Does Small-Scale Watershed Land Use Affect Location of
Cyanobacterial Algal Blooms in Milford Lake?
SPATIAL ANALYSIS OF MILFORD LAKE
CYANOBACTERIA BlOOMS
PROJECT GROUP NAME: Biological and
Agricultural Engineering Team 1
TEAM MEMBERS: Erica Schmitz, Margaret Spangler, Sean Clary,
Garrett Cates
DATE: 12-05-2014
Introduction:
The region being studied is Milford Lake, as well as the rivers, drainages and land use
directly surrounding it. In recent years the region has seen an enormous influx of algal blooms,
including hazardous cyanobacterial blooms containing toxins. These toxins include neurotoxins,
hepatotoxins and lipopolysaccharide toxins, depending on the species present (Pitois, 2001).
These toxins have been known to pose potential risk of harm or death to humans in different
water systems across the country. With toxins in a public waterway, risks are posed to human
safety. The goal of the study is to examine the sources of this phenomenon based on its
geospatial location.
Research Question/Hypotheses:
Nutrient rich water drainage from agricultural areas in Kansas may be leading to
expansion of cyanobacteria blooms in flow-contributing reservoirs across the state. Spatial
analysis of areas rich in cyanobacteria may be used to draw conclusions about the agricultural
effect on densities