Average Annual Soil Loss of the Sevenmile Creek Watershed

University of Wisconsin-Whitewater Whitewater Geography Department

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King, Aaron J

With changing regulations and fast growing agriculture, the state of Iowa is currently undergoing soil erosion assessment to determine the impact soil erosion is having on local watersheds. The Sevenmile Creek watershed, located in the loess hills of southern Iowa, a northeastern tributary of the Nodaway River, has been assessed using the modified soil loss equation in order to calculate the estimated annual soil loss (tons/acre).

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 (Schwab et al. 1993). Not only, but research also has provided that fact that 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 to calculate average annual soil loss for watersheds is so important. This can help manage and then develop solutions as to how problems can be dealt with. The Sevenmile Creek, located in the Southern Loess Hills of Southern Iowa will 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, which is a tributary to the Missouri River, which eventually leads to the Mississippi River. The loess hills refers to the type of soil sediment in the area. The dominate way that soil has been deposited in the loess hills is through loess, or wind-blown silt. 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) which is the most commonly seen unit in Iowa.

The loess hills have a distinctive land scape. Its western extent flows into the Missouri River. The topography of the area is sharp with alternating peaks. The silt allows for a dense network of drainage ways, resulting in gullies and ravines. This allows for very pronounced gully erosion. This further affects crop 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 root in the soil solidifies the soils in place. However due 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 will again increase, causing more environmental and economic harm.

The way in which the watershed will be analyzed will be done through the modified annual soil loss equation (RULSE). This equation takes into account several factors that could affect the 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.

Methods and materials:The Sevenmile Creek watershed creek data was downloaded on February 6th, 2014 from the

Natural Resources Geographic Information Systems Library (http://www/igs.uiowa.edu/nrgislibx) (NRGIS) by Aaron King, University of Wisconsin-Whitewater student. 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, with a small section located in Montgomery County. Each county supplied soil data (includes soil description, variables and K-factor) and a DEM.

In order to calculate annual 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 for the average annual soil loss in tons/acre for a given area. A is found by multiplying 6 components together. Each component is a function important to soil loss. R stands for the rain fall and run-off erosivity factor. K stands for the soil erodibility factor. L and S are often combined to make an LS factor, that is the slope steepness and length factor of the given area. C is a cover management factor. P is the conservation practice factor.

The R factor is the rain fall and erosivity factor. The R factor changes depending on the amount of rainfall and the storm precipitation. Each storm is given an index value, EI. E stands for the kinetic energy of the storm. I stands for the 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 for this specific watershed of the Sevenmile Creek was determined from “Predicating Rainfall 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 well as Iowa NRCS data (1999). This value was not a shapefile, rather a value that was plugged into the raster calculator as 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 for the county data. Since the Sevenmile watershed expands into two counties, two different 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

used in the raster calculator, the tool Feature 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, which is the slope length and steepness was done via the Raster Calculator featuring flow accumulation and slope. In order to get the slope of the water shed, the DEM (Digital Elevation model) was downloaded for each of the counties. Once these features were downloaded, they were then merged using the tool, mosaic to new raster. This allows ArcMap to merge the DEMS into a new raster file. Once these were merged, the raster file was clipped according to the watershed basin. Once the 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 forward and backwards the slope elevation goes, was changed to 0.01 to account for 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 calculator is 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 accumulation is plugged in as well as the slope. The cell size, which is 30m,30m (plug in 30) is also plugged in for the resolution. This output will be the LS factor as a raster that can be used in the RUSLE equation.

The C factor is land cover data. This data was downloaded from the watershed data which included the basin cover. Three separate years were downloaded, one from 2002, one from 2000 and one from 1992. In order to get the most accurate data, the 2002 land cover was initially used to determine land cover types, but the file was corrupted. So, as a backup, the 2000 land cover was used. Each specific land cover type was given a value that has been predetermined. This value is a coefficient that allows for the amount of soil erosion each land cover type has. Each value that was plugged into the attribute table in correspondence with a paper “MODELING ALTERNATIVE AGRICULTURAL SCENARIOS USING RUSLE AND GIS TO DETERMINE EROSION RISK IN THE CHIPPEWA RIVERWATERSHED, MINNESOTA” by Elena Doucet-Bëer. (Bëer, 2011, PG. 27)(Breiby, 2006). This report outlines specific land cover types and the C-factor that corresponds with it. In order to insert these into the C-factor shapefile, I opened the attribute table and added a field. This

field was named C-factor. The field was a float, with precision of 5, scale of 4. After the field was added, I opened up an editing session, and plugged in the numbers for each attribute. See Table 1

Rowid VALUE COUNT CLASS C-FACTOR0 1 115 Water 01 2 7606 Forest 0.0022 3 130541 Grass 0.0053 4 123446 Corn 0.64 5 103831 Beans 0.455 6 712 Artifical 06 7 143 Barren 0.3

Table 1. This is the attribute table for the C-factor. These values were used exported and used in the RUSLE equation.

In order select these features, the Look Up tool (Spatial Analyst) was used. This allows you to take a feature in the attribute table for a vector file, and convert the selected attribute to a new raster file that can be used in the raster calculator.

The P factor, which is 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 what the potential soil erosion in the watershed would be.

In order to calculate the 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 will give the estimated average annual soil loss in tons per acre per year.

AnalysisThe watershed basin that was in use is located in the Southern Iowa Loess Hills, located in

Cass and Montgomery Counties see figure. 1. The watershed covers 94668 acres.

Figure 1. This is the area of the Watershed that is being analyzed.

K factor, see fig. 2 was determined via soil data given through the county data. This data has values that range from 0-1. Each soil type that the watershed area is made of is given a value, in accordance to its erodibility. K can vary depending on seasonal variation in soil erodibility as well as after tillage. These values were determined from merge soil data and had been previous collected and put in the attribute table. In this map, the values range from 0 to 0.43 showing a generally high amount of soil erodibility. The soil in the watershed is predominantly loess deposited sands and silts. The darker values show higher K-Factors, predominantly along the creek valley, and the browner the values get, the lower the K-Factor.

LS factor was derived through DEM, Flow Direction, Flow Accumulation, and Slope. The DEM, see fig. 3, shows low spots along the valley of the river, which coincides with the previous base maps. This also shows small smaller creeks that flow out; this is also lower in elevation. The highest points look to be rolling hills, most likely from the deposited loess that dominates the soil of the regions. This also correlates with the base map.

From the DEM the Slope was found. Since the values are in meters, the Z-factor needed to be adjusted in order to properly display the slope of the area in degrees. See fig. 4. The slope corresponds with the DEM of the area, showing low slope values of 0 degrees slope change at creek and highest locations right next to the creek. The high value is approximately 24 degrees. The values show from the headwaters and grow downstream, with the values becoming more frequent towards the confluence. Most of these look like they are the result of soil being deposited as the stream flows downriver.

The DEM was also used to calculate flow direction and later flow accumulation. In the Flow direction, see fig. 5, the lighter values show an increase in the amount of flow, while as the dark it get, the amount of runoff decreases, showing the flow of direction. This map was then used to get flow accumulation, see fig. 6. The lighter the area is for the flow accumulation shows the area with the highest amount of flow accumulation. On the map, this centers in the actual creek, which would be accurate.

The combination of the two maps in the raster calculator gives the LS factor, see fig. 7. The LS factor displays length and steepness of slope as a value. The green areas on the map area are areas that have higher the values. This means that they will be most affected by the slope steepness and length.

C-factor, see fig. 8, contains specific land covers features that have a determined value for soil erosion. The values were broken down to 7 classes, water, forest, grass, corn, beans, artificial, and barren. Each land cover was given a value (see table 2). Water=0, Forest-0.002, Grass-0.005, Corn-0.3, Beans-0.5 and Barren-0.6.

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.

Figure 3. This is the DEM of the Watershed analysis. It has a range of 11486.

Figure 4. This is the slope of the watershed. It ranges from 24 degrees at the highest point to 0 degrees at its lowest point.

Figure 5. This shows flow direction. The Darker values show higher amounts of runoff and movement as opposed to light areas that show very little movement.

Figure 6. The flow accumulation is highest at the Creek due to where the water and soil is deposited.

Figure 7. LS is shown here. This layer will be combined with the other factors to produce the RUSLE equation. This shows slope length and steepness.

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

When all R-Factor (160), K-Factor, LS-Factor, C-Factor, and P-Factor (1) are combined in the Raster Calculator, the result is the average annual soil loss for the watershed. This is the RUSLE equation, see fig. 9. The areas that have are the darkest show the least amount of soil loss. This is mostly in the physical creek itself, which would make sense due to the lack of soil actually in the water. The lighter areas are the areas that have the most soil loss. These are predominately in areas that are higher up, and have the highest slope.

Once the RUSLE equation was calculated, basic statistics were run to determine the average annual soil loss per acre per year. According the data from the RUSLE equation, the sum total of soil loss in a year was 582765.2704 tons. If you divide that by the area of the watershed (94668 acres) there is an estimated value of 6.1 tons per acre per year.

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

Conclusion:Using ArcMap software, R, K, LS, C and P were combined to create the average annual soil loss

equation or the RULSE equation. This was done in 30 by 30 meter resolution and displays accurately the data that was downloaded. With the data that was used in the process of estimating the average annual soil loss, the estimated value ended up being 6.1 tons per acre per year.

The average annual erosion for Iowa is roughly 5.2 tons per acre per year according to Environmental Working Group as a summary of Iowa Soil Loss. This number is deemed slightly higher than sustainable. The soil erosion for the Sevenmile Creek watershed is slightly higher than the average. This could possible not be all human error however. As the report continues, it storms as recent as of 2007 have triggered soil losses that were 12 times greater than the federal average for the state of Iowa. This intensity could be a factor in the R value. Since the value that was used in this report was from a chart that was published in 1978, there is a probability that the erosion is even higher with a more accurate R factor. A good way to check is via the website, which produced a graphic that displays average annual soil loss for all of Iowa, see fig 10.

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

This graphic shows two important details. First, the estimated soil loss that was calculated is in accurate to the point where it should be (between 5.1-10.0). Second, there is also leeway in the average annual soil loss. So an increased R, which would lead to an increase average soil erosion, would put me still in the proper column of 5-10.0. This adds legitimacy to the project.

This RUSLE equation also does not account for a changing P value. The P value helps to determine land cover practices. A value of 1 was used in the equation, which deems that little to no extra practices in order to prevent soil erosion were used. If this experiment were to be redone, then P and R –Factor need to be further examined to further analyze this project.

Since the average annual soil loss is already above the “sustainable” limit, measures need to be taken in order to prevent any more soil loss. This is a trend that is only rising. According to figure 10, there is a high amount of soil loss in the south western part of the state. The best measure to take would be to start implementing more CRP crop land to prevent soil loss.

Since the majority of the soil is wind-blown loess, planting grasses would help prevent the loose soil from coming up. Looking at the C-Factor (fig. 8) you can see the majority of the land it either bean farms, barren or grasses. These all have very high C-factor values, ranging from 0.4-0.6. This could also increase the chance of runoff from overland flow. This could be the reason that the soil erosion is so high. There is no plant material to keep the soil from eroding away. This is important for many reasons. One primary reason is that soil erosion can reduce the productivity of some soils (Lowdermilk, 1953; Shertz et al. 1989 via Soil Conservation and Sediment Budgets). With the reduction of soil, this does adhere to sustainable soils.

Breiby, T. (2006). Assessment of Soil Erosion Risk within 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 University of Minnesota, Winona, MN, Volume 8,

Cox, C., Hug A., Bruzelius, N. (2014) Executive Summary, Environmental Working Group, Losing Ground. http://www.ewg.org/losingground/report/executive-summary.html

Doucet-Bëer, E., (2011) MODELING ALTERNATIVE AGRICULTURAL SCENARIOS USING RUSLE AND GIS TO DETERMINE EROSION RISK IN THE CHIPPEWA RIVER WATERSHED, MINNESOTA. University of Michigan. Master’s Thesis

Schwab, G. O., Fangmeier, D. D., Elliot, W. J., and Frevert, 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. Smith D.D. (1978). “Predicating Rainfall Erosion Losses – A Guide to Conservation Planning” . USDA Handbook 537