:
La presente investigación trata sobre la evaluación de la erosión hídrica del suelo en una zona árida de la región Tacna al sur de Perú, tomando como caso de estudio la Quebrada del Diablo. Se usaron los modelos USLE (Universal Soil Loss Equation) y RUSLE (Revised Universal Soil Loss Equation) en conexión con sistemas geográficos de información (GIS) y técnicas de teledetección. Los factores R, K, LS, C y P de los modelos fueron calculados a partir de información pluviométrica local registrada en tres estaciones y de datos provenientes de sensores remotos integrados mediante el GIS, obteniendo así una simple y efectiva herramienta para determinar mapas, áreas y tasas de erosión. Los resultados indican que la máxima tasa de erosión hídrica, debido a la precipitación extraordinaria ocurrida el año 2020, calculada mediante ambos modelos, varía de 0 a 50 t/ha/año, variación considerada en el rango bajo, ligero y moderado. Los modelos USLE y RUSLE arrojan un potencial de erosión menor a 10 t/ha/año para el 71,81 % y el 45,27 % del área de estudio, respectivamente; para tazas de erosión mayores a 10 t/ha/año las áreas calculadas con el modelo RUSLE superan a los estimados con el modelo USLE.
44
Armstrong, JL. "Runoff and soil loss from bare fallow plots at Inverell, New-South-Wales." Soil Research 28, no. 4 (1990): 659. http://dx.doi.org/10.1071/sr9900659.
Abstract:
Three 41 m long bare fallow plots were established on a chocolate soil (Mollisol) at Inverell in late 1976 to determine the soil erodibility (K) factor for use in the Universal Soil Loss Equation (USLE). The K-factor was estimated as 0.018 tonne hectare hour per hectare megajoule millimetre, indicating a soil of low to moderate erodibility. This value was close to that predicted from the soil erodibility nomograph used in the USLE. The average annual soil loss over the eight year period was 51 t/ha, while the largest individual storm soil loss from the plots was 47 t/ha. The two largest soil losses in each year accounted for 60-99% of the annual soil loss. Various erosivity indices were examined for their ability to predict runoff and soil loss from individual erosive storms. Indices which had separate variables for soil particle detachment (energy component) and particle transport (runoff component) were superior, although a large proportion of the variation in runoff and soil loss remained unaccounted for, and the possible reasons for this are examined. The highest correlation was obtained between soil loss and runoff amount.
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Posch, Maximilian, and Seppo Rekolainen. "Erosivity factor in the Universal Soil Loss Equation estimated from Finnish rainfall data." Agricultural and Food Science 2, no. 4 (July 1, 1993): 271–79. http://dx.doi.org/10.23986/afsci.72650.
Abstract:
Continuous rainfall data recorded for many years at 8 stations in Finland were used to estimate rainfall erosivity, a quantity needed for soil loss predictions with the Universal Soil Loss Equation (USLE). The obtained erosivity values were then used to determine the 2 parameters of a power-law function describing the relationship between daily precipitation and erosivity. This function is of importance in erosion modeling at locations where no breakpoint rainfall data are available. The parameters of the power-law were estimated both by linear regression of the log-transformed data and by non-linear least-square fitting of the original data. Results indicate a considerable seasonal (monthly) variation of the erosivity, whereas the spatial variation over Finland is rather small.
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Panicker, G. K., G. A. Weesies, A. H. Al-Humadi, C. Sims, L. C. Huam, J. Harness, J. Bunch, and T. E. Collins. "391 C-factor Research on Horticultural Crops for Erosion Prediction: Philosophy and Methodology of Data Collection." HortScience 35, no. 3 (June 2000): 460C—460. http://dx.doi.org/10.21273/hortsci.35.3.460c.
Abstract:
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.
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Luo, Banglin, Zhen Han, Jing Yang, and Qing Wang. "Assessment of Erosion Characteristics in Purple and Yellow Soils Using Simulated Rainfall Experiments." International Journal of Environmental Research and Public Health 19, no. 1 (December 30, 2021): 357. http://dx.doi.org/10.3390/ijerph19010357.
Abstract:
Soil erosion of sloped lands is one of the important sources of substantive sediments in watersheds. In order to investigate erosion characteristics of sloped lands during rainfall events in the Three Gorges Reservoir Area, erosion processes of purple and yellow soils under different slope gradients and rainfall intensities were studied by using a rainfall simulator. The results showed that the sediment concentration in runoff was closely correlated with rainfall intensity. The sediment concentration in runoff gradually rose to a peak with time, and then gradually declined and approach a steady rate during simulation rainfall events. The particle size distribution of surface soils before the rainfall was different from that after the rainfall. Soil erosion mainly resulted in the loss of fine particles of surface soil through runoff, and the fine particles of soil were enriched in sediments. Soil erosion rates were gradually increased with the slope gradient when the slope gradient was less than 10°, and significantly increased when the slope gradient was more than 10°. The slope factor of yellow soil could be fitted well to that calculated by the formula of Universal Soil Loss Equation (USLE). The trend of the slope factor of purple soil was similar to that of the slope factor that was derived from USLE. Therefore, the effect of slope gradients on soil erosion need to be further researched when USLE was applied to predict erosion in purple soil area.
