Sediment Production
Brad Taylor
CEE 5440 :
Last Updated
Introduction
With
increasing development of forested land; as well as natural causes including
fire, rainfall, and snowmelt runoff; an increase of erosion can greatly affect
not only human developments but also entire aquatic ecosystems downstream of
increased erosion.
Soil erosion is a
natural process that averages 0.2 tons per acre. The loss rate is accelerated to 0.5 tons per
acre for managed forests, 1.5 to 20 tons per acre for pasture and cultivated
lands, and 150 to 200 tons per acre for unprotected construction sites. Sediment is the number one polluter of
As houses and other
buildings are constructed higher and higher up the mountainside and mountain
roads are continually constructed, erosion will become an increasingly great
problem that needs to be dealt with in a way that is favorable to both the
environment and development.
Background
As
a group we decided to do an erosion study on
A
portion of
http://www.utahfireinfo.gov/firephotos/index.htm
MUSLE Equation
As a group we decided to evaluate
Each member was
responsible for one of the different elements in the equation.
Ys=a*[Q*qp]β*K*LS*CP*SDR
Ys – Total Tons per Event
Q – Storm Runoff
(acre-ft) - Ryan McBride
qp – peak runoff (cfs)
a,β – storm
factors
Typical Rain Storm
a=95
β=.56
Snow Melt Flood
a=120
β=.3
K – Soil Erodibility
Factor - Brad Taylor
LS – Slope Factor
(length and steepness) - Jeff Jensen
CP – Cover and
Management Practice Factor - Iosefa Matagi
SDR – Sediment
Delivery Ratio
FOR INFORMATION ON
THE OTHER FACTORS PLEASE LINK TO MY TEAM MEMBER’S WEBPAGES
K Soil Erodibility Factor
I was responsible for obtaining the K
(Soil Erodibility Factor) value of the equation for the Farmington Canyon
Watershed. A good definition of the K
factor is as follows:
K factor is soil
erodibility factor which represents both susceptibility of soil to erosion and
the rate of runoff, as measured under the standard unit plot condition. Soils
high in clay have low K values, about 0.05 to 0.15, because they resistant to
detachment. Coarse textured soils, such as sandy soils, have low K values,
about 0.05 to 0.2, because of low runoff even though these soils are easily
detached. Medium textured soils, such as the silt loam soils, have a moderate K
values, about 0.25 to 0.4, because they are moderately susceptible to
detachment and they produce moderate runoff. Soils having a high silt content
are most erodible of all soils. They are easily detached; tend to crust and
produce high rates of runoff. Values of K for these soils tend to be greater
than 0.4.
Organic matter reduces erodibility because it reduces the susceptibility of the
soil to detachment, and it increases infiltration, which reduce runoff and thus
erosion. Addition or accumulation of increased organic matter through
management such as incorporation of manure is represented in the C factor rather
than the K Factor. Extrapolation of the K factor nomograph beyond an organic
matter of 4% is not recommended or allowed in RUSLE. In RUSLE, factor K
considers the whole soil and factor Kf considers only the fine-earth fraction,
the material of <2.00mm equivalent diameter. For most soils, Kf = K.
Soil structures affects both susceptibility to detachment and infiltration.
Permeability of the soil profile affects K because it affects runoff.
Although a K factor was selected to represent a soil in its natural condition,
past management or misuse of a soil by intensive cropping can increase a soil's
erodibility. The K factor may need to be increased if the subsoil is exposed or
where the organic matter has been depleted, the soil's structure destroyed or
soil compaction has reduced permeability. A qualified soil scientist can assist
in making this interpretation.
From Technical
Guide to RUSLE use in
http://www.iwr.msu.edu/rusle/kfactor.htm
Using ARC Map in GIS the watersheds were broken up into
catchments. The DEM of the area was
obtained and through the use of TauDEM and ARC Hydro tools individual
catchments were established.
It was then my job to
find the average K-value for each catchment.
I went to the STATSGO webpage to get soil information relevant to the
particular webpage. Information was
downloaded for Davis and Morgan counties.
I had a lot of information in both counties up to where the Mountainous
area started. The there wasn’t any information
so I wasn’t able to use STATSGO. (The DEM of the Watershed appears in green and
the area I needed in the yellow circle.)
I
then tried to get soil information from SSURGO but ran into the same type of
problem that data for the valleys existed but not for the mountainous area.
(Grey areas not available to download.)
Without being able to
download information pertaining to the needed watershed we found our own
data. On a trip to
Originally the
samples were taken to check Soil Distributions and verify K values obtained
from STATSGO but with no information available we needed to obtain K values for
the different catchments. It would have
been better if we would have had taken a lot more samples, but the time
involved with analysis of more samples was more than we could do. The soils were sieved in the Soils Lab and
soil distributions for each were calculated.
After soil
distributions were calculated; and values were assigned to each sample based on
Percent Organic Matter, Permeability, and Soil Structure; K values were calculated
from a chart. The K values are below.
Using the Create
Features packet we received in class I was able to create a new feature class “SoilPoints”
for our geodatabase and put the factors I calculated into the feature
class. I was then able to assign a
different color to each value from the different sample locations (Triangles).
Although I didn’t
have very many points to work from, the range of K values wasn’t very great
(0.08-0.15). I assigned each catchment a K value interpolating a K value for
those catchments without a sample.
Conclusion
Calculated
K values were used from each catchment, as well as the other values calculated
by my team members and applied to the watershed area. We were then able to calculate the Ys
value (Total tons per event) for the Farmington Canyon Watershed for 2
different events.
Final Numbers
•
Sediment yield for
Snowmelt Flood
10373 tons
•
Sediment yield for
1-inch rainstorm over 6 hour interval
8539 tons
In
future work we would like to look at the capacity of existing sediment
diversion facilities and see if they are adequate to successfully contain
potential sediment floods. We would also
like to refine our process and try to calculate sediment yield in each cell
using the raster calculator or other methods to get a more accurate
representation of Sediment Yield. It
would also be beneficial to get more soil samples from each catchment to give
us a better idea of K-values.
