Relevant bibliographies by topics / Universal Soil Loss Equation

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  2. Dissertations / Theses
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Academic literature on the topic 'Universal Soil Loss Equation'

Author: Grafiati
Published: 4 June 2021
Last updated: 10 March 2023

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Journal articles on the topic "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 (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 (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 (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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7

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 (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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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 (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 (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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Dissertations / Theses on the topic "Universal Soil Loss Equation"

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Kelsey, Kurt L. "Use of the Revised Universal Soil Loss Equation (Rusle) to predict event soil loss /." Link to abstract, 2002. http://epapers.uwsp.edu/abstracts/2002/Kelsey.pdf.

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Yuan, Xu. "EVALUATION OF THE PHOSPHORUS LOSS ASSESSMENT TOOL (PLAT) AND REVISED UNIVERSAL SOIL LOSS EQUATION (RUSLE) USING GEOSPATIAL INFORMATION." NCSU, 2007. http://www.lib.ncsu.edu/theses/available/etd-12212006-120809/.

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Excessive agricultural phosphorus (P) has been a major contributor to non-point source pollution. North Carolina developed the Phosphorus Loss Assessment Tool (PLAT) to evaluate the potential P loss from agricultural fields to waterbodies via four components. Our overall goal was to evaluate the potential of using spatial data to estimate P loss without physically visiting fields since many PLAT required parameters occur in spatial formats. The objective of the first study was to assess the possibility of spatial implementation of PLAT and to compare the effect of scale on the PLAT numerical results and the associated categorical rankings. Since an important input parameter, the average annual soil loss determined by the Revised Universal Soil Loss Equation, is not directly available from field measurement, our objective in the second study was to assess the potential of obtaining RUSLE estimates, specifically the topography factor LS, through Digital Elevation Model data in a Geographic Information System environment. In the first study, two methods of whole field average (WFA) and grid average (GA) were used to compare the difference in modeling P loss at different scales. The same list of PLAT required parameters were prepared from soil test reports and spatial database at the coarse scale of whole agriculture field and the fine scale of 0.4-ha grid. Soil tolerance value was used to temporarily replace the soil loss data. In the second study, a widely used Arc Macro Language (AML) program for estimating RUSLE topographic factor LS was evaluated through two approaches of whole field (WF) and representative profile (RP) analysis on a North Carolina landscape. Watershed delineation technique was adopted to select the representative profiles based on the references of slope distributions and field subdivisions from NRCS water quality specialists. Results from the first study indicated that soluble and particulate P loss, which occupied 59.3% and 26.3% of the total P loss through WFA method, and 56.1% and 39.0% through GA method, were the major pathways. Leaching P loss from PLAT was negligible. Particulate P loss was sensitive to scale as verified by the 12.7% increase of proportion in total P loss. The difference of particulate P loss through two methods was significant (p < 0.05), but no difference of soluble P loss and P source effect was found on a 95% confidence level. The overall P loss potential through two methods exhibited no significant difference due to the neutralization effect of individual pathways. Results from the second study showed that the AML program alone was not suitable for calculating RUSLE topographic factor on a North Carolina landscape because of the significant underestimation (~35% and ~20% through WF and RP approach, respectively). The concept of representative profile indeed improved the estimation accuracy (~15%), however, the linearity of the fitted line between field measured LS and GIS-aided LS estimate was not satisfactory. An adjustment factor was proposed rectifying the RUSLE-based AML program in order to approximate field measurements. This study demonstrated the potential of implementing PLAT model and the soil loss equation using spatial parameters derived from database instead of visiting the fields. The scale of modeling in estimating particulate P loss and RUSLE topographic factor LS was important and the adjustment factor was necessary to adapt the AML program application. The accuracy of model performance needed to be improved before claiming that GIS-aided PLAT modeling will provide a complete replacement for the field measurement.
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Rapp, John Francis 1963. "Error assessment of the revised universal soil loss equation using natural runoff plot data." Thesis, The University of Arizona, 1994. http://hdl.handle.net/10150/291699.

