Geography
Universal Soil Loss Equation (USLE)
The Universal Soil Loss Equation (USLE) is a widely used model for predicting soil erosion. It takes into account factors such as rainfall, soil erodibility, slope length, and land cover to estimate the amount of soil that is likely to be lost due to erosion. By considering these variables, the USLE helps in assessing and managing soil erosion in different geographic areas.
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10 Key excerpts on "Universal Soil Loss Equation (USLE)"
eBook - ePubLand And Soil Management
Technology, Economics, And Institutions
- Alfredo Sfeir-Younis(Author)
- 2019(Publication Date)
- CRC Press(Publisher)
As with C, it is a ratio comparing soil loss at a particular site to soil loss under a set standard. Values of P have to be experimentally determined. The USLE is universal only insofar as the principles by which it operates are universal. A distinction must be made between the equation and the values entered into the equation. Merely to transfer the values derived in the United States to other climates, topographies and farming conditions would be a grave error. Furthermore, the USLE is applicable only to sheet and rill erosion. That is, it is not applicable to gully erosion. Modifications of the USLE have been proposed to predict wind erosion and erosion under other environmental conditions, but it should be recognized that these modifications have not been fully evaluated. The USLE was designed for the following uses, (1) to predict annual soil losses from fields (it was not designed to predict soil losses in watersheds, building sites, regions, countries and the like), (2) to guide the selection of conservation practices for specific sites, (3) to estimate the reduction in soil loss attainable from various changes that a farmer might make in his cropping system or cultivation practices, (4) to determine how much more intensively a given field could be safely cropped if it were contoured, terraced, or strip-cropped, (5) to assess the design and application of other conservation measures on farmers' fields. Limitations of the USLE Three types of criticism of the USLE have been voiced: technical, practical and economic. 1 From a technical viewpoint, the USLE has the following weaknesses. First, it was designed only to predict soil loss from sheet and rill erosion and therefore underestimates erosion of other kinds. Second, not all sediments leave a particular field and the equation was not designed to predict sediment yields outside the field. Third, it only predicts average soil losses and more work needs to be done to estimate loss "variability"
eBook - PDFWater Resources
Planning, Development and Management
- Ralph Wurbs(Author)
- 2013(Publication Date)
- IntechOpen(Publisher)
2.1. Governing equations The USLE was developed in 1965 by the Agricultural Research Service (ARS) scientists Wischmeier and Smith to predict long time average soil losses in run-off from specific field areas in specified cropping and management systems (Wischmeier & Smith, 1978). The USLE disaggregates the erosion process into 6 factors that were each determined based on the analyses of more than 11 000 plot-years of research data from 47 locations in 24 states in the United States. Notwithstanding its initial north American focus, this approach or its re‐ vised successor (RUSLE, see Renard et al., 1991) has been applied in many studies around the world including a particularly interesting study that estimates sediment yield in the past 6000 years in the Meuse catchment area (Ward et al., 2009). The basic equation follows: A R x K x LS x C x P = (1) Where: A = Mean annual soil loss (t ha -1 yr -1 ) R = Rainfall and runoff erosivity index (J mm.m -2 h -1 ) K = Soil erodibility factor (t J -1 mm -1 ) LS = Slope and length of slope factor C = Cropping – Management factor P = Erosion control factor practice The SLEMSA model was developed by Elwell (1977) in Zimbabwe to estimate the long-term mean annual soil loss from sheet erosion on arable land (Bonda et al., 1999). SLEMSA was developed on the basis of the USLE and is an attempt to adapt the USLE model to an African environment. It is a relatively widely used soil loss model in Afri‐ can environments (Elwell & Stocking, 1982), and should be seen as a modelling techni‐ que or framework, rather than mechanistic descriptions of the erosion system (Smith, 1999). The SLEMSA model divides the soil erosion environment into four physical sys‐ tems: crop, climate, soil and topography.
