Using GIS for Facilitating Erosion Estimation

M. Yitayew, S. J. Pokrzywka, K. G. Renard

Abstract. Geographic Information System (GIS) combined with soil loss models can enhance the evaluation of soilerosion estimation. ARC/INFO geographic information system with the Revised Universal Soil Loss Equation (RUSLE)was used to estimate soil erosion on a portion of the Walnut Gulch experimental watershed in southeastArizona. Spatialdata from different sources provided input for four alternate GIS basedprocedures in computing the combined slopelength and steepnessfactor in RUSLE for determining soil erosion estimates. Results of GIS based RUSLE erosionestimates from the fourprocedures arecompared with actual sediment yieldobserved on the experimental watershedforthe period 1973 through 1989. Results indicate GIS based RUSLE predictedsoil erosion estimates are less than theobserved measured sediment yield in most years. Application of a sediment delivery ratio which varies with watershedarea is addressed as possible explanation for the differences in estimated erosion and measured sediment yield. GIS canbe used with RUSLE to get a good estimate of soil erosion but care has to be taken in interpreting the result andcomparing it to measured sediment yield. The results from this study clearly show the needfor more work in using GISand RUSLEfor soil erosion estimation.Keywords. Erosion, Geographic information system, GIS, Soil loss, Water.

Reduction of soil erosion by water requiresidentification of excessive soil loss areas andestimation of soil loss using predictive erosionmodels. One of the most widely used erosion

prediction models is the Universal Soil Loss Equation(USLE). Recently this model was revised by incorporatingnew materials that have becomeavailable through40 yearsof additional research. The revised version, known as theRevised Universal Soil Loss Equation (RUSLE), is alsoable to include computational algorithms that enhance itscapability to predict soil erosion. RUSLE is an erosionmodel designed to predict the longtime average annual soilloss carried byrunofT from specific field slopes in specifiedcropping and management systems as well as rangeland(Renard et al., 1997). It was derived from theory of erosionprocesses combined with 10,000 plot-years of data fromnatural runoffplots and about 2,000 plot-years of rainfallsimulator data. RUSLE estimates soil loss from sheet andrill erosion caused by rainfall and its associated overlandflow. Like most erosion models, it is based on detachmentlimiting conditions designed to evaluate hill-slopes. Incontrast to process-based models that consider erosionprocesses individually, RUSLE has a lumped equationstructure thatdoes not explicitly consider runoff, processes

Article hasbeen reviewed andapproved for publication bytheSoil &Water Division of ASAE.

The authors are Muluneh Yitayew, ASAE Member Engineer,Associate Professor, Dept. of Agricultural and Biosystems Engineering,University of Arizona, Tucson, Arizona; Steven J. Pokrzywka, ASAEMember Engineer, Engineer, CH2M HILL, Phoenix, Arizona; andKenneth G. Renard, ASAE Member Engineer, Research HydraulicEngineer (retired), USDA-ARS, Southwest Watershed Research Center,Tucson, Arizona. Corresponding author: Muluneh Yitayew, Universityof Arizona, Dept. of Agricultural and Biosystems Engineering, 403Shantz Bldg. #38,Tucson, AZ 85721; voice: (520) 621-7232; fax: (520)621-3963; e-mail: myitayew@ag.arizona.edu.

of detachment, transport or deposition individually, butrather as a combination (Renard et al., 1991, 1994).

The algorithms used in computing RUSLE factors aresomewhat complex and are linked to each other and aredifficult unless used with computers. RUSLE's utility canonly be enhanced by using models with a high degree ofcomputational performance. Even though the algorithmswith which to estimate RUSLE factors are more extensive,the regression form of the factors that are used in RUSLEremain the sameas the USLE. This relationship for RUSLEis given by:

A=RKLSCP (1)

where A is the amount of erosion for the specific fieldslope measured in tons/ha/year (tons/acre/year); R is arainfall runoff erosivity factor; K is a soil erodibility factor;LS is a combined slope length and steepness factor; C is acover management factor; and P is a support practicefactor. The detail of the factors and how they effect theerosion prediction process are discussed in Renard et al.(1991, 1997).