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The error associated with the Revised Universal Soil Loss Equation (RUSLE) was determined by utilizing data from 21 U.S. sites representing 1704 years of measurements from 206 plots. RUSLE estimates were compared to the measured values for each year and the average value for each plot duration. The model efficiency coefficient on an annual basis was (.58) and on an average annual basis was (.73). The RUSLE was consistent with a previous study of the USLE which tended to over predict on plots with low erosion rates and under predict on plots with high erosion rates. Also the Topographic Factor (LS) value and the Cover and Management Factor (C) value had the most influence on model efficiency. The basis for this study was to compare the RUSLE with the USLE and to compare RUSLE simulations with observed data that was not a part of its critical development.
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Son, Vo Thanh, and n/a. "Evaluation of the USLE (Universal Soil Loss Equation) to estimate soil loss from hobby farms and commercial pastoral properties around Murrumbateman, NSW, Australia." University of Canberra. Applied Science, 1993. http://erl.canberra.edu.au./public/adt-AUC20061108.171337.

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This thesis is an evaluation of the use the USLE to estimate soil loss from two pastoral land uses - commercial properties and "hobby farms" in Murrumbateman. Sensitivity analysis was used to evaluate the USLE components. Sediment measurement in farm dams was taken to estimate sediment yield from several sites, as an alternative approach to study soil loss. The annual soil loss from entire study area was 0.25 t/ha/year whilst these figures from commercial properties and hobby farms were 0.29 t/ha/year and 0.21 t/ha/year, respectively. The annual average sediment yield from three catchments in hobby farms was 0.3 t/ha/year. The USLE was found to be highly sensitive to slope steepness, ground cover and stocking rates. The critical values were 16% for slopes, 35% for the ground cover and 19 Dry Sheep Equivalent/ha for stocking rate. I tentatively conclude that the USLE is sufficiently sensitive to detect differences in soil loss between the two land uses. There is, however, a need to improve the operation of the model in some respects. The use of farm dams for estimating sediment yield also shows promise.
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Marr, Paul Gerard. "Approximating soil loss calculations with satellite data and multivariate regression analyses." Thesis, University of North Texas, 1989. https://digital.library.unt.edu/ark:/67531/metadc798418/.

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Digital satellite remote sensing and Geographic Information Systems (GIS) have been used effectively to determine the Universal Soil Loss Equation (USLE) output for a number of North Texas watersheds. This method involves determining the values of each of the USLE factors and using these factors as information layers within the GIS.
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Sourlamtas, Konstantinos. "Soil Erosion estimation for the Göta Älv river using remote sensing, GIS and the Revised Universal Soil Loss Equation (RUSLE) model." Thesis, Stockholms universitet, Institutionen för naturgeografi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-175412.

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According to previous studies, the study area of Göta Älv river has high risk of landslides along the river banks due to the water flow. Soil erosion can affect the increase of the landslides in an area with unstable soils caused by the increase rainfall. The Swedish climate is getting more vulnerable thus there is a potential increased risk in erosion and landslides due to unpredictable rainfall intensity. This study aims to calculate soil erosion for the Göta Älv river using the Revised Universal Soil Loss Equation (RUSLE) where a comparison of data from remote sensing and meteorological and geological agencies were completed. Two research questions will be addressed, first if the different calculation of the soil erodibility (K) factor affects RUSLE result, and second how much soil erosion occurs and will potentially occur in the future. Factors including rainfall erosivity (R), soil erodibility (K), slope length and steepness (LS), land cover management (C) and conservation practices (P) were analyzed and used as inputs for the RUSLE model. Moreover, three scenarios were applied for the calculation of K factor in order to show how each one can affect the soil erosion result. The scenarios includes the K-scenario 1, 2 and 3, where the values were derived from a world soil database, a table with literature values and estimated field measurements, respectively. Also, three scenarios for R factor were applied for the periods 2000-2018, 2021-2050 and 2069-2098 (R-scenarios 1, 2 and 3) in order to show how future changes to rainfall patterns could affect soil erosion in the Göta Älv river and if it increases the risk of the landslides. The results suggest that the soil erosion varied between 0 – 0.5 t/ha for all the time periods with mean annual soil loss between 20 – 22 t/ha/yr and maximum soil loss between 2158- 5443 t/ha. The difference between the three K factor scenarios is almost 4%, which is pretty low thus, no influence on the soil erosion results. In conclusion, the different calculations of the K factor affected more the estimated maximum soil loss instead of the mean annual soil loss. The different calculations of R factor showed that more than 90% of the total area was not affected by the soil erosion when the soil loss will not be increased considerably in the future due to the rainfall increase.
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Bochichi, Diego da Cruz. "Avaliação do potencial de produção de sedimento na sub bacia hidrográfica do rio Pirajibu-Mirim /." Sorocaba, 2018. http://hdl.handle.net/11449/157448.

