1. Kennesaw State University
GEOG 3315 Final
Project
Analysis of soil thickness and water quality sites in Georgia watershed
Paige A Whitfield
12-Nov-14
2. Paige A Whitfield
GEOG 3315
1
Introduction:
My project is going to be oriented around the state of Georgia. I have some data
already that I want to use that include soils, water quality sites and watershed areas.
There is a lot of water studies in this state and a lot of data collected. My data is focused
in the late 90’s and early 2000’s. The datasets are a couple years apart so the data is
basically collected around the same time, still good to perform accurate study and
analysis. The audience of my project will be to any whom it may pertain interested in
this topic. My project will be best aimed for some kind of scientists, or the general public
in this state, or even some young adults in this field of study interested in looking at data
of this kind. Studying the area of Georgia and its water affects areas around this state.
Around the Atlanta area is the largest location in the southeast where most of the water
originates. There are mountains here in this landscape, the Application Mountains. In
order to study water in this area most efficiently, you should look at the location of the
origins of where the water flows. These rivers flow from Georgia into South Carolina,
Florida, Alabama and even Tennessee. So, is there a relationship between thicknesses
of soil and number of water quality sites? Do those two factors show vulnerability to
pollution?
Objectives:
I plan to make 3 data frames for the display of the data. The objectives I have for
my project are to relate and analyze the areas in the state of Georgia between the soil
thickness, the water quality sites and where those are located. I expect to find a relation
of some kind between either the soil thicknesses to the water quality sites locations
within the watershed areas. I expect to find more water quality sites within the
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watershed areas with the highest ratio of soil thickness in square miles to the total soil
coverage in square miles.
Data Acquisition:
I found my data on the Georgia GIS data Clearing House website. All files
downloaded there were e00 files, except for one, the Georgia counties file; the water
quality sites point file, water supply watershed shapefile, the Georgia soils shalefile and
the Georgia counties boundaries shapefile. A few of my layers will be pieces of data I
already had and used in my other assignments, the background file of Canada and
Mexico, that I will use in all of my data frames and the US shapefile of all the states.
One of my layers in one of my data frames was manually created and digitized by
myself, the outline of the state of Georgia in the Locator Map frame.
Methodology:
I plan to find the locations where my data overlap in the special frame and create
a new shape where they share, then create analyses from those. I am to review the
data and look at them in different ways to come up with separate ideas and results that
hopefully agree and make sense. I plan to put the data together in the assorted data
frames to see all the data clearly, then start to create new data from that where the
existing data points towards the same proposition. My new data I create will be only
within the watershed areas of northern Georgia.
Results:
Observations before analysis, I have displayed here, on map one in my appendix, the
red areas are the thinnest layers of soils in the watershed areas. The watersheds are
outlined in black and labeled, with blue dots for water quality sites, the areas in red are
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GEOG 3315
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the most vulnerable and susceptible to erosion in these watersheds, based from
thickness levels of the soils. On another note, these areas with thin layers of soils might
be so because a city is present or mountains are present creating steep slopes and
causing more erosion making soils thinner there, allowing more erosion and chemicals
to be washed into the rivers and waters. Furthermore, in map “BaseMap”, I have dot
locations on the map shown in blue, water quality sites, which are correlated to
watersheds and the environment. I have noticed there are more water quality sites in
the northern part of Georgia where the mountains are located. This might be so
because other areas outside of the large city areas are more non-point sites so more
water quality sites are present. Point sites are locations where pollution comes from one
known location like a pipe disposing waste from a factory, whereas non-point sites are
areas like highway runoff and atmosphere deposition.
Results after analysis, I have made new layers, I am now displaying those in the next
map, “Soil and Water Quality Analysis within Watershed Areas in GA”, map two in my
appendix. In these maps, I made a new layer for both maps, one I symbolized with
graduated symbols and the other I symbolized with graduated colors or a chloropleth
map. With each data frame, I correlated the data layer to the watershed layer, the data
layer either being either the water quality layer or the soil layer. On the graduated color
map, the reds and oranges represent a higher density of square mile of thin soil to the
total square mile of soil. The Greens and yellows have a lower density. Furthermore, on
the graduated symbols map, the larger the symbol means the more water quality sites
are located in the basin. Some of the basins with a higher density of square mile of thin
soil have a lot of water quality sites spread around the basin. This might be because
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GEOG 3315
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higher up in northern Georgia, there are mountains, which have high slopes and the
water quality can be affected by several factors since some of the water comes from
other regions, which would inquire more non-point pollution present in these regions.
Non-point pollution is pollution caused from a larger location and harder to pinpoint
where the pollution is actually coming from.
Conclusion:
There seems to be some kind of relationship with these attributes within the
watersheds. The watersheds in the northern part of Georgia have the most water quality
and the highest ratio of the square miles of the thinnest soil thickness by the total soil of
square miles. Therefore, there is a relationship shown in map 2 in my appendix, the
largest circles on the right map do correlate to the reds and oranges on the left one.
Furthermore as shown in map 3, analysis part 2, shows the watersheds most vulnerable
to erosion.
Limitations and Drawbacks:
The drawbacks, I would have liked to have incorporated some kind of elevation or been
able to show where the mountains in northern Georgia to be located within the
watershed areas. The water quality locations would be more beneficial in areas with low
ability to keep toxins away from water sources, to help monitor the toxic levels in higher
frequency since those areas tend to have more non-point pollution sources. Usually
non-point pollution sources are more seen when no factories are present and most of
the area is surrounded by natural landmasses and other physical properties that are
affected by the atmosphere that covers most of the area being studied.
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GEOG 3315
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Description of how I gathered data and createdthe new data:
In data frame 1, took the watershed layer and clipped the GA counties layer to it, in the
Watershed data frame, to make the study area shape file. Took the new
“WatershedCounties_Clip” and intersected with the “Water Quality Sites” layer and
made a new water quality layer, “WaterQuality_Intersect”, of points only inside the
watershed. Went to data frame 2, Soils, took the same “Water Supply Watershed” layer
and clipped it once again to the counties layer, to make the study area and then clipped
it to the “Soils” layer to make a new “Soils_Intersect” layer that is only inside the
watershed. Dissolved the “WatershedCounties_ClipDF2” to the attribute “Basin” to
make a new layer file with each basin in their own attribute,
“WatershedBasin_Dissolve”. Back in data frame 1, Spatial joined layer
“WatershedBasin_Dissolve” to layer “WaterQuality_Intersect” to make newly joined
spatial layer “Join_WatershedWaterQuality”. In the “Soils_Intersect” layer, selected the
lowest thickness soils and made a new layer file “Soils_LowThick”. Clipped the
“WaterQuality_Intersect” layer to the “Soils_LowThick” layer and made one more layer
of points inside the areas of the lowest soil thickness. Symbolized the
“Join_WatershedWaterQuality” layer previously created by count of points inside their
polygons, then hid the “WatershedCounties_Clip” layer. Then joined layer “GA_Soils” to
excel table. Made new layer with the percentage attributes calculated in excel called
“Thinnest_Soil_Percentages”. Then symbolized that layer by normalized percentages of
square miles of thinnest soils by total amount of square miles of total soil in each basin
name. That gives you map two, in the appendix, from map one.
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Appendix:
Map one – Basemap
Map two – Analysis
Map three – Analysis Part 2