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Fiener, Peter, Tomáš Dostál, Josef Krása, Elmar Schmaltz, Peter Strauss, and Florian Wilken. "Operational USLE-Based Modelling of Soil Erosion in Czech Republic, Austria, and Bavaria—Differences in Model Adaptation, Parametrization, and Data Availability." Applied Sciences 10, no. 10 (May 25, 2020): 3647. http://dx.doi.org/10.3390/app10103647.
Abstract:
In the European Union, soil erosion is identified as one of the main environmental threats, addressed with a variety of rules and regulations for soil and water conservation. The by far most often officially used tool to determine soil erosion is the Universal Soil Loss Equation (USLE) and its regional adaptions. The aim of this study is to use three different regional USLE-based approaches in three different test catchments in the Czech Republic, Germany, and Austria to determine differences in model results and compare these with the revised USLE-base European soil erosion map. The different regional model adaptations and implementation techniques result in substantial differences in test catchment specific mean erosion (up to 75% difference). Much more pronounced differences were modelled for individual fields. The comparison of the region-specific USLE approaches with the revised USLE-base European erosion map underlines the problems and limitations of harmonization procedures. The EU map limits the range of modelled erosion and overall shows a substantially lower mean erosion compared to all region-specific approaches. In general, the results indicate that even if many EU countries use USLE technology as basis for soil conservation planning, a truly consistent method does not exist, and more efforts are needed to homogenize the different methods without losing the USLE-specific knowledge developed in the different regions over the last decades.
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Steinhoff-Knopp, Bastian, and Benjamin Burkhard. "Mapping Control of Erosion Rates: Comparing Model and Monitoring Data for Croplands in Northern Germany." One Ecosystem 3 (June 12, 2018): e26382. http://dx.doi.org/10.3897/oneeco.3.e26382.
Abstract:
Control of erosion rates (CER) is a key ecosystem service for soil protection. It is mandatory for sustaining the capacity, especially of agroecosystems, to provide ecosystem services. By applying an established framework to assess soil regulating services, this study compares two approaches to assess CER provision for 466 ha of cropland in Lower Saxony (Central Northern Germany). In a "sealed modelling approach", the structural and the mitigated structural impact were modelled by applying the Universal Soil Loss Equation (USLE). The second approach uses spatially explicit long-term monitoring data on soil loss rates obtained in the investigation area as an alternative to the USLE-based modelled mitigated structural impact. Assuming that the monitoring data have a higher reliability than the modelled data, the comparison of both approaches demonstrated the uncertainties of the USLE-based assessment of CER. The calculated indicators based on a sound monitoring database on soil loss rates showed that, due to limitations of the USLE model, the structural impact in thalwegs has been underestimated. Incorporating models with the ability to estimate soil loss by rilling und gullying can help to overcome this uncertainty. The produced set of complementary large-scale CER maps enables an integrated analyses of CER. In the entire investigation area, the provision of CER regulating ecosystem services was generally high, indicating good management practices. Differences at the field scale and between the different regions can be explained by variations of the structural impact and the management practices.
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Carra, Bruno Gianmarco, Giuseppe Bombino, Manuel Esteban Lucas-Borja, Pietro Denisi, Pedro Antonio Plaza-Álvarez, and Demetrio Antonio Zema. "Modelling the Event-Based Hydrological Response of Mediterranean Forests to Prescribed Fire and Soil Mulching with Fern Using the Curve Number, Horton and USLE-Family (Universal Soil Loss Equation) Models." Land 10, no. 11 (October 31, 2021): 1166. http://dx.doi.org/10.3390/land10111166.
Abstract:
The SCS-CN, Horton, and USLE-family models are widely used to predict and control runoff and erosion in forest ecosystems. However, in the literature there is no evidence of their use in Mediterranean forests subjected to prescribed fire and soil mulching. To fill this gap, this study evaluates the prediction capability for runoff and soil loss of the SCS-CN, Horton, MUSLE, and USLE-M models in three forests (pine, chestnut, and oak) in Southern Italy. The investigation was carried out at plot and event scales throughout one year, after a prescribed fire and post-fire soil mulching with fern. The SCS-CN and USLE-M models were accurate in predicting runoff volume and soil loss, respectively. In contrast, poor predictions of the modelled hydrological variables were provided by the models in unburned plots, and by the Horton and MUSLE models for all soil conditions. This inaccuracy may have been due to the fact that the runoff and erosion generation mechanisms were saturation-excess and rainsplash, while the Horton and MUSLE models better simulate infiltration-excess and overland flow processes, respectively. For the SCS-CN and USLE-M models, calibration was needed to obtain accurate predictions of surface runoff and soil loss; furthermore, different CNs and C factors must be input throughout the year to simulate the variability of the hydrological response of soil after fire. After calibration, two sets of CNs and C-factor values were suggested for applications of the SCS-CN and USLE-M models, after prescribed fire and fern mulching in Mediterranean forests. Once validated in a wider range of environmental contexts, these models may support land managers in controlling the hydrology of Mediterranean forests that are prone to wildfire risks.