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Orientador: Antonio Cesar Germano Martins<br>Banca: Roberto Wagner Lourenço<br>Banca: Darllan Collins da Cunha e Silva<br>Resumo: A perda de solos em bacias hidrográficas pode causar prejuízos ambientais e comprometer os usos múltiplos potenciais desta bacia. Ações como desmatamento, atividades agrícolas, entre outras, expõem o solo aos agentes naturais (precipitação, vento) e estes desprendem e carreiam o solo para as porções mais baixas na bacia, promovendo o assoreamento dos corpos d'água. Estudos sobre o tema são importantes para auxiliar na gestão, manejo e entendimento destes ambientes e mitigação do fenômeno. Neste sentido, este trabalho tem como objetivo estimar a perda de solo na sub-bacia do Pirajibu-Mirim. Para isto, foram utilizadas ferramentas de análise de imagens e de dados da bacia para calcular a Equação Universal da Perda de Solos (EUPS) definida por Wischmeier & Smith (1978). A sub-bacia está inserida no município de Sorocaba-SP e tem uma área de aproximadamente 54 km². Para compor a equação, todas as variáveis foram levantadas separadamente para então calcular a EUPS em ambiente SIG para os anos de 2000 a 2016. Os resultados indicaram que os anos de 2003 e 2012 apresentaram os maiores valores de perdas de solo acima de 200 ton/ha.ano e em 2013 os menores valores de perdas de solo acima de 200 ton/ha.ano. Outra informação importante foi que no período estudado, aproximadamente 50% da área tem perdas de solo abaixo de 15 ton/ha.ano, indicando boa preservação do solo.<br>Abstract: The loss of soil in watershed can cause environmental damage and compromise the multiple potential uses of this watershed. Action as deforestation, agricultural activities, among others, expose the soil to natural agents (precipitation, wind) that causes the release of these soils to lower portion of watershed, promoting silting of the water bodies. Studies about the theme are important to improve the management and understanding of these environments and phenomenon mitigation of silting. Therefore, this work had as aim the estimation of soil loss in sub basin Pirajibu-Mirim. For this, tools of image and data analysis were used to estimate the Universal Soil Loss Equation (USLE) defined by Wischmeier & Smith (1978). The sub basin is inserted in Sorocaba city, São Paulo state and has an area 54km². To compose the equation, the variables were obtained separately and then the USLE was calculated from 2000 to 2016. The results indicated that the years 2003 and 2012 demonstrated the highest values of soil loss above 200 ton/ha.a and 2013 the lowest value of soil loss above 200 ton/ha.a. Other important information is that in studied period, almost 50% of the area had soil lower than 15 ton/ha.a, indicating satisfactory soil preservation.<br>Mestre
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Hunter, Bruce Allan. "A comparison of universal soil loss equation results using a remote sensing/GIS technique to results obtained using a field survey technique." Thesis, University of North Texas, 1990. https://digital.library.unt.edu/ark:/67531/metadc798044/.

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Digital satellite remote sensing and Geographic Information Systems (GIS) have been used in conjunction with the Universal Soil Loss Equation (USLE) to model soil erosion potential within watersheds. This study compared erosion estimates calculated by the remote sensing method to results obtained in the field by soil conservationists using conventional methods.
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Flack, Paul E. 1960. "A method for establishing base-line soil loss rates on surface mine sites." Thesis, The University of Arizona, 1989. http://hdl.handle.net/10150/276985.

