1. Average Annual Soil
Loss of the Sevenmile
Creek Watershed
U n i v e r s i t y o f W i s c o n s i n -
W h i t e w a t e r
W h i t e w a t e r G e o g r a p h y
D e p a r t m e n t
3 / 1 1 / 2 0 1 4
King, Aaron J
With changingregulationsandfastgrowingagriculture,the
state of Iowaiscurrentlyundergoingsoil erosion
assessmenttodetermine the impactsoil erosionishaving
on local watersheds.The Sevenmile Creekwatershed,
locatedinthe loesshillsof southernIowa,anortheastern
tributaryof the NodawayRiver,hasbeenassessedusing
the modifiedsoillossequationinordertocalculate the
estimatedannual soil loss(tons/acre).

2. Introduction:
Soil Erosion is fast becoming a problem that is only getting worse. Some estimates state that
as much as 4 billion tons of soil erosion occurred annually by the 1970s (Schwabet al. 1993). Not
only, but research also has provided that factthat soil erosion can also reduce the productivity of
some soils (Lowdermilk, 1953; Shertz et al. 1989). This can limit the amount that the soils can
actually produce. This is why erosion is the main source of sediments that pollutes streams and fills
reservoirs. For this reason, the need tocalculate average annual soil loss forwatersheds is so
important. This can help manage and then develop solutions as to how problems can be dealt with.
The Sevenmile Creek, locatedin the Southern Loess Hills of Southern Iowawill be examined using
the modified soil loss equation in order to accurately determine the estimated annual soil loss per
year of that specific subwatershed.
The Sevenmile Creek is located in the Southern Loess hills. It is a tributary to the Nodaway
River, whichis a tributary to the Missouri River, whicheventually leads to the Mississippi River.
The loess hills refers to the typeof soil sediment in the area. The dominate way that soil has been
deposited in the loess hills is through loess, or wind-blownsilt. Over millions of years, silt particles
have accumulated to form the Loess Hills of Southern Iowa.These particles were deposited as the
result of the Pleistocene glacial activity. There are 3 major loess deposits, Loveland(120000-
150000 yrs. ago), Pisgah (25000-31000 yrs. ago) and Peoria (12500-25000 yrs. ago) whichis the
most commonly seen unit in Iowa.
The loess hills have a distinctive land scape. Its western extent flowsinto the Missouri
River. The topography of the area is sharp withalternating peaks. The silt allows for a dense
networkof drainage ways, resulting in gullies and ravines. This allows for very pronounced gully
erosion. This further affectscrop lands and stream channels. This is another reason the soil
erodibility needs to be measured and check.
Since the 1980s, CRP lands have become more popular. This is where the government buys
up crop and pays farmers to not plant on it. This immensely stops soil erosion. Having plants rootin
the soil solidifies the soils in place. Howeverdue to the ethanol popularity, in 2007, the government
signed into law gasoline needed to use ethanol. This led to a huge increase in corn prices. This made
it more profitable to plant again instead of not, so CRP land is starting to drop. For this reason, there
is a growing concern that soil erodibility willagain increase, causing more environmental and
economic harm.
The way in whichthe watershed will be analyzed will be done through the modified annual
soil loss equation (RULSE).This equation takes into account several factors that could affectthe
erodibility of the area. These factors include rainfall intensity, soil cover, slope length, slope
steepness, land cover,and support practices. Combined, these elements can help determine the
average annual soil loss of the Sevenmile Creek Watershed.

3. Methods and materials:
The Sevenmile Creek watershed creek data was downloaded on February 6th, 2014 fromthe
Natural Resources Geographic Information Systems Library (http://www/igs.uiowa.edu/nrgislibx)
(NRGIS) by Aaron King, University of Wisconsin-Whitewaterstudent. This data was collected and
maintained by the G.I.S. department of the Iowa Department of Natural Resources. All geo-
referenced data is projected in Universal Transverse Mercator (UTM),Zone 15, North American
Datum of 1983(NAD83). The watershed data includes basin shapefiles, basin buff shapefiles, and
reference files (DEM,soil data, slope, and Land Cover from 2002) used for the project. The primary
data that was downloaded came from the county data. The Sevenmile Creek is located primarily in
the Cass County, witha small section located in Montgomery County. Each county supplied soil data
(includes soil description, variables and K-factor) and a DEM.
In order to calculateannual soil loss, the modified RUSLE equation will be used. This
equation is the most widely accepted method of estimating soil loss. This equation was developed
my Wischmeier and Smith (1978):
A stands forthe average annual soil loss in tons/acre for a given area. A is found by
multiplying 6 components together. Each component is a functionimportant to soil loss. R stands
for the rain fall and run-off erosivity factor.K stands forthe soil erodibility factor.L and S are often
combined to make an LS factor,that is the slope steepness and length factorof the given area. C is a
covermanagement factor.P is the conservation practicefactor.
The R factoris the rain fall and erosivity factor. The R factorchanges depending on the
amount of rainfall and the storm precipitation. Eachstorm is given an index value, EI. E stands for
the kinetic energy of the storm. I stands forthe maximum 30-min intensity for that storm. All EI
values in a specific area are calculated throughout the year to get an annual sum. This value is the R
value. The value that was used forthis specific watershed of the Sevenmile Creek was determined
from “PredicatingRainfall Erosion Losses – A Guide to Conservation Planning” via figure 9.6 of the
Soil Conservation and Sediment Budgets paper (Table 9.6, chapter 9, pg. 263) as wellas IowaNRCS
data (1999). This value was not a shapefile, rather a value that was plugged into the raster
calculatoras a number, instead of a field. In this watershed we used a value of 160.
K Factor is found through soil data that was downloaded forthe county data. Since the
Sevenmile watershed expands into two counties, twodifferent soil datas were downloaded. Soil69
and soil15 shapefiles (for Cass and Montgomery County) were merge together using a merge
shapefiles tool. The field in the attribute table that was used to join the two files were Muskey. This
allows the two shapefiles to share the data, allowing the user to use one big continuous shapefile.
There was a specific K-Factor field in these combined fields that was used to do the K-factor.At this
point, the shapefile was clipped to just include the the watershed. In order to pull out the field to be

4. used in the raster calculator,the toolFeature to raster was used. Using this field, I selected K-factor,
and pulled out the K-factor.This automatically pulls this field out and creates a raster that was used
in the Raster Calculator.
LS factor, whichis the slope length and steepness was done via the Raster Calculator
featuring flow accumulationand slope. In order to get the slope of the water shed, the DEM (Digital
Elevationmodel) was downloaded foreach of the counties. Oncethese features were downloaded,
they were then merged using the tool, mosaic to new raster. This allowsArcMap to merge the DEMS
into a new raster file.Once these were merged, the raster file was clipped according to the
watershed basin. Oncethe Dem was finished, the Fill tool was used to bring out and fill the DEM
spaces. This DEM was used in both flow accumulation and slope (Beriby, 2006).
In order to take the slope of the DEM,the Slope tool (spatial analyst extension) was used.
The slope is featured in degrees. Since the slope was featured in centimeters, the Z-factor,which
determines how far forwardand backwards the slope elevation goes, was changed to 0.01 to
accountfor this change; this gives us the proper slope in degrees.
In order to get flow accumulation, flow direction needs to be calculated.This is done from
the filled DEM. The Flow direction tool was used to acquire Flow direction. Once the Flow Direction
was finished, the Flow Accumulation tool was run on the Flow Direction.This allows for the Flow
accumulation.
Once the Flow Accumulation and the slope have been done, a raster calculatoris used to find the LS
factor.In the raster calculator,this equation was used (Breiby,2006):
Power(“flow_accumulation”* cell resolution / 22.1, 0.4) * Power(Sin(“slope”* 0.01745) / 0.09, 1.4)
* 1.4
Here, flow accumulationis plugged in as wellas the slope. The cell size, which is 30m,30m (plug in
30) is also plugged in forthe resolution. This output will be the LS factoras a raster that can be used
in the RUSLE equation.
The C factoris land coverdata. This data was downloaded from the watershed data which
included the basin cover.Three separate years were downloaded, one from2002, one from 2000
and one from 1992. In order to get the most accurate data, the 2002 land coverwas initially used to
determine land covertypes, but the file was corrupted. So, as a backup, the 2000 land cover was
used. Eachspecific land cover typewas given a value that has been predetermined. This value is a
coefficientthat allows forthe amount of soil erosion each land covertype has. Each value that was
plugged into the attribute table in correspondence with a paper “MODELINGALTERNATIVE
AGRICULTURALSCENARIOS USING RUSLE AND GIS TODETERMINE EROSIONRISK IN THE
CHIPPEWARIVERWATERSHED,MINNESOTA” by ElenaDoucet-Bëer.(Bëer, 2011, PG. 27)(Breiby,
2006). This report outlines specific land covertypes and the C-factorthat corresponds with it. In
order to insert these into the C-factorshapefile, I opened the attribute table and added a field. This

5. field was named C-factor.The field was a float, withprecision of 5, scale of 4. After the field was
added, I opened up an editing session, and plugged in the numbers foreach attribute. See Table 1
Rowid VALUE COUNT CLASS C-FACTOR
0 1 115 Water 0
1 2 7606 Forest 0.002
2 3 130541 Grass 0.005
3 4 123446 Corn 0.6
4 5 103831 Beans 0.45
5 6 712 Artifical 0
6 7 143 Barren 0.3
Table 1. This is the attribute table for the C-factor. These values were used exported and used inthe RUSLE
equation.
In order select these features, the LookUp tool (Spatial Analyst) was used. This allows you
to take a feature in the attribute table for a vectorfile, and convertthe selected attribute to a new
raster file that can be used in the raster calculator.
The P factor,whichis the management practices, is also a constant in the RUSLE equation,
much like the R factor.This number ranges from 0-1, 0 being the best prevention practices and 1
being none. Because the soil erosion practices are unknown, a value of 1 is used in the raster
calculator,in order to see primarily whatthe potential soil erosion in the watershed would be.
In order to calculatethe RUSLE equation, all of the mentioned fields were combined in the
raster calculator. They were combined as
160 (R-factor)*“K-Factor” * “LS-Factor” * “C-Factor” * 1 (P-Factor)
This output willgive the estimated average annual soil loss in tons per acre per year.

6. Analysis
The watershed basin that was in use is located in the Southern IowaLoess Hills, locatedin
Cass and Montgomery Counties see figure. 1. The watershed covers 94668 acres.
Figure1. This is the area of the Watershed that is being analyzed.

7. K factor,see fig.2 wasdeterminedviasoil datagiventhroughthe countydata. Thisdatahas
valuesthatrange from 0-1. Each soil type thatthe watershedareaismade of isgivena value,in
accordance to itserodibility.Kcan varydependingonseasonal variationinsoil erodibilityaswell asafter
tillage.These valuesweredeterminedfrommerge soildataandhad beenpreviouscollectedandputin
the attribute table.Inthismap,the valuesrange from0 to 0.43 showinga generallyhighamountof soil
erodibility.The soil inthe watershedispredominantlyloessdepositedsandsandsilts.The darkervalues
showhigherK-Factors,predominantlyalongthe creekvalley,andthe brownerthe valuesget,the lower
the K-Factor.
LS factor was derivedthroughDEM,Flow Direction,Flow Accumulation,andSlope.The DEM,
see fig.3, showslow spotsalongthe valleyof the river,whichcoincideswiththe previousbase maps.
Thisalsoshowssmall smallercreeksthatflow out;thisisalsolowerinelevation. The highestpointslook
to be rollinghills,mostlikelyfromthe depositedloessthatdominatesthe soil of the regions.Thisalso
correlateswiththe base map.
From the DEM the Slope wasfound.Since the valuesare inmeters,the Z-factorneededtobe
adjustedinorderto properlydisplaythe slope of the areaindegrees.See fig.4.The slope corresponds
withthe DEM of the area,showinglowslope valuesof 0 degreesslope change atcreekandhighest
locationsrightnexttothe creek. The highvalue isapproximately24 degrees.The valuesshow fromthe
headwatersandgrowdownstream,withthe valuesbecomingmore frequenttowardsthe confluence.
Most of these looklike theyare the resultof soil beingdepositedasthe streamflowsdownriver.
The DEM wasalsousedto calculate flow directionandlaterflow accumulation.Inthe Flow
direction, see fig.5,the lightervaluesshow anincrease inthe amountof flow,while asthe darkitget,
the amountof runoff decreases,showingthe flowof direction.Thismapwasthenusedto getflow
accumulation,see fig.6.The lighterthe areais forthe flow accumulationshowsthe areawiththe
highestamountof flowaccumulation.Onthe map,thiscentersinthe actual creek,whichwouldbe
accurate.
The combinationof the twomaps inthe raster calculatorgivesthe LS factor,see fig.7. The LS
factor displayslengthandsteepnessof slope asavalue.The greenareason the map area are areas that
have higherthe values.Thismeansthat theywill be mostaffectedbythe slope steepnessandlength.
C-factor,see fig.8, contains specificlandcoversfeaturesthathave adeterminedvalueforsoil
erosion.The valueswere brokendownto7 classes,water,forest,grass,corn,beans,artificial, and
barren. Each land coverwasgivena value (see table 2).Water=0,Forest-0.002, Grass-0.005, Corn-0.3,
Beans-0.5and Barren-0.6.

8. Figure 2. This shows the K factor for the area. This file was converted from a vector shapefile into a raster using the feature
to raster. This value will be used in the RUSLE equation.

9. Figure3. This is the DEM of the Watershed analysis. It has a range of 11486.

10. Figure4. This is the slopeof the watershed. It ranges from 24 degrees at the highest point to 0 degrees at its
lowestpoint.

11. Figure5. This shows flow direction. TheDarker values show higher amounts of runoff and movement as opposed
to light areas that show very little movement.

12. Figure6. The flow accumulationis highest at the Creek due to where the water and soil is deposited.

13. Figure7. LS is shownhere. This layer will becombined with the other factors to producethe RUSLE equation.
This shows slopelength and steepness.

14. Figure 8. This shows the various land covers in the watershed. This is mostly barren, bean and grass land.

15. Whenall R-Factor (160), K-Factor,LS-Factor,C-Factor,and P-Factor(1) are combinedinthe
RasterCalculator,the resultisthe average annual soil lossforthe watershed. Thisisthe RUSLE
equation,see fig.9. The areasthat have are the darkestshow the leastamountof soil loss.Thisismostly
inthe physical creekitself,whichwouldmake sense due tothe lackof soil actuallyinthe water.The
lighterareasare the areasthat have the most soil loss.These are predominatelyinareasthatare higher
up,and have the highestslope.
Once the RUSLE equationwascalculated,basicstatisticswere runtodeterminethe average
annual soil lossperacre peryear.Accordingthe data from the RUSLE equation,the sumtotal of soil loss
ina yearwas 582765.2704 tons.If youdivide thatbythe area of the watershed(94668 acres) there isan
estimatedvalue of 6.1tons peracre peryear.

16. Figure 3. This shows the RULSE equation. The average annual soil loss is 6.1 tons per acre per year.

17. Conclusion:
UsingArcMap software,R,K,LS, C and P were combinedtocreate the average annual soil loss
equationorthe RULSE equation.Thiswasdone in30 by30 meterresolutionanddisplaysaccuratelythe
data that wasdownloaded.Withthe datathatwas usedinthe processof estimatingthe average annual
soil loss,the estimatedvalue endedupbeing6.1tonsper acre peryear.
The average annual erosionforIowaisroughly5.2 tonsper acre peryearaccording to
Environmental WorkingGroupas a summaryof Iowa Soil Loss. Thisnumberisdeemedslightlyhigher
than sustainable. The soil erosion forthe SevenmileCreekwatershedis slightlyhigherthanthe average.
Thiscouldpossible notbe all humanerrorhowever.Asthe reportcontinues,it stormsasrecentas of
2007 have triggeredsoil lossesthatwere 12 timesgreaterthanthe federal average forthe state of Iowa.
Thisintensitycouldbe afactor inthe R value. Since the value thatwasusedinthisreportwas froma
chart that was published in1978, there isa probabilitythatthe erosionisevenhigherwithamore
accurate R factor. A good wayto check isviathe website,whichproducedagraphicthatdisplays
average annual soil lossforall of Iowa,see fig10.
Figure 10. This graphic was taken from the EWG website and shows average Soil Erosion for Iowa
http://www.ewg.org/losingground/report/executive-summary/2.html

18. Thisgraphic showstwo importantdetails.First,the estimatedsoil lossthatwascalculatedisin
accurate to the pointwhere itshouldbe (between5.1-10.0).Second,there isalsoleewayinthe average
annual soil loss.Soan increasedR,whichwouldleadto anincrease average soil erosion,wouldputme
still inthe propercolumnof 5-10.0. Thisadds legitimacytothe project.
ThisRUSLE equationalso doesnotaccountfor a changingP value.The P value helpsto
determine landcoverpractices.A value of 1was usedinthe equation,whichdeemsthatlittle tono
extrapracticesinorder to preventsoil erosionwere used.If thisexperimentweretobe redone,thenP
and R –Factor needto be furtherexaminedtofurtheranalyzethisproject.
Since the average annual soil lossisalreadyabove the “sustainable”limit,measuresneedtobe
takeninorder to preventany more soil loss.Thisisa trendthat isonlyrising.Accordingtofigure 10,
there isa highamount of soil lossinthe southwesternpartof the state.The bestmeasure to take
wouldbe to start implementingmore CRPcroplandto preventsoil loss.
Since the majorityof the soil iswind-blownloess,plantinggrasseswouldhelppreventthe loose
soil fromcomingup.Lookingat the C-Factor (fig.8) you can see the majorityof the landit eitherbean
farms,barrenor grasses.These all have veryhighC-factorvalues,rangingfrom0.4-0.6. Thiscouldalso
increase the chance of runoff fromoverlandflow. Thiscouldbe the reasonthatthe soil erosionisso
high.There isno plantmaterial tokeepthe soil fromerodingaway. Thisisimportantformanyreasons.
One primaryreasonisthat soil erosioncanreduce the productivityof some soils(Lowdermilk,1953;
Shertzetal. 1989 viaSoil ConservationandSedimentBudgets).Withthe reductionof soil,thisdoes
adhere tosustainable soils.
Breiby, T. (2006). Assessment of Soil Erosion Riskwithin a Subwatershed using GIS and RUSLE with a Comparative
Analysis of the use of STATSGO and SSURGO soil Databases. Department of Resources Analysis, Saint Mary’s Universityof
Minnesota, Winona, MN, Volume 8,
Cox, C., Hug A., Bruzelius, N. (2014) Executive Summary, EnvironmentalWorking Group, LosingGround.
http://www.ewg.org/losingground/report/executive-summary.html
Doucet-Bëer, E., (2011) MODELING ALTERNATIVE AGRICULTURAL SCENARIOSUSING RUSLE ANDGISTO DETERMINE
EROSION RISK IN THE CHIPPEWA RIVER WATERSHED, MINNESOTA. Universityof Michigan. Master’s Thesis
Schwab, G. O., Fangmeier, D. D., Elliot, W. J., andFrevert, R. K. (1993). Soil and Water Conservation Engineering. John
Willey& Sons, Inc., New York, NY USA, 4 edition.
Soil Conservation and Sediment Budgets. Environmental Hydrology. Pg. 257-290
Wischmeir, W.H. SmithD.D. (1978). “Predicating Rainfall Erosion Losses – A Guide to Conservation Planning” . USDA
Handbook537