Since computers are able to process larger amounts ofdata, recent research has been directed towards automationof erosion prediction by integrating the models withGeographic Information Systems (GIS) to save time,money, and field work. GIS is designed to store, retrieve,manipulate, and display large volumes of spatial dataderived from a variety of sources. Input data such as theUnited Sates Geological Survey (USGS) quadrangle maps,orthophotocontours and a Digital Elevation Model (DEM)are data sources that GIS can spatially process fordevelopment of length and slope steepness values. TheGIS used to analyze data was PC ARC/INFO(Environmental Systems Research Institute, 1992), avector based GIS in which features are represented by

Vol. 15(4): 295-301

Applied Engineering in Agriculture

© 1999American Societyof Agricultural Engineers 0883-8542 / 99 / 1504-295 295

Cartesian geometry. The primary role of the GIS was toobtain slope lengths and steepness and for spatiallydisplaying estimated erosion on the entire watershed. Theadvantage of GIS is that large quantities of data can beprocessed in less time than could be done by thetraditional approach of using topographic maps.

Linkage of GIS and erosion is made possible by thespatial format in which RUSLE factors are presented.These factors can be stored in GIS for each unit area insidea watershed for further calculation and graphicalpresentation of erosion. Examples of this linkage have beenattempted by Hession and Shanholtz (1988), where a GISfor targeting non-point source agricultural pollutioncombined with USLE was used to evaluate sediment

loading to streams from agricultural lands. The GIS database could be readily accessed for correcting or updatingdata, while the manual approach was not as flexible.

One of the first procedures to become widely acceptedinvolving length and slope extraction using a DEM wasevaluated by Spanner et al. (1983). In a similarinvestigation, Blaszczynski (1992) used the same criteriawhere regional soil loss was predicted using a RUSLE/GISinterface. Both found that by using GIS based data,standardization of automated derivation of slope gradientsand slope lengths from DEMs can remove the subjectiveelement usually present in determination of LS factors.

A method of LS extraction from a DEM using overlandflow theory has been proposed by Moore and Wilson(1992). They proposed a simple soil-erosion index whichaccounts for major hydrological and terrain factorsaffecting erosion. This index is considered to be equivalentto the LS factor in RUSLE. Results of previousapplications show use of GIS with a soil erosion model canenhance management decisions for land use planning andprovide an essential role in using data from many formats.

This research presents results of application of GISbased factors in RUSLE for erosion estimation for a givenwatershed, with major focus on the different algorithmsused to calculate length and slope steepness factors inRUSLE.

4.5ha (112 acre)EXPERIMENTALSUBWATERSHED

150 km'(58 mi2)

Figure 1-Walnut Gulch Experimental Watershed, Tombstone,Arizona.

296

MethodsThe Experimental Watershed

Experimental data was collected on a 4.5-ha (11.2-acre)sub-watershed located within the USDA Walnut Gulchexperimental watershed in Tombstone, Arizona (fig. 1). Thedominant vegetative cover is characterized by desert brushwith scattered grasses. Although the intended land use is asrangeland, it has not been grazed since 1962. Little changein vegetation type, density, and surface groundcover hasbeen measured since the watershed was fenced(Simanton et al., 1993).Slopes of the watershed range from1 to 15%. The soil falls in the series Rillito-Laveen whichis a gravelly loam with medium and moderately coarsetexture soil formed in calcareous old alluvium. Gullies inthe watershed are not prevalent and the existing ones arenot deeper than 0.6 m (2 ft.). Three rain gages evenlydistributed within the watershed have been operational forover 20 years. Records indicate average annual rainfall ofabout 279 mm (11 in.). This is assumed to be uniform overthe entire 4.5-ha (11.2-acre) watershed. Most of thisprecipitation comes from air-mass thunderstorms betweenthe months of July and September. As a result, precipitationin these months accounts for 95% or more of the annual

runoff. Measured sediment yield values were obtained byon site sampling techniques which are discussed in detailby Simanton et al. (1993). Erosion was estimated for years1973 through 1989 using parameters developed specificallyfor the watershed and computed by RUSLE. Theparameters used by RUSLE were computed following theprocedures below. Figure 2 represents a flow diagram ofthe RUSLE/GIS interface for data and processing used inthis study.