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Surface mining operations require a comparison of post-mining erosion rates with pre-mining soil loss to ascertain if remedial measures are needed. In this study the Universal Soil-Loss Equation (USLE) was modified to reflect conditions of western rangelands to develop a procedure for estimating pre-mining soil loss rates. The modification used back-calculation for the C-Factor and an adjusted R-Factor based on storm size. Soil loss simulation based on stochastic precipitation patterns is appropriate to the site--the La Plata mine area in northern New Mexico--and increases the flexibility of the USLE as a soil loss predictor for western rangelands.
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Costa, Ana Lúcia Carneiro da [UNESP]. "Estudo da vulnerabilidade à erosão com a aplicação da Equação Universal de Perda de Solo na alta bacia hidrográfica do rio Jacaré Pepira, utilizando SIG/SPRING." Universidade Estadual Paulista (UNESP), 2005. http://hdl.handle.net/11449/92785.

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Made available in DSpace on 2014-06-11T19:26:11Z (GMT). No. of bitstreams: 0 Previous issue date: 2005-11-11Bitstream added on 2014-06-13T19:13:07Z : No. of bitstreams: 1 costa_alc_me_rcla.pdf: 2147425 bytes, checksum: 989559e2911176da648ab906f9e027e0 (MD5)<br>A expansão territorial do agronegócio e da área urbana na Alta Bacia Hidrográfica do Rio Jacaré Pepira impacta a preservação de seus recursos naturais. A erosão se destaca como um processo do meio físico que possui uma relação estreita com o uso do solo. Para estudo da vulnerabilidade à erosão realizou-se uma análise segmentada dos fatores naturais e antrópicos, apoiando-se no modelo matemático da Equação Universal de Perda de Solo (EUPS). O meio físico foi compartimentado nas sub-bacias que estão parcial ou totalmente inseridas na Área de Proteção Ambiental (APA) de Corumbataí. As sub-bacias foram consideradas como unidades de análise, com a medição de parâmetros morfométricos. Para estudo dos fatores antrópicos, realizou-se levantamentos de campo para caracterização do uso e manejo do solo praticado nas principais explorações agropecuárias com a identificação de feições erosivas lineares. As informações de ocupação do solo foram extraídas da classificação automática de imagens de satélite dos anos de 1988 (TM/Landsat) e 2004 (CBERS) com controle de campo. O trabalho apresentou como resultado o zoneamento da área quanto à susceptibilidade natural, vulnerabilidade e adequação ao uso do solo. As informações integradas em um banco de dados em SIG/SPRING permitem fornecer instrumentos para trabalhos de gerenciamento ambiental.<br>Territorial expansion of agrobusiness and urban areas in the Jacaré Pepira River watershed impact its natural resources. Erosion stands out as a physical environment process that has a close relationship with land use. The study of erosion vulnerability was accomplished on a natural and human factors segmented analysis, based on Universal Soil Loss Equation (USLE) model. Physical environmental was shared on the sub-basin belong to APA de Corumbataí (Corumbataí Environmental Protection Area). Sub-basins were considered as units of analysis, witch the morfometric parameters measurements. Field work was used on human factor study, aiming the management characterization for main crops, including the identify of areas affected by gullies. Data about land use were obtained by automatic classification of 1988 (TM/Landsat) and 2004 (CBERS) satellite images with field control. Zoning of the area was accomplished based on natural erosion potential (PNE), erosion vulnerability and wishing land use. Data input to a GIS/SPRING database can provide tools for environmental management.
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Books on the topic "Universal Soil Loss Equation"

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Kiggundu, Lawrence. Distribution of rainfall erosivity in Swaziland: For use in the universal soil loss equation (USLE) and the soil loss estimator for southern Africa (SLEMSA) to estimate soil loss due to sheet and rill erosion. Social Science Research Unit and Research and Publications, University of Swaziland, 1986.

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Kesteren, A. R. Van. Universal soil loss equation (USLE) soil erodibility (K) factors for some common forest types of western Newfoundland. Canadian Forest Service, Newfoundland and Labrador Region, 1994.

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Thomas, A. W. Computer program for stochastic utilization of the USLE. Southern Piedmont Conservation Research Center, Agricultural Research Service, USDA, 1989.

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G, Renard K., Smith D. D, Wischmeier W. H, and United States. Department of Agriculture. Agricultural Research Service., eds. Predicting soil erosion by water: A guide to conservation planning with the Revised Universal Soil Loss Equation (RUSLE). United States Department of Agriculture [for sale by the U.S. Government printing Office, 1997.