eBook - ePubGeological Hazards
Their Assessment, Avoidance and Mitigation
- Fred G. Bell(Author)
- 1999(Publication Date)
- CRC Press(Publisher)
The example is for a 10-square-mile basin–follow the arrow on drainage area, to percentage of cultivated land, to run-off and then to sediment yield (after Brune, 1951). The bedload transport is generally determined separately. Bedload may vary from zero to nearly all of the total load, depending on the sediment sources and transport capability of the stream. Bedload transport dominates when the ratio between the lifting forces and the stabilizing forces on a particle is less than 0.2, that is, when particles are large in relation to the force of flow. 9.4 The Universal Soil Loss Equation As noted above, the most frequently used method to determine the soil loss is the Universal Soil Loss Equation (USLE) developed by the United States Soil Conservation Service: A = R K L S C P (9.1) where A is the average annual loss of soil, R is rainfall erosivity, K is the soil erodibility factor, L is the slope length factor, S is the slope gradient factor, C is the cropping management factor and P is the erosion control practice factor. The method was developed as a means of predicting the average annual soil loss from inter-rill and rill erosion. However, the equation is not universally applicable. Its primary purpose is to predict losses from arable or non- agricultural land by sheet and rill erosion and to provide guides for the selection of adequate erosion control practices (Wischmeier and Smith, 1978; Mitchell and Bubenzer, 1980). As such, the USLE can be used to determine how conservation practices can be applied or altered to permit more intensive cultivation, to predict the change in soil loss that results from a change in cropping or conservation practices, and to provide soil loss estimates for conservationists to use for determining conservation needs
eBook - ePubGIS Applications in Agriculture, Volume Four
Conservation Planning
- Tom Mueller, Gretchen F. Sassenrath, Tom Mueller, Gretchen F. Sassenrath(Authors)
- 2015(Publication Date)
- CRC Press(Publisher)
Predicting Soil Erosion by Water: A Guide to Conservation Planning with the Revised Universal Soil Loss Equation (RUSLE). Agric. Handbook 703, Washington, DC: U.S. Department of Agriculture-Agricultural Research Service.- Renard, K.G., D.C. Yoder, D.T. Lightle, and S.M. Dabney. 2011 . Universal soil loss equation and revised universal soil loss equation. In Morgan, R.P.C. and M.A. Nearing (eds.), Handbook of Erosion Modeling , pp. 137–167. Oxford, England: Blackwell Publishing Ltd.
- Tomer, M.D., T.B. Moorman, J.L. Kovar, D.E. James, and M.R. Burkhart. 2007 . Spatial patterns of sediment and phosphorus in a riparian buffer in Western Iowa. Journal of Soil and Water Conservation 62:329–338.
- USDA-ARS. 2012 . Revised Universal Soil Loss Equation 2—Changes in 2010. Available at http://www.ars.usda.gov/Research/docs.htm?docid=20222 (accessed January 10, 2014).
- USDA-ARS. 2013 . Science documentation. Revised Universal Soil Loss Equation, Version 2 (RUSLE2). Available at http://www.ars.usda.gov/sp2UserFiles/Place/64080510/RUSLE/RUSLE2_Science_Doc.pdf (accessed June 06, 2014).
- Van Oost, K., T.A. Quine, G. Govers, S. De Gryze, J. Six, J.W. Harden, J.C. Ritchie, G.W. McCarty, G. Heckrath, C. Kosmas, J.V. Giraldez, J.R. Marques da Silva, and R. Merckx. 2007 . The impact of agricultural soil erosion on the global carbon cycle. Science 318:626–629.
- Vieira, D.A.N., and S.M. Dabney. 2009 . Modeling landscape evolution due to tillage: Model development. Transactions of the ASABE 52:1505–1522.
