Natural Resources

Geographic Information Systems Lab

Geographic Information Systems Lab

The potential for soil erosion is based on a number of factors, including climate, soil type, slope, and slope. We summarized this using factors from the Universal Soil Loss Equation. The Soil Erosion data layer represents a general risk score for potential soil erosion on a 0-100 point scale, 100 being the highest risk. Larger values indicate soils that have a higher potential to erode if no conservation practices were in place and overland sheet or rill runoff was present.

A subset of the Universal Soil Loss Equation (USLE) was used to determine soil erosion risk values. The USLE is a multiplicative equation using the formula A =R x K x LS x C x P where:

- A = potential long term average annual soil loss in tons/acre/year
- R = rainfall and runoff factor
- K = soil erodibility factor
- LS = slope length-gradient factor
- C = crop/vegetation and management factor
- P = support practice factor

The R (Rainfall), K (Soil Erodibility), and LS (Length/Slope) factors were used and calculated based on NRCS spatial and tabular SSURGO soils data, statewide county climate maps, as well as mathematical formulas based on standard USLE calculations. SSURGO stands for Soil Survey Geographic Database.

The crop/vegetation and management factor and support practice factor were not used. This is because there are no reliable statewide spatial data that represent these factors. Although there exist statewide data depicting current cropping practices, there are no statewide data representing current tillage methods (e.g. fall plow, ridge tillage, no-till) or support practice (e.g. cross slope, contour farming, strip cropping) that are required for these calculations. Furthermore these factors are temporal and will therefore shift over time.

Since only non-management factors were used, the resulting data layer should be viewed as a “worst-case” scenario, i.e. highest potential soil erosion of bare soil with no mitigating land use practices in place. Although quantitative soils loss numbers (tons/acre/year) may be exaggerated under this model, the resulting data layer is used here in a qualitative, comparative capacity in order to compare the relative differences in soil loss risk between various parts of the landscape.

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