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Laurens J. P. Van Vliet. Water erosion prediction for soils in the Peace River Region of British Columbia: Estimates using the universal soil loss equation. Research Branch, Agriculture Canada, 1989.

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Marx, Johannes. Die Erodierbarkeit charakteristischer Böden im Südosten der VR China. In Kommission bei Duncker & Humblot, 1988.

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Programme zur Erfassung von Landschaftsdaten, eine Bodenerosionsgleichung und ein Modell der Kaltluftentstehung =: Programmes for the collection of landdscape data, a soil erosion equation and a model showing how cold air arises. Im Selbstverlag des Geographischen Institutes der Universität Heidelberg, 1986.

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Blaszczynski, Jacek S. Watershed soil erosion, runoff, and sediment yield prediction using geographic information systems: A manual of GIS procedures. U.S. Dept. of the Interior, Bureau of Land Management, BLM Service Center, 1994.

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Kasran, Baharuddin. A guide for estimating surface soil loss using the modified soil loss equation (MSLE) on forest land. Forest Research Institute Malaysia, 1999.

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U S Department of Agriculture Soil C. Predicting Soil Loss Using the Universal Soil Loss Equation. Creative Media Partners, LLC, 2022.

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Book chapters on the topic "Universal Soil Loss Equation"

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Renard, K. G., D. C. Yoder, D. T. Lightle, and S. M. Dabney. "Universal Soil Loss Equation and Revised Universal Soil Loss Equation." In Handbook of Erosion Modelling. John Wiley & Sons, Ltd, 2011. http://dx.doi.org/10.1002/9781444328455.ch8.

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Browning, George M. "Development for and of the Universal Soil Loss Equation." In Universal Soil Loss Equation. Soil Science Society of America, 2015. http://dx.doi.org/10.2136/sssaspecpub8.c1.

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Robinson, A. R. "Sediment Yield as a Function of Upstream Erosion." In Universal Soil Loss Equation. Soil Science Society of America, 2015. http://dx.doi.org/10.2136/sssaspecpub8.c2.

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Foster, G. R. "Sediment Yield from Farm Fields: The Universal Soil Loss Equation and Onfarm 208 Plan Implementation." In Universal Soil Loss Equation. Soil Science Society of America, 2015. http://dx.doi.org/10.2136/sssaspecpub8.c3.

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Onstad, C. A., R. A. Young, M. A. Otterby, and R. F. Holt. "Sediment Yield Modeling for 208 Planning." In Universal Soil Loss Equation. Soil Science Society of America, 2015. http://dx.doi.org/10.2136/sssaspecpub8.c4.

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Moldenhauer, W. C. "Erosion Control Obtainable under Conservation Practices." In Universal Soil Loss Equation. Soil Science Society of America, 2015. http://dx.doi.org/10.2136/sssaspecpub8.c5.

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Holt, R. F., D. R. Timmons, and R. E. Burwell. "Water Quality Obtainable under Conservation Practices." In Universal Soil Loss Equation. Soil Science Society of America, 2015. http://dx.doi.org/10.2136/sssaspecpub8.c6.

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LaRocque, Armand. "Universal Soil Loss Equation (USLE)." In Encyclopedia of Natural Hazards. Springer Netherlands, 2013. http://dx.doi.org/10.1007/978-1-4020-4399-4_43.

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Thapa, Pawan. "Soil Erosion Estimation Using Revised Universal Soil Loss Equation (RUSLE) Model and GIS." In GIScience for the Sustainable Management of Water Resources. Apple Academic Press, 2022. http://dx.doi.org/10.1201/9781003284512-16.

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Box, J. E., and L. D. Meyer. "Adjustment of the Universal Soil Loss Equation for Cropland Soils Containing Coarse Fragments." In Erosion and Productivity of Soils Containing Rock Fragments. Soil Science Society of America, 2015. http://dx.doi.org/10.2136/sssaspecpub13.c9.

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Conference papers on the topic "Universal Soil Loss Equation"

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Shellenberger, Kim, Nicole Wagner, and Nicole Wagner. "UNIVERSAL SOIL LOSS EQUATION AND ARCGIS APPLICATIONS IN EROSION OF ARSENIC CONTAMINATED SOIL." In Joint 69th Annual Southeastern / 55th Annual Northeastern GSA Section Meeting - 2020. Geological Society of America, 2020. http://dx.doi.org/10.1130/abs/2020se-344216.