- Vieira, D.A.N., and S.M. Dabney. 2012 . Two-dimensional flow patterns near contour grass hedges. Hydrological Processes
eBook - ePub- Andy D. Ward, Stanley W. Trimble, Suzette R. Burckhard, John G. Lyon(Authors)
- 2015(Publication Date)
- CRC Press(Publisher)
http://websoilsurvey.sc.egov.usda.gov/App/HomePage.htm . In some cases, criteria for control of sediment pollution may dictate lower tolerance values. In other cases, it may be necessary to allow for higher levels of erosion to ensure the economic viability of a rural community.9.5 UNIVERSAL SOIL LOSS EQUATION
The USLE continues to be a widely accepted method of estimating sediment loss despite its simplification of the many variables involved in soil loss prediction. It is useful for determining the adequacy of conservation measures in resource planning and for predicting nonpoint sediment losses in pollution control programs. The average annual soil loss, as determined by Wischmeier and Smith (1978) , can be estimated from the equation:A = R×K×LS×C×P (9.1)where A is the average annual soil loss in tons/acre, R is the rainfall and runoff erosivity index for a geographic location (hundreds.foot.ton-force.inch/acre.hour.year), K is the soil erodibility factor (ton.acre.hour/hundreds of acre.foot.ton-force.inch), LS is the slope steepness and length factor, C is the cover management factor, and P is the conservation practice factor. In this text, English units are used for A, R, and K and because of the awkwardness, the units of R and K are not usually reported. If using other sources of information for the USLE, ensure that units are consistent (Foster et al. 1981 ). Although developed for use in the United States, the procedure is used in many countries and has been the focus of considerable study during the past 40 years. Moreover, the equation is now also used for nonagricultural areas such as forests, urban development, and spoil banks from surface mining. Methods for determining each of the input parameters for the USLE and examples of their use follow. Note, however, that the USLE actually estimates the amount of soil moved on a field (termed gross erosion) and not necessarily the amount moved from
eBook - PDFHandbook of Engineering Hydrology
Modeling, Climate Change, and Variability
- Saeid Eslamian(Author)
- 2014(Publication Date)
- CRC Press(Publisher)
The main factors contributing to upland erosion losses include rainfall erosivity, soil erodibility, land topography, land use, and land conservation practices [9]. Specific degradation rates in reservoirs of the United States are typically less than 2000 tons/km 2 /year [18] and are primarily linked to upland erosion rates. Upland erosion losses have been estimated using well-known methods like the Universal Soil Loss Equation (USLE) from Wischmeier and Smith [34]. The USLE includes all the factors affecting upland erosion from sheet and rill erosion. Renard et al. [25] provided a modified version named the Revised Universal Soil Loss Equation (RUSLE). The advances in geographic information system (GIS) have allowed applications of raster-based for-mats for the determination of the various parameters of the USLE and RUSLE. Some detailed applica-tions at the watershed scale include Mitasova et al. [20], Molnar and Julien [21], and Kim and Julien [19]. The applicability in tropical areas represents a challenge because of the reduced availability in GIS-gridded information for topography, soil type, and land use, as well as for the evaluation of the rainfall erosivity parameter R. In tropical regions, the early and widely accepted soil erosion models consist of relatively simple response functions to predict mean annual erosion losses. Forest Research Institute Malaysia (FRIM) [11] provided a guide for soil erosion losses on Malaysian forestland using MUSLE. Schoorl et al. [28] state that the current trend is towards replacing these by far more elaborated process-based models. Among these models include water prediction program (WEPP) of the USDA; the erosion productiv-ity impact calculator (EPIC); chemical, runoff, and erosion from agricultural management systems (CREAMS); and European soil erosion model (EUROSEM). These models are usually event based and are more applicable to agricultural areas than mountainous watersheds.
