Proceedings of An International Symposium 
 - Soil Erosion Research for the 21st Century 
Honolulu, HI. Jan. 3-5, 2001

(ASAE Paper)

Web-Based GIS Application for Soil Erosion Prediction 

Da Ouyang and Jon Bartholic 

Institute of Water Research, Michigan State University

Abstract

Soil erosion is a worldwide environmental problem that degrades soil productivity and water quality, causes sedimentation and increases the probability of floods. Erosion control requires a quantitative and qualitative evaluation of potential soil erosion on a specific site, and the knowledge of terrain information, soils, cropping system and management practices. Revised Universal Soil Erosion Equation (RUSLE) is widely used to estimate soil erosion. This study has developed an interactive web-based approach to use RUSLE and geographical information system (GIS) to predict soil erosion. The on-line RUSLE web site has a user-friendly interface. Users can get access to the website and use the drop-down menus to define the inputs and get the calculated results quickly. A user can run different “what-if’ scenarios and compare the impacts of different terrain, crops, soils and management on soil erosion. It will help implement the best management practices for erosion control. In addition, the web site has capability to generate an erosion index map. The map is generated from Arc/Info GIS and can be displayed on the web browser. It also allows users to inquiry the terrain information for the study area through the Internet Map Server. The on-line RUSLE web site provides a good tool for soil conservationists, crop consultants, extension agents as well as environmental educators. The framework/interface developed in this study is useful to many other applications.
 
Keywords: soil erosion, RUSLE, watershed, GIS, web 

Introduction

Problem

Soil erosion by water is a process in which topsoil on the soil surface is washed away from the land by water. Soil erosion is a worldwide environmental problem that degrades soil productivity and water quality, causes sedimentation and increases the probability of floods. Erosion control requires a quantitative/qualitative evaluation of potential soil erosion on a specific site, and the knowledge of terrain information, soils, cropping system and management practices. This project is conducted to address these issues.

Objectives

The objective of the study is to develop a user-friendly interface with ease access to users for evaluating/predicting soil erosion. Specifically, this project is to create a web site that provides information about the Revised Universal Soil Loss Equation, and the method to automate the process to compute soil erosion based on digital elevation, soil data, and other information. This online tool can be accessed to all users including farmers, crop consultants, conservationists, and education and extension personals.

Study area

The study area of this project is the southern Sycamore Creek Watershed. Sycamore Creek Watershed is located in Ingham County in south-central lower Michigan. The entire watershed drainage area is approximately 67,740 acres and is located in the center of the county. The study area is about one third of the entire watershed. The land use in south part of the watershed is primarily agriculture. Soil erosion and sediments are major concerns for water quality. The prior studies showed that in Sycamore Creek Watershed, there are approximately 1,800 acres of cropland that have a very severe erosion problem with soil losses exceeding 12 tons per acre per year. There are approximately 13,000 acres of cropland with soil losses of 8 to 10 tons per acres per year. Critical soil erosion areas deliver sediment, nutrients and pesticides, which cause water impairment in the stream. The following figure (soil map) shows the area.

Figure. 1 Soil map in the study area: South Sycamore Creek Watershed

Methodology

Soil Loss Equation

The original soil erosion model called Universal Soil Loss Equation (USLE) was empirically derived from more than 10,000 plot-years of basic runoff and soil-loss data contributed from 49 locations in the United States (Renard., et al. 1997). USLE was designated to provide a convenient tool for soil conservationists and can be used to any geographic region with modified its factors. It has been used in developing conservation plan and land use decision. Some recent research and additional information have led to a revision of USLE which provides more accurate estimation of soil loss, i.e. the Revised Universal Soil Loss Equation (RUSLE) (Renard., et al. 1997) which is chosen in this study. The procedures to estimate soil erosion in Michigan (Grigar and Davis, 1995) is followed.

Both USLE and RUSLE compute the average annual erosion by using a functional relationship of several factors, expressed in an equation as

A = R * K * LS * C * P                         (1)

where: 

• A = computed spatial average soil loss and temporal average soil loss per unit of area. Usually, A is expressed in tons/acre/year (other units can also be used).

• R = rainfall-runoff erosivity factor.

• K = soil erodibility factor, i.e. the soil loss rate per erosion index unit for a specified soil as measured on a standard plot which is defined as a 72.6-ft (22.1 m) length of uniform 9% slope in continuous clean-tilled fallow.

• L = slope length factor, i.e. the ratio of soil loss from the field slope length to soil loss from a 72.6-ft length under identical conditions.

• S = slope steepness factor, i.e. the ratio of soil loss from the field slope gradient to soil loss from a 9% slope under otherwise identical conditions.

• C = cover-management factor, i.e. the ratio of soil loss from an area with specified cover and management to soil loss from an identical area in tilled continuous fallow.

• P = support practice factor, i.e. the ratio of soil loss with a support practice such as contouring, stripcropping, or terracing to soil loss with straight-row farming up and down the slope.

A detailed description of these factors is available from the web site. Among these factors, terrain factor – “L” is most difficult to compute. Fortunately, the soil loss equation is much less sensitive to L factor than another terrain factor – S factor which can easily be computed from the digital elevation model (DEM).

L factor and S factor are usually considered together to combine the effect of slope and slope-length, which basically reflects the terrain on a given site. For this project, an approach developed by Moore and Burch (1985) is used to compute LS factor. They developed an equation to compute length-slope factor:

LS = (As / 22.13) ^m  * (sin β / 0.0896) ^n                                 (2)

where: 

• m = 0.4 – 0.6 and n = 1.2 – 1.3.

• LS = computed LS factor.