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Park, Soyoung, Cheunggil Jin, and Chuluong Choi. "Predicting soil erosion under land-cover area and climate changes using the revised universal soil loss equation." In SPIE Remote Sensing, edited by Christopher M. U. Neale and Antonino Maltese. SPIE, 2011. http://dx.doi.org/10.1117/12.896325.

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BRIBIESCA RODRIGUEZ, MIGUEL ANGEL, ORGE IVAN JUAREZ DEHESA, FERNANDO J. GONZALEZ VILLARREAL, and GABRIELA GUTIÉRREZ AVIÑA. "SEDIMENT LOAD CALCULATION BY USING THE UNIVERSAL SOIL LOSS EQUATION WITH A GEOGRAPHIC INFORMATION SOFTWARE." In 38th IAHR World Congress. The International Association for Hydro-Environment Engineering and Research (IAHR), 2019. http://dx.doi.org/10.3850/38wc092019-0300.

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Li, Hui, Huizhong He, Xiaoling Chen, and Lihua Zhang. "An approach to compute the C factor for universal soil loss equation using EOS-MODIS vegetation index (VI)." In International Conference on Earth Observation Data Processing and Analysis, edited by Deren Li, Jianya Gong, and Huayi Wu. SPIE, 2008. http://dx.doi.org/10.1117/12.815335.

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Kopecký, Marek, Jaroslav Bernas, Ladislav Kolář, and Pavlína Hloucalová. "MONITORING OF ENERGY GAIN AND EROSION PROTECTION OF CORN AND TALL WHEATGRASS CROPS IN THE CONDITIONS OF THE CZECH REPUBLIC." In RURAL DEVELOPMENT. Aleksandras Stulginskis University, 2018. http://dx.doi.org/10.15544/rd.2017.084.

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With the growing energy demand of the society and the increased requirements for ecological aspects of obtaining and utilizing energies, renewable energy sources have been getting to the forefront. In the conditions of Central Europe, transformation of biomass to biogas through anaerobic digestion appears to be promising. The article describes the results of a field experiment carried out in an experimental site of the University of South Bohemia in České Budějovice (South Bohemia, Czech Republic). The goal of the article is to compare the conventionally grown corn (Zea mays L., hybrid Simao), the areas of which have increased considerably as a result of the development of biogas stations, and the alternative perennial grass called tall wheatgrass (Elymus elongatus subsp. ponticus cv. Szarvasi-1), which is, according to the literature, well positioned to replace corn. The harvests of the plants took place in 2013-2015, and tall wheatgrass was cut twice per season. A number of aspects – dry phytomass yield, specific methane yield and hectare methane yield – were monitored. In addition, the long-term soil loss by water erosion was calculated through the Universal Soil Loss Equation for both species of energy crops. In terms of yield parameters and methane production, better results were achieved by corn, given the average energy gain 238 GJ·ha-1 as compared to 126 GJ·ha-1 for tall wheatgrass. The protection of the soil surface from water erosion by corn appears to be insufficient and, in this criterion, it absolutely lags behind the anti-erosion abilities of tall wheatgrass, which protects soil incomparably better.
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Chia-Chun Wu and Tsung-Wen Wang. "Feasibility of Irregular-slope Equation on Soil Loss Prediction for Grass Strips." In International Symposium on Erosion and Landscape Evolution (ISELE), 18-21 September 2011, Anchorage, Alaska. American Society of Agricultural and Biological Engineers, 2011. http://dx.doi.org/10.13031/2013.39259.

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Badulescu, Bianca. "ESTIMATION AND MODELLING OF UNCERTAINTY PROPAGATION IN SOIL LOSS ASSESSMENT USING RUSLE EQUATION." In 19th SGEM International Multidisciplinary Scientific GeoConference EXPO Proceedings. STEF92 Technology, 2019. http://dx.doi.org/10.5593/sgem2019/2.2/s11.099.