eBook - PDFGIS Applications in Agriculture, Volume Four
Conservation Planning
- Tom Mueller, Gretchen F. Sassenrath, Tom Mueller, Gretchen F. Sassenrath(Authors)
- 2015(Publication Date)
- CRC Press(Publisher)
Computer Vision, Graphics and Image Processing 28:328–344. Rachman, A., S.H. Anderson, E.E. Alberts, A.L. Thompson, and C.J. Gantzer. 2008. Predicting runoff and sediment yield from a stiff-stemmed grass hedge system for a small water-shed. Transactions of the ASABE 51:425–432. Renard, K.G., G.R. Foster, G.A. Weesies, D.K. McCool, and D.C. Yoder. 1997. Predicting Soil Erosion by Water: A Guide to Conservation Planning with the Revised Universal Soil Loss Equation (RUSLE). Agric. Handbook 703, Washington, DC: U.S. Department of Agriculture–Agricultural Research Service. Renard, K.G., D.C. Yoder, D.T. Lightle, and S.M. Dabney. 2011. Universal soil loss equation and revised universal soil loss equation. In Morgan, R.P.C. and M.A. Nearing (eds.), Handbook of Erosion Modeling , pp. 137–167. Oxford, England: Blackwell Publishing Ltd. Tomer, M.D., T.B. Moorman, J.L. Kovar, D.E. James, and M.R. Burkhart. 2007. Spatial pat-terns of sediment and phosphorus in a riparian buffer in Western Iowa. Journal of Soil and Water Conservation 62:329–338. USDA-ARS. 2012. Revised Universal Soil Loss Equation 2—Changes in 2010. Available at http://www.ars.usda.gov/Research/docs.htm?docid=20222 (accessed January 10, 2014). USDA-ARS. 2013. Science documentation. Revised Universal Soil Loss Equation, Version 2 (RUSLE2). Available at http://www.ars.usda.gov/sp2UserFiles/Place/64080510/RUSLE /RUSLE2_Science_Doc.pdf (accessed June 06, 2014). 83 Erosion Modeling in 2D with RUSLE2 Van Oost, K., T.A. Quine, G. Govers, S. De Gryze, J. Six, J.W. Harden, J.C. Ritchie, G.W. McCarty, G. Heckrath, C. Kosmas, J.V. Giraldez, J.R. Marques da Silva, and R. Merckx. 2007. The impact of agricultural soil erosion on the global carbon cycle. Science 318:626–629. Vieira, D.A.N., and S.M. Dabney. 2009. Modeling landscape evolution due to tillage: Model development. Transactions of the ASABE 52:1505–1522. Vieira, D.A.N., and S.M. Dabney. 2012. Two-dimensional flow patterns near contour grass hedges.
- Maria C. Hernandez Soriano(Author)
- 2013(Publication Date)
- IntechOpen(Publisher)
No approach adequately supplies spatially distributed information on the erosion necessary for effective control of the erosion and sediments. Thus, on a hydrographic basin or landscape scale, the spatial distribution of the soil erosion predicted by such models will distort the current conditions and will tend to overestimate the erosion [47, 48]. Some studies mention overestimates in lower soil losses and underestimates for the high losses using the USLE and RUSLE models [44, 49, 50]. As a solution, studies recommend to first identify those portions of the landscape subject to the deposition and to exclude them from the analysis when applying the USLE and RUSLE models [9]. USLE was related to GIS due to the advantages of handling great amounts of spatial data. Until the middle of the 1990’s, a great limitation in the use of the USLE and RUSLE erosion models on a regional landscape scale was the difficulty in estimating appropriate LS factor values for applications in GIS [7], since such models evaluate the effects of the topography on the erosion in a two-dimensional way. In that context, the use of models distributed in space came to represent a powerful environmental analysis tool, highlighting soil erosion by water on the hydrographic basin scale. Soil Processes and Current Trends in Quality Assessment 118 3.2. Conceptual modeling The conceptual methods incorporate the impact of different erosive processes through empirical parameters [51] usually obtained through calibration with observed data, such as flow discharge and sediment concentration [52]. Therefore, these models represent the processes within the scale in which they were simulated [53].