Rainfall-runoff Factor (R). The rainfall-runofferosivity factor (R) is one the most important parameters inerosion estimation by RUSLE. The value of R requiresconverting on-site rain gage data to an energy intensity (EI)value which represents total storm kinetic energy (E) timesthe maximum 30-min intensity (I30) as:

MEASURED

RAINFALL

EI=(E)(I»)= 5>rAV,Vr=l

130 (2)

SOIL SURVEY,

FIELD SAMPLES

DEM.USGS.

0RTH0PK0T0

FIELD

DATA

1GIS

1f 1 ' '

L S c

RUSLE

GIS

EROSION

Figure 2-Flow diagram of data and processing.

Applied Engineering in Agriculture

In equation 2, AVr is depth of rainfall for the xth incrementof the storm hyetograph which is divided into "m parts"each with essentially constant rainfall intensity (mm, in.),er is rainfall energy per unit depth of rainfall per unit area.130 is twice the maximum amount of rain falling in30 consecutive minutes. The R value for each year is thesum of the EI^Q) values for all storms within that year. Rvalues range from a low of 306 MJ-mm/ha-h-yr (18hundreds of foot-tonf-in./acre-h-yr) in 1989, to a high of3149 MJ-mm/ha-h-yr (185 hundreds of foot-tonf-in./acre-h-yr) in 1975. This demonstrates the importance ofaccurate rainfall records near the site of interest. The effect

of doubling the R value is to double predicted erosion;similarly halvingthe R value will reduce erosion by half.

Soil Erodibility Factor (K). The soil erodibility factoris related to the integrated effect of rainfall, runoff, andinfiltration on soil loss. Input to soil erodibility factorswere obtained by direct measurements in the field and aresummarized in table 1. The soil erodibility nomograph(Wischmeier and Smith, 1978) method was used tocompute an erodibility factor from soil characteristicsfound by sampling. The nomograph is comprised of fivesoil and soil profile parameters: percent silt and very finesand (<0.1 mm), percent modified sand (0.1-2mm), percentorganic matter (OM), and codes for structure (S) andpermeability (P).

The average annual value obtained for K by thenomograph method in RUSLE's program is0.01765 ton-ha-h/ha-MJ-mm (0.134 ton-acre-h/100 acre-ft-tonfin.). Sensitivity of K stems from problems such assoils that are rarely homogeneous over large areas, butmany times are treated as homogeneous for standardmapping procedures. In this sense, soils are difficult tocharacterize in terms of specific attributes needed forRUSLE's K routine. For example, on a watershed hilltop,silt and fine sands can be 18%; whereas, at the toe of hill-slopes, silt and fine sands of 25% may be present. Fieldsampling and testing of the site of interest are the bestmethods available. In this research, soil sampleswere takenacross the watershed but the average values of the input tothe RUSLE soil nomograph routine (table 1) were used toestimate an average K value. Silt and very fine sandpercentage was the most sensitive input to the RUSLE Kfactor routine.

Cover Management Factor (C). Table 2 summarizesfield input data which are required for the RUSLE C factorroutine. Field data obtained by line transect observationand sampling were input to RUSLE's cover managementfactor routine quantifying percent cover, rock, bare ground,and data such as fall height, and above and belowgroundbiomass. Forthe period from 1973 to 1989 there may havebeen somedifferences fromyear to year but such data werenot available and as a result a constant value was assumed.