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Son, S. I., and K. W. Kim. "The Effect of Micro-Grooves on Hydrodynamic Lubrication Characteristics of a Piston Ring and a Cylinder Liner." In ASME/STLE 2011 International Joint Tribology Conference. ASMEDC, 2011. http://dx.doi.org/10.1115/ijtc2011-61118.

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In this study, the effect of micro-grooves on hydrodynamic fabrication characteristics between a piston ring and a micro-grooved cylinder liner is analyzed numerically. Elrod’s universal equation satisfying JFO theory is adopted to predict the cavitation region properly and calculate the pressure distribution between a piston ring and a micro-grooved cylinder liner. The analysis is carried out by varying the shape, depth, length, width and location of micro-grooves during the full engine cycle. The results show that micro-grooves can make friction loss decrease in comparison with a non-textured cylinder liner.
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Rodrigo-Comino, Jesús, Enric Terol, and Artemi Cerdà. "IMPROVED STOCK UNEARTHING METHOD (ISUM) ALLOW TO ASSESS SOIL EROSION PROCESSES IN GRAFTED PLANTS USING IN SITU TOPOGRAPHICAL MEASUREMENTS." In 3rd Congress in Geomatics Engineering. Universitat Politècnica de València, 2021. http://dx.doi.org/10.4995/cigeo2021.2021.13256.

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Policymakers, stakeholders and rural inhabitants must be aware of the relevance of soil erosion as an irreversible landdegradation process. This is key to achieve the land degradation neutrality challenge and the sustainability of humankindand natural ecosystems. Agricultural areas are being affected by soil erosion threatening soil quality and, subsequently,food security. Therefore, it is necessary to develop new techniques and methods visually friendly and easy to be accessedto survey and assess the soil erosion concerns. ISUM (Improve Stock Unearthing Method) is a well-contrasted procedureto estimate and map soil mobilisation and erosion rates. To achieve this goal, using the plant graft union as a biomarkerconducting in situ topographical measurements along perpendicular transects allow us to i) explain key factors related tothe activation of soil erosion processes such as tillage, the age of plantation, parent material or hillslope positions; ii)complete other well-contrasted methods such as RUSLE (Revised Soil Loss Equation), IC (Index of connectivity) orStructure from Motion; and, iii) identify hotspot areas affected by soil depletion, accumulation or mobilisation. In thisconference, we will show how we developed a new improvement of this method in different crops (vineyards, citrus,persimmons or almonds), under different environmental conditions (parent material, vine ages, soil management, or slopeangle) with diverse geomatic procedures (interpolation methods and geostatistical analysis, topographical measurementsand models) using GIS techniques.
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Filimonov, Mikhail Yu, and Nataliia A. Vaganova. "Simulation of Thermal Fields in the Permafrost With Seasonal Cooling Devices." In 2012 9th International Pipeline Conference. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/ipc2012-90287.

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A new mathematical model of heat distribution in permafrost soils is considered taking into account different climatic and physical factors. The first group of factors includes consideration of solar radiation, seasonal changes of air temperature, leading to periodic thawing (freezing) of soil, and possible snow layers. The second group of factors is the heterogeneity of the soil, the presence of a number of piles, or foundation structures, seasonal cooling devices. Seasonal cooling devices are vapor-fluid devices consisting of a hermetically sealed and seasoned with coolant, metal pipe with diameter 57 mm, length up to 10 meters or more, consisting of aerial parts (condenser fins) up to 2.5 meters and an underground part. These devices operate without external power sources only by the laws of physics. Taking into account these factors leads to solution of three-dimensional quasilinear heat distribution equation (quasi-linear equation due to the dependence of the thermophysical parameters on temperature) of the Stefan problem in a rectangular parallelepiped, but also with a nonlinear boundary condition at the soil surface associated with solar radiation. It is assumed that the lateral faces of the computational domain are insulated and are chosen sufficiently far from the location of engineering structures, and a computational grid of large dimension to be used, with adaptation to the heat (cold) sources. Software product is designed for numerical simulation of thermal fields in permafrost and melted soil, taking into account thermal diffusion properties of the soil and heat exchange between the soil and air, including also due to heat loss by radiation. The paper is devoted to the results of numerical simulations carried out for the project work in several oil and gas fields in Russia, located in the permafrost zone.
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