• As = specific catchment area, i.e. the upslope contributing area per unit width of contour (or rill), in m‘ / m. It is calculated in Are/Info using the function called “flowaccumulation” multiply by the squared cell size and divided by the cell size.

• As = calculated flowaccumulation * 30 * 30 / 30 (for cell size = 30 m).

• β = slope angle in degrees. It is calculated in Arc/Info using the function called “slope” with option “percentrise” which is 100 times Tan β. Then β is calculated using “Atan” function in Arc/Info.

• Tan β = slope (in percentrise) / 100 

• β = Atan (Tan β)

Data Source

The original digital soil data and elevation data were obtained from the NRCS-USDA Michigan State Office. The K value, one parameter in RUSLE, is added into soil data as the original data did not contain it. K values are taken from Ingham county soil survey (1992). A R value (95 for Ingham county, MI) was used for RULSE according to the Technical Guide. C and P values are used based on the user’s inputs and the RUSLE Technical Guide.

Web Design

The web site is designated to provide information about the soil loss equation (RUSLE), drop-down menus to allow a user to select parameters to estimate the soil erosion, information about terrain in the study area, and mapping erosion index for the study area. Several pieces of programs are written by the author to accomplish the tasks.

In order to get the inputs from the user, an input form is created using hypertext markup language (HTML). Then the input data is parsed and sent to the server using a Common Gateway Interface (CGI program, written in Perl). This CGI program calls another program (RUSLE model, written in C++) for calculation of erosion, and sends the results back to the client side (web browser).
For erosion index mapping, a program written in Arc Macro Language (AML) is used to calculate the erosion based on the user’s input and a grid for the study area. The grid coverage is then reclassified using a grid function called “SLICE” in Arc/Info. Then the reclassified grid is converted to an image file with different colors representing different erosion potentials, using a grid function called “GRIDIMAGE” in Arc/Info. The image is sent back to the web browser through a CGI program.

In addition, the Internet Map Server extension for ArcView is used to show the different layers for the study area. The information includes soils, DEM, streams, roads, contours, slope, flow length, and calculated LS factor. A user can look at the study area for those information by simply selecting a layer.

Results and Discussions

The end product of this project is to create an online RUSLE web site. The web address is http://www.iwr.msu.edu/rusle.  From this web site, a user can do a wide range of scenario predictions for soil erosion. Data is available for most counties in Michigan. Terrain information inquiry and erosion index mapping is also available for the study area.

This web site can be used for “what-if’ scenario prediction and sensitivity analysis. The following tables show an example use of this online RUSLE website. The results are obtained from the web site with same area, crop rotation and tillage management.

Table 1. Sensitivity analysis of soil erosion and slope (other factors are same)

  
 Table 2. Sensitivity analysis of soil erosion and slope length (other factors are same)

Table 1-2 show that soil erosion is more sensitive to the slope steepness than to slopelength.
In addition, the online RUSLE tool can be used to compare the impacts of different management practice on soil erosion. By selecting a different practice, an erosion index map can be generated from the web site in real-time. The difference between conservation tillage, no till and conventional tillage can visually be seen from the erosion index maps.

The online RUSLE web page is the most visited page on the website of Institute of Water Research at Michigan State University. Online RUSLE can save users a lot of time compared to the traditional method by which a user has to go through many tables in RUSLE User’s Guide to manually check all parameters.

Conclusions

This online RUSLE web site demonstrates a promising application of information technology combined with GIS for conservation, environmental education, planning and beyond. The advantages include easy to use, available all time, no software to install, and quick calculation. Terrain feature, particularly slope, plays an important role in soil erosion. Best management practices can greatly reduce soil erosion. 

Acknowledgements

The web page is developed based on the RUSLE technical guide provided by the USDA-NRCS Michigan State Office. We thank Jerry Grigar, Jr., State Agronomist and RUSLE specialist for providing the assistance.

References

Bone, S,W., R. Christman, L.M.Feusner, R.L.Goettemoller, and B.H. Nolte. 1979. Ohio Erosion Control and Sediment Pollution Abatement Guide for Agricultural Land. The Ohio State University Extension Bulletin 594.

Grigar, J., and S. Davis. 1995. Water Erosion Prediction and Control. Technical Guide Section I-C. Michigan State Office, NRCS, USDA.

Hoover, K.A., M.G. Foley, P.G. Heasler, and E.W. Boyer. 1991. Sub-Grid-Scale Characterization of Channel Lengths for Use in Catchment Modeling. Water Resources Research. Vol. 27, No. 11, pp 2865-2873.

Moore, I.D., and R.B. Grayson. 1991. Terrain-Based Catchment Partitioning and Runoff Prediction Using Vector Elevation Data. Water Resources Research, Vol. 27, No.6, pp 1177-1191.

Moore, I.D., and G.J. Burch. 1986. Sediment Transport Capacity of Sheet and Rill Flow: Application of Unit Stream Power Theory. Water Resources Research, Vol. 22, No. 8, pp 1350-1360.

Moore, LD., and G.J. Burch. 1986. Modelling Erosion and Deposition: Topographic Effects. Transactions of the ASAE.

Moore, LD., and G.J. Burch. 1985. Physical Basis of the Length-slope Factor in the Universal Soil Loss Equation. Soil Sci. Soc. Am. J. 50: 1294-1298.

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). USDA Agricultural Handbook, #703.

USDA-NRCS, MAES. 1992. Soil Survey of Ingham County, Michigan.

If you have any questions regarding this paper, please contact us: Da Ouyang or Jon Bartholic.  Visit our RUSLE online website:  http://www.iwr.msu.edu/rusle