Soil Erosion Modeling Using “USLE”: Two Approaches for Evaluating the Parameters “L” and “S”

Antonio Roberto Formaggio Marcelo Goncalves Gameiro Jo& Carlos Neves Epiphanio

National Institute for Space Research - INPE AV. dos Astronautas, 1758

12227-010 - S2o Jose dos Campos (SP) -Brazil Phone: +55-12-345-6472 - Fax: +55-12-345-6460

e-mail : formagal tid. in=bE / e&hani@l tid. inwdr

ABSTRACT

Agricultural activities can accelerate signrficantly the soil natural erosion processes. Thus, for a sustainable environment, the modeling of soil erosion losses is a fundamental step towards the rationalization I optimization of the agricultural land use. In this research, the model “Universal Soil Loss Equation” (USLE) [ 11 was used, test site was a watershed in a region of highly intensive agriculture in S b Paul0 State (Brazil); and GIS / remote sensing techniques were employed for spatialize the soil erosion losses by water. The main objective was to test Werent approaches of modeling the USLE topographic parameters: ‘Z” and “S”. For “S” parameter there wasn’t statistical differences, but for “L” the approaches of modeling cause significant differences in the final spatialized results, showing the need of improve the methods of modeling the USLE most impacting parameters.

INTRODUCTION

Last decades have experienced a great deal of efforts for a mathematical and computational description of natural and anthropogenic processes through the environmental modeling techniques [2]. In this direction, one of the main models for studymg soil erosion by water is the “Universal Soil Loss Equation” - USLE 111. This model can be applied in many situations and it is adaptable to the various environments over different latitudes.

In this model, the topographic factor is used to measure the joint effect of the hill length (L) and the slope (S). Topographic factor is one the main factors responsible for the final soil erosion caused by water when evaluated by USLE [3]. However, there are different approaches for modeling L and S. In this paper it is presented, discussed and evaluated some of these approaches in terms of soil loss in a watershed.

0-7803-4403-0/98/$10.00 0 1998 IEEE

STUDY AREA

Pinheirinho watershed is a test site of about 7 x 4 km2 located near the center-east of S b Paul0 State (22’47’40” - 22’54’52”s; 47’20’16”- 47’20’23”W). The terrain is mainly gentle-undulated and the soils are predominantly oxic soils. There are several classes of land use, such as sugarcane, pastures, corn, potato, sorghum, cotton, fallow fields, beans, urban area, water bodies, bare soil, and natural vegetation.

METHODS FOR MODELING L AND S PARAME’IERS

To model the topographic factor for a watershed is to quantitatively spatialize it for the entire watershed. This can be done in an GIs environment.

The more traditional used methods for spatializing the hill length (L factor) are: (a) grid method (L1 method): when the L factor can be calculated using a regular grid laying over a topographic sheet that contains isoaltitude curves (see [4] and [5] ) ; (b) isocomplere method (L2 method): firstly, over the topographic map, the water preferential paths are traced, then each water preferential path is divided in regular intervals, beginning from the top of the hill. After that, the points with the same distance of water path are linked, generating the isocompleres. These isocompleres are then digitized, entered in a GIs, and a regular grid of 250 x 250 m or better is generated (see [6]). Since roads and terraces moderate the superficial water path in the hill, some correction methods may be applied for the isocomplere method.

[3] stated that when the preferential water path crosses a road (until the 45’ limit), the hill length should be zeroed, and that point should start a new water path length. For fields that have terraces, the value of hill length should be 50 m, and fields without terraces should have their original values.

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Two ways of modeling the hill slope (S factor) were availed: (a) abacus method (S1 method) (see [7]), that is a simple method of calculating the slope classes by measurements in a topographic map; and (b) grid method (S2 method) (see [SI): in this method a grid of 0.5 x 0.5 cm (terrain resolution of 250 x 250 m for 1:50,000 scale map) is used and in each cell it is counted the number of isoaltitude curves that cross it. Thus, it is obtained a bidimensional matrix that is input in GIS as an information plane referred to as slope factor.

All the possibilities of combining these methods were entered in-the USLE model using a GIs for obtaining the final spatialized values of soil losses in the studied watershed, then statistical testing were applied to assess how significant were the differences between the employed methods.

Soil Loss

(ton/ha.year)

14.5

10.0

RESULTS

Information Plane and Sign Test Result

A2 A3 A4 A5 A6

(Equal) (Eq ual) Equal (Greater) Greater

Lower Lower (Lower) Greater (Greater)

Table 1 shows the mean soil loss calculated for the entire watershed for six different combinations of methods used to calculate L and S USLE factors. The superficial runoff method for calculating the L factor is not advantageous because it has to be calculated manually. Consequently, as the resolution increases it is more difficult to calculate it. Thus, only coarse resolutions or small areas can be studied by using this method. It should be remembered that the final precision of the system in a GIs is limited by the information plane with the coarsest resolution.

Taking the L factor into account, the isocomplere method has an advantage because it allows to work in any resolution, while the grid method has some limitations. Another advantage is that it allows some degree of automation, thus less subjective. Its main disadvantage is that for points near the channel, there are some misinterpolation. The same happens for roads, where in the process of tracing the isocompleres, the distance between the roads and isocompleres is less than the usual, and this causes misinterpolation. In order to minimize this difficulty and the error, whenever there was suf€icient space, it was traced one isocomplere with less than the conventional 200 m in between the last one and the road or channel.

The abacus method for calculating S is well-known; however it presents some drawbacks in the weighting process and for veiy flat areas. In order to weight for each slope class one must choose a weighting factor. Here, the mean value of the class can be used as weighting factor; however, this does not mean that the mean value is the best value for each facet of the trapezoid used for calculating the slope. For flat areas it is difficult to find points to slide the trapezoid; in this case the slope class was the class following the last one that could be determined. Nevertheless, this method can be considered very useful for calculating hill slope.

The grid method for calculating the S factor is simple and fast; however, its main drawback is the resolution. Only coarse resolution or small areas are workable. In addition, the efficiency of the method decreases for areas presenting heterogeneous topography.

Table 1 - Comparison between the various methods for calculating L and S, in the 250 x 250 m resolution. The

2-j 4.5

Greater (Greater) + Significance level = 0.05. Parenthesis shows relations where only one factor (L or S) is calculated in a different manner. Interpretation: Information Plane A2 is statistically lower than the Information Plane A3. A1 = LlSl; A2 = LlS2; A3 = L2ASl: A4 = L2AS2: A5 = L2CSl: A6 = L2CS2. “A” indicates isocomplere method without corrections for roads and terraces; “C‘ indicates isocomplere with corrections for roads and terraces.

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For S parameter, Table 1 shows that there is no statistical difference between abacus (Sl) and grid (S2) method. Nevertheless, in general, soil loss estimated by the abacus method tends to be higher than the grid method (see comparisons A1 x A2, A3 x A4 and A5 x A6).

For the L factor (see comparisons A1 x A3, A1 x A5, A3 x A5, A2 x A4, A2 x A6, and A4 x A6) there is a good agreement between the soil loss estimation and the result of the sign test, when one fix the hill slope method. In addition, except for the comparison A1 x A3, there is a statistical difference between the methods for calculating the L factor. In general, the soil loss estimation is low for isocomplere with correction for roads and terraces method, intermediate for superficial runoff method, and higher for the isocomplere without correction method.

When one fixed the method for hill length estimation, one could evaluate the impact of the method for slope estimation. There was no statistical difference between the soil loss estimation by the abacus (S1)and grid (S2) method (see comparisons A1 x A2, A3 x A4, and A5 x A6); however, the values of soil loss for the abacus method were always higher than those for the grid method. This could be explained by high subjectivity and low resolution of the grid method. In addition, some authors (e.g. [9]) have shown that USLE is not very sensitive to the L factor.

CONCLUSIONS

After integrating all the parameters in the USLE model, trough a GIs for evaluating soil loss in a watershed, and testing three methods for evaluating hill length and two methods for hill slope, it was concluded that there was no statistical difference between the methods (abacus or grid) for evaluating S factor.

However, for L does exist si@icant difference among the methods. The new method isocomplere with correction for roads and terraces presented the lowest values of soil loss and it was the method that gave the best results after field work evaluation. In addition, this method is not limited with respect to the resolution. Nevertheless. the method of isocomplere with corrections presents some drawbacks for areas near the roads and channels.

REFERENCES

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