eBook - ePubFundamentals of Plan Making
Methods and Techniques
- Edward J. Jepson, Jr., Jerry Weitz(Authors)
- 2015(Publication Date)
- Routledge(Publisher)
http://fargo.nserl.purdue.edu/rusle2_dataweb/RUSLE2_Index.htm ) to predict the amount of runoff from development. Standing for Revised Universal Soil Loss Equation, RUSLE2 is the current version of a method first developed in the 1960s.While RUSLE2 is “user-friendly,” it also requires significant specialized knowledge to establish the various parameters for estimating runoff. The original RUSLE method can be used by planners to generate an initial erodibility index that can then be confirmed through application of RUSLE2 and/or additional on-site analysis (see Box 6.5 for an explanation of the RUSLE methodology).Table 6.4The procedure for identifying a site as an AOI within a county consists of the following steps:Box 6.4Identifying A Site Within A County For Soil Analysis- Step 1 Access the website at http://websoilsurvey.nrcs.usda.gov/app/HomePage.htm .
- Step 2 Click on the “Start WSS” button.
- Step 3 Click on “State and County” under “Quick Navigation.”
- Step 4 Select your state and county from the drop-down menus and click on the “View” box.
- Step 5 There are several ways to identify your site:
- Type in its address. A map will be generated that shows the address. Click on the “AOI” box above the map that contains a red triangle. Place your cursor near the location marker and click and drag to establish an AOI. Or :
- Enter its longitude–latitude. A map will be generated that shows the location marker. Follow the same procedure for an address.* Or :
- Click on the “AOI” box above the county map that contains a red rectangle. Place your cursor near the site and click and drag.
- Step 6 Click on the “Soil Map” tab to generate a map that shows the location of soils in the AOI and a list and the acreage of the soils contained within the AOI.
* The latitude and longitude of a site can be determined at http://itouchmap.com/latlong.html .
eBook - PDF- R. P. C. Morgan(Author)
- 2009(Publication Date)
- Wiley-Blackwell(Publisher)
Thus, good information on rainfall and soils is required for suc-cessful prediction. An example of the use of the model is presented in Table 6.12. Like the Universal Soil Loss Equation, the model cannot be used to predict soil loss from indi-vidual storms or from gully erosion. The model has been used to predict soil loss in Indonesia Table 6.8 Input parameters to the Morgan–Morgan–Finney method of predicting soil loss Factor Parameter Definition Rainfall R Annual or mean annual rainfall (mm) R n Number of rain days per year I Typical value for intensity of erosive rain (mm h -1 ); use 10 for temperate climates, 25 for tropical climates and 30 for strongly seasonal climates (e.g. Mediterranean type or monsoon) Soil MS Soil moisture content at field capacity or 1/3 bar tension (wt %) BD Bulk density of the top soil layer (Mg m -3 ) EHD Effective hydrological depth of soil (m); value depends on vegetation/crop cover, presence or absence of surface crust, presence of impermeable layer within 0.15 m of the surface K Soil detachability index (gJ -1 ) defined as the weight of soil detached from the soil mass per unit of rainfall energy COH Cohesion of the surface soil (kPa) as measured with a torvane under saturated conditions SD Total soil depth (m) defined as the depth of soil surface to bedrock W Rate of increase in soil depth by weathering at the rock–soil interface (mm yr -1 ) V Rate of increase in effective hydrological layer (mm yr -1 ) as a result of crop management practices and the natural breakdown of vegetative matter into humus Landform S Slope steepness (°) Land cover A Proportion (between 0 and 1) of the rainfall intercepted by the vegetation or crop cover E t / E o Ratio of actual ( E t ) to potential ( E o ) evapotranspiration C Crop cover management factor; combines the C and P factors of the Universal Soil Loss Equation CC Percentage canopy cover, expressed as a proportion between 0 and 1 GC Percentage ground cover, expressed as a proportion between 0 and 1 PH Plant height (m), representing the height from which raindrops fall from the crop or vegetation cover to the ground surface Time N Number of consecutive years for which
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