Table1. Data for input to RUSLE'ssoilnomograph routine

% Silt and very fine sand% Modified sand%OM

%ClayStructure

Permeability

20.8%

54.6%

0.4%

5.0%

2 (fine granular 1-2 mm)3 (moderate, 5-20 mm/h)(0.2-0.8 in./h)

RUSLE computed K factor = 0.134

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Table 2. Summary of input values for the RUSLEC factor routine, ver. 1.02

Roughness valueBare ground (%)Canopy cover (%)Average fall heightRoot mass in top 10 cm (4 in.)Aboveground biomassRatio below/aboveground biomassBelowground biomass (%) found in

top 10 cm (4 in.)Plant community codeRUSLE computed C factor

0.8

28

26

0.305 m (lft)4285 Kg/ha (3,822 lb/acre)3065 Kg/ha (2,730 lb/acre)2.5

56

12 (South Desert shrub)0.013

A cover management factor of 0.013 for the watershed wascalculated.

Support Practice Factor (P). Support practice factorrepresents the ratio of soil loss with a support practice likecontouring, strip cropping, or terracing to soil loss withstraight-row farming up and down the slope. For ungrazed,undisturbed southwest rangeland that best characterizes theexperimental watershed, the support factor P equal to 1 forRUSLE was used.

Slope Steepness and Length Factor (LS). The LSfactor characterizes the effect of topography on erosion inRUSLE. Slope steepness and lengthfactors werecomputedby GIS using four different methods. The first twoapproaches use vector data input while the third and fourthuse raster data input. The first LS factor was derived by aphotogrammetric correctedorthophoto with 0.305 m (1 ft)elevation contour intervals. The analysis includedsubdividing the 4.5-ha (11.2-acre) watershed into 38 unitareas. The lengths and slopes were measured and recordedby digitizing each area in the GIS. Slope steepness andlength values were input to the RUSLE software LS routineto calculate LS factors on the watershed.

The second algorithm used a Tombstone, Arizona,USGS topographic 7.5-min quadrangle map with 7.6-m(25-ft) elevation contour intervals to derive the LS factor.The7.6-m (25-ft) contours on the hard copy were digitizedusing GIS to obtain lengths and slopes for input toRUSLE's LS factor routine. Slope steepness and lengthwere computed based on the 7.6-m (25-ft) contours andaverage flow length of each area. The scale of inputdata isgreater than the first algorithm.

The third was derived from a DEM with a resolution of15 x 15 m (49.2 x 49.2 ft) requiring roughly 200 gridscovering the 4.5-ha (11.2-acre) watershed. Methods used incalculating lengths and slopes using the DEM, wereadapted from Spanner et al. (1983) and Blaszczynski(1992). Slope steepness and lengths derived by GIS wereinput to RUSLE to compute LS factors for the entirewatershed.

The fourth used a DEM algorithm adapted from Mooreand Wilson (1992), using an erosion index thought to besimilar to the LS factor in RUSLE. Using the same 15 m x15 m DEM, this technique differs from Spanner (1983) inthat it modeled overland flow from a DEM whereSpanner's algorithm computed length based on existingslope steepness. The idea of extending the LS factor tothree-dimensional form, to include topographic effects isimportant in erosion estimation when using DEMs. Theerosion index is given by:

297

Figure 3-Moore's As — specific area.

Tc =-A

22.

s V"( sine )n13/ U).0896/

(3)

where Tc* is a sediment transport index substituted directlyin RUSLE in place of the LS factor. As is the contributingupland sloping area (specific area) including the area forwhich erosion was calculated (fig. 3); 6 is the slope anglein degrees; m is 0.6 and n is 1.3 (Moore and Wilson, 1992).The specific area can be rewritten as:

j

-Ii=l

Hiai(4)

where |ij is a weightingcoefficientdependenton the runoffgeneration mechanism and soil properties; as is the area ofthe \th cell; b is the width of each cell; and i = l,j representall of the i cells hydraulically connected to cell j. Here weassumed \LX = 1 for all cells which means rainfall excess isgenerated uniformly over the entire catchment area. Asfigure 3 illustrates, the As reflects the contributing areafrom various grids to be used in theTc* calculation.

Comparison of the resulting LS factors calculated usingeach method over the entire 4.5-ha (11.2-acre) watershedare presented in table 3. It should be clear that thecombined LS factor, not separate L and S, was usedbecause of the use of DEMs specifically in the third andfourth algorithm.

Results and DiscussionResults (table 3) showed that different sources of field

data for calculating length and slope factors on the same

298

Table 3. Average LS factors per unit area forfour LS estimation methods

LS Estimation Method

0.304 m(l ft.) contourUSGS 7.62 m (25 ft.) MAPSpanner 15 m (49.2 ft) DEMMoore 15 m (49.2 ft) DEM

LS Factor

1.95

1.82

1.22

2.19

experimental area had significant effects on RUSLE'serosion estimation. In comparing Spanner's algorithm (using15x 15m resolution elevation data), withMoore'salgorithm(using the same 15 x 15 m resolution elevation data), twodifferent procedures with the same source data resulted inone computing an LS factor nearly double that of the other.This resulted in doubling of predicted erosion rates.

Table 4. Summary of RUSLE calculations

/MJmm\ /

Uahyrj [

lOOfttonfin.

K

tonhahJL)mm/ha-MJ-mi

ton-acre-h

LS A

Predicted

Erosion

tons/ha

(tons/acre)

acre-h- d100-acrefltonf

1055 0.01765 1.95 0.013 1 0.471 (0.21)(62) (0.134) 1.82 0.448 (0.20)

1.22 0.292(0.13)2.19 0.538 (0.24)

* DEM estimates from table 3.

4&ha. (11.2 acre)Watershed

ESTIMATED EROSION

(tons/ha.yr)(tons/acre.yr)

> 0.964

(>0.43)

Figure 4-Erosion calculated by RUSLE derived from the 0.305 m(1 ft) contour orthophoto map.

4.5ha. (11.2acre)Watershed

ESTIMATED EROSION

(tons/ha.yr)(tcns/acre.yr)

•

•

< 0.359

(< 0.16)

0.718 - 0.964

(0.32-0.43)

Figure 5-Erosion calculated by RUSLE derived from the USGS 7.6 m(25 ft) contours.

Applied Engineering in Agriculture

A summary of the six RUSLE factor calculations (R, K,LS, C, P) are given in table 4. Four average predictederosion values are presented resulting from the 4 methodsof LS estimation procedures, and an average R value ispresented.

Figures 4 and 5 show as the resolution of elevationcontours increased (7.6 m-0.305 m) (25 ft-1 ft), the greaterdetail gave the ability to map hill-slopes more accurately.The 0.305-m (1-ft) contour elevation data indicated wherethe LS factor may be high within the watershed. Although.for computing an average LS factor per unit area on theentire watershed, there is little difference between 0.305-m(1-ft) contour LS factors and LS factors computed using7.6-m (25-ft) contour intervals at least for this illustration.

Figures 6 and 7 show the two algorithms, i.e., Spanner'sand Moore's, are able to identify spatial areas of highestimated erosion similarly, but the Spanner 15-m DEMalgorithm is consistently lower than the Moore 15-m DEMalgorithm. This stems from the procedure used forcalculating overland flow processes, and the differentalgorithm used by each for determining the flow length. Asit can be observed from the figures, the spatial variations in

4.5 ha. (11.2acre) Watershed

OUTLET

ESTIMATED EROSION EROSION(tons/ha.yr)

(tons/acre.yr)

•

•

< 0.359

(< 0.16)

0.359 - 0.718

(0.16-0.32)

0718 • 0.964

(0.32 - 0.43)

> 0.964

(> 0.43)

Figure 6-Erosion calculated by RUSLE using Spanners algorithm onthe 15 m DEM.

4.5 ha. (11.2 acre) Watershed ESTIMATED EROSION

(tons/ha.yr)(tons/acre.yr)

•< 0.359

(< 0.16)

0.359 - 0.718

(0.16-0.32)

0.718 - 0.964

(0.32 • 0.43)

> 0.964

(> 0.43)

Figure 7-Erosion calculated by RUSLE using Moore's algorithm onthe 15 m DEM.

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Table 5. Comparison of measured sediment yield and predictederosion by RUSLE using the four different LS

estimation procedures (tons/ha)

R/MJ-nini^

Measured

Sediment

Estimated Erosion by Method

Ortho Spanner Moore

Year Ihahyr/ Yield photo USGS DEM DEM

1973 1021 0.785* 0.471 0.426 0.292 0.5161974 1157 1.682* 0.516 0.493 0.314 0.5831975 3148 3.184* 1.413 1.323 0.874 1.5921976 425 0.695* 0.202 0.179 0.112 0.2241977 1293 2.982* 0.583 0.538 0.359 0.6501978 681 0.179t 0.314 0.292 0.202 0.3361979 391 o.ooot 0.179 0.157 0.112 0.202

1980 306 0.224+ 0.135 0.135 0.090 0.1571981 885 0.000? 0.404 0.381 0.247 0.448

1982 833 1.1661 0.381 0.359 0.224 0.426

1983 1140 0.359+ 0.516 0.471 0.314 0.5831984 2332 4.282+ 1.054 0.964 0.650 1.166

1985 1072 0.359+ 0.493 0.448 0.292 0.5381986 1481 0.000? 0.673 0.62S 0.404 0.740

1987 494 0.000+ 0.224 0.202 0.135 0.247

1988 851 0.740+ 0.381 0.359 0.247 0.426

1989 306 0.000+ 0.135 0.135 0.090 0.157

Average 1055 0.987 0.471 0.448 0.292 0.538St,l. dcv. 71-1 1.300 0.336 0.314 0.202 0.381

* Depth integrated pump sampler used for sediment measurement.t Flows in these years were smaller than the depth required to activate

the sampler.+. Sampler problem.

length-slope factors by the different procedures havesignificant effects on the total erosion calculation.

Total annual sediment values measured on the

experimental watershed are summarized in table 5 for years1973 through 1989. The sediment yield values werecalculated from recorded sediment yield measuring deviceson site. Discussion regarding methods and equipment canbe found in Simanton et al. (1993).

It is important to note that sediment yield and erosionare two different terms that are not interchangeable.Sediment yield for a watershed includes the erosion fromslopes, channels, and mass wasting, minus the sedimentthat is deposited after it is eroded but before it reaches thepoint of interest. Sediment delivery ratios (SDR) have beenproposed (Branson et al., 1981) to explain differencesbetween sediment yield and predicted erosion estimationfor area-wide studies (fig. 8). This was developed becauseRUSLE estimates erosion for individual hill-slopes;whereas, sediment yield is measured on a watershedconsisting of many hill-slopes with a collecting channeland its erosion source. When many hill-slopes arecombined, effects such as deposition and channel-gullyerosion may increase or decrease sediment yield.

Figure 9 shows scatter of estimated erosion using thefour methods. For each year all methods seem to underestimate average annual soil erosion, in years of normaland high erosion, compared to the observed measuredsediment yield. In years of low or no measured sedimentyield the estimated erosion by RUSLE was greater in mostcases. If years of zero sediment yield were excluded fromthe measured sediment yield data, the average would be1.39 tons/ha/year (0.62 tons/acrc/ycar). In this watershedit is possible to have no sediment yield years due to mainlylow rainfall such that the sampler was not able to reach athreshold depth to detect sediment. In some years the

299

1.0 10

DRAINAGE AREA(SQ. MI)

Figure 8-ExampIes of the relationship of sediment yield for varioussizes of watershed. Data reported as tons was converted to volumeassuming 1441.7 kg/m3 (90 lbs/ft3) (adapted from Branson et al.,1981).

1.8

1.6

«• 1.4

1 12§ 1 ^o

u] 0.8

fj 0.6

£ 0.4

0.2

X- ♦ Orthophoto- • USGS ♦

•

-

a SpannerX

x Moore•

*

:

j i X Ax x 1

A

0 12 3 4

Measured SedimentYield (tons/ha)

Figure 9-Measured sediment yield vs predicted erosion.

sampler had mechanical problems and was not measuringsediment even though there was reasonable rainfall. Therewere 5 years, (1979, 1981, 1986, 1987, 1989) withoutsignificant measurable sediment transport through thewatershed outlet in the 17-year record.

Contribution by tributary gullies as pointed out byOsborn and Simanton (1989) may be one reason for thehigher values found by sediment yield measurements. Inmost cases, years with higher rainfall runoff produced moremeasured sediment yield than what was estimated usingRUSLE. This may be explained by coarse sedimentreaching the basin outlet only during high flow years,indicating the theory of a sediment delivery ratio was notadequate in explaining the difference in measured versuspredicted values on a yearly basis (Simanton et al., 1993).It is also important to note other factors such as history oferosion events, temporal changes of the soil on thelandscape, and morphology of the drainage system caninfluence the difference between the predicted erosion andthe measured sediment yield.

A sediment delivery ratio (SDR) has been used toexplain differences between sediment yield and predictedsoil erosion. Previous research has used the SDR to explain

300

higher sediment yield on smaller watersheds, anddecreasing sediment yield with increasing watershed size.This in general is a true relationship, but did not adequatelyexplain results reflected by our data. For example, we havesome years where zero sediment yield was measured and atthe same time RUSLE predicted some erosion. It isdifficult to select a ratio which accurately explains thedifference between measured sediment yield and predictederosion. For instance, in low runoff years 0 < SDR < 1, butin normal and high runoff years SDR > 1, channel erosionincreased yield for most cases. This shows that it isinappropriate to use SDR effectively because it changesfrom year to year due to changes in flow.

The concept of a SDR was evaluated by Branson et al.(1981) in which different researchers found variation inSDR based on location to be so wide on a case by casebasis, it causes difficulty in selecting an appropriate value.This is reflected in figure 8, where all research shows adecreasing sediment yield with increasing watershed size,which makes the SDR theory generally hold true, butvariation is so large that selection may be impossible.

Based on the unsatisfactoryexplanation of the data by aSDR, some insight may be gained on the problemsexperienced on the experimental watershed by consideringdeposition and channel erosion. It is proposed that in awatershed, sediment yield (Sy) can be expressed as:

Sy = RUSLE predicted erosion

+ Channel erosion - Deposition

Thus, instead of applying a new sediment delivery ratioeach year, difference in sediment yield and RUSLEpredicted erosion can be explained by channel erosion anddeposition. Measurements and recording of these two itemspose an additional problem, but with some initial effort itmay be possible to explain annual difference observed withdata such as that used herein.

ConclusionsGIS based erosion estimations using RUSLE were less

than field sedimentyield values in normal and high rainfallyears. Estimated erosion was consistently higher in yearswhere low and zero sediment yield was measured. It wasassumed that the measured sediment yield based on thesampling techniques were sufficient to estimate totalwatershed sediment yield. Utilizing RUSLE with GIS topredict erosion on watersheds still requires quantifyingchannel erosion and deposition to achieve accurate results.To this end, more field work to measure these componentsand improve RUSLE's ability to predict erosion anddeposition is needed. Caution should be used in applying aSDR, for it only explains the difference in predictederosion and sediment yield for average annual conditions.The difference between wet and dry year RUSLE estimatesand measured sediment can only be explained by channeldeposition.

Acknowledgments. This article is a contribution from theDepartment of Agricultural and Biosystems Engineering ofthe University of Arizona and the Arizona AgriculturalExperiment Station at Tucson, Arizona. The authors would

Applied Engineering in Agriculture

like to acknowledge the support from these two units of theCollege of Agriculture, University of Arizona, Tucson,Arizona.

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Vol. 15(4): 295-301

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