# *Regional sheet and rill soil erosion prediction with the Revised Universal Soil Loss Equation (RUSLE) -- GIS Interface*

*by*

*Jacek Blaszczynski*

*Physical Scientist*

*BLM National Science and Technology Center (NSTC)*

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**Introduction**

An ecological issue of great importance to the United States and the
world is degradation of rangelands, the major symptom of which is
increase in the rate of water caused soil erosion. Livestock grazing and
drought can combine to reduce the grass cover, compact the soil
increasing runoff and erosion, and create a patchy distribution of of
water and nutrients. If during periods of moderate drought grazing is
intensified this can lead to decrease in grass cover that normally
supports a uniform distribution of moisture and nutrients throughout the
soil profile. Bare patches, which may have developed during the drought,
become more pronounced. Topsoil is compacted by trampling causing less
rainfall to infiltrate the topsoil leading to greater surface runoff and
related sheet and rill erosion. The problem of erosion is further
exacerbated by loss of organic matter in the topsoil that tends to hold
the soil particles together. If overgrazing and drought continue the
bare patches expand, and erosion becomes a significant problem as rain
and nutrients are carried away from them to be deposited in nearby
surface depressions. Creosote and mesquite shrubs begin to appear in
these areas of concentration of soil, water, and nutrients. As soil
around the grasses loses its depth, nutrients, and moisture through
erosion and leaching, it can no longer sustain the grasses limiting
their ability to regenerate. To avoid this downward spiral from which
there might not be a recovery, management of rangeland soil resources
has to be performed in a way that does not deplete the resource and
result in loss of soil productivity. Technologies to estimate, manage
and contain soil erosion are very much needed to help prescribe
management practices so as to maintain existing rangelands in an
ecologically healthy state and to return degraded rangelands to a more
pristine condition.

For most of the world\'s regions accurate estimates of soil erosion are
not available. An examination by experts of the available figures
including a review carried out for the United Nations Food and
Agriculture Organization concluded that these estimates have little
scientific merit. The overwhelming share of land reported to the United
Nations Environment Program (UNEP) as under danger from soil erosion and
related desertification processes is rangeland. If global climate
warming proves to be actual and leads to increase in mean surface
temperature, the rates of desertification in various regions can sharply
increase. Since a significant portion of the public lands managed by the
Bureau of Land Management (BLM) is rangeland, the Bureau has a vested
interest in issues related to rangeland soil erosion. Development and
application of a soil erosion prediction methodology is therefore of
utmost importance. The Revised Universal Soil Loss Equation
(RUSLE)/Geographic Information System interface provides such
methodology for regional level analysis of soil erosion. While it is
likely that application of more complex models than RUSLE can provide
more accurate estimates of soil erosion for specific sites, the data
requirements of these models make them difficult to use on a regional
scale. By comparison, data for utilization of RUSLE can be relatively
easy to obtain.

**Methodology**

The RUSLE/GIS methodology permits calculation of potential soil loss
from sheet and rill erosion for rangelands. The RUSLE equation
calculates potential erosion **(A)** as follows:

# A = R \* K \* L \* S \* C \* P

Where **R** is the rainfall and runoff factor; **K** is the soil
erodibility factor; **L** is the slope-length factor; **S** is the
slope-steepness factor; **C** is the cover and management factor; and
**P** is the erosion control practice factor. In the modeling procedure
the values for the different RUSLE factors are assigned to their
respective mapping units, i.e. a soil unit map, a vegetation class map,
and a digital elevation model.

Values for the factors can be obtained from reference sources or
calculated from mapped or field collected data about the project
location. To facilitate calculation of RUSLE factors the user can input
relevant information into a PC program, such as the RUSLE program
prepared by BLM in 1987 upon which this interface is based. The rainfall
and runoff **R** factor value is calculated from latitude and longitude
and 2yr-6hr precipitation of the study area. The soil erodibility **K**
factor values are obtained from soil surveys, or calculated from field
information that includes percentage of silt and very fine sand in the
soil, percentage of clay, percentage of organic matter, soil structure
and permeability code. The slope gradient **S** and slope length **L**
factors are calculated from digital elevation data using GIS hydrologic
terrain analysis capabilities and map algebra. The **C** factor values
are calculated using field survey information on percent and average
height of vegetation cover, the root mass in the top 4 inches of soil,
percent of rock, gravel, litter and vegetation on the ground surface. In
calculation of the **C** factor the RUSLE program also provide a table
of surface roughness values from which one can pick the appropriate
roughness number. The erosion control **P** factor values are obtained
from a table provided by the designers of the RUSLE. After the factor
values have been assigned or calculated for each of the mapping units,
the factor maps are overlaid to produce a visualization of soil erosion
potential.

There has been a lot of controversy over the accuracy of the Universal
Soil Loss Equation and its close cousin the Revised Universal Soil Loss
Equation. However even if we regard the resultant soil-erosion potential
map purely as a relative evaluation based on known interactions and
influence of rainfall, soil cohesion, terrain, vegetation cover, and
erosion control practices such a map still provides us with useful
information. A preliminary run of the procedure based on existing data
should provide us with a rapid reconnaissance level evaluation as to
where we can expect low, medium, high, and very high erosion rates based
on these environmental parameters expressed by the factors. Although the
RUSLE model oversimplifies the complexity of erosion processes it's
estimates can be improved to by refining the quality of input data.
Examples include using higher resolution digital elevation models to
derive values for the terrain factor or enhancing the quality of soil
surveys and vegetation data by combining them with slope gradient and
aspect information. New techniques of automated terrain analysis are
also proving useful for improving the predictive capability of RUSLE by
orienting each site in the context of its location in the hydrologic
network or on the hillslope or valley floor.

The RUSLE/GIS interface can be used to model various possible management
and ecological health scenarios by changing the values for various
environmental factors to simulate different conditions, such as seasonal
change, change in precipitation, vegetation cover, terrain and
management practice.

**Automated calculation of terrain factor from Digital Elevation Models
(DEMs)**

At the National Applied Resource Sciences Center (NARSC) we developed
automated tools to make the RUSLE/GIS procedure easier to perform.
LSFACTOR.AML provides three methods of calculation of the RUSLE terrain
factor from DEM data. The products are three terrain factor maps that
represent the various methods. The first method calculates the terrain
factor according the equation used by Simanton, J. R. 1987 in his
***Revised USLE for Rangeland***, a BASIC program for DOS, Version 3.16,
developed at USDA-ARS, Tucson, Arizona. The equations reflect the
empirically derived model of how slope gradient and slope length
influence potential erosion. The second and third method are based on
the same approach originally developed for contour map analysis by
Moore, I. D. and J. P. Wilson and described in their 1992 paper
***Length-slope factors for RUSLE: Simplified method of estimation***
(Journal of Soil Science Society of America, vol. 50, pp. 1294-1298). It
was then adapted for grid cell processing by Helena Mitasova and
described in her 1993 paper ***Surfaces and modeling*** (GRASS GIS
Newsletter ***Grassclippings***, Spring 1993). This algorithm calculates
the terrain factor based on the upslope drainage area. The map LSFACTOR2
is calculated for hillslope areas defined as extending 244 meters (about
800 feet) from the ridge (slope length = 244 meters). LSFACTOR3 is
calculated for hillslopes separated from drainages by having flow
accumulation values less than 50 in contrast to drainages with flow
accumulation values of 50 and above.

**Benefits and Uses**

Interfacing GIS geographic analysis capabilities with the Revised
Universal Soil Loss Erosion provides the resource specialist with a tool
to relatively quickly visualize the likely sheet and rill soil erosion
potential (soil detachment potential but not transport and deposition)
based on several major environmental parameters for large areas. This
permits regional scale studies by quickly processing large amounts of
information from data sources that are relatively easy to obtain from
various government agencies.

The interface also permits modeling of various management alternatives
and changing environmental conditions in space and time. This permits
evaluation of the possible effects of each management practice on the
stability of the soil resource. Because the procedure relies on
standardized methodology it also allows for comparison of different
geographic areas. Furthermore it provides maps of various resolutions
depending on the resolution of the input data. The RUSLE/GIS interface
can serves as core methodology for further GIS hydrologic and
geomorphologic applications such as analysis of watershed condition,
sediment loading of streams and rivers, non-point source pollution, the
effects of hazardous wastes in soils on water quality, etc..

The RUSLE/GIS can also be seen as an example of how automation can help
to standardize application of the RUSLE to large areas. Given that input
map data is of equal quality, and the procedure is standardized,
automated processing permits repetition of the same procedure on areas
in different geographic regions. For example, automated derivation of
slope gradients and slope lengths removes the subjective elements
usually present in determination of these factors. Consistent
application of the RUSLE/GIS interface could lead to development of an
erosion index at recconnaissance level by which different areas can be
compared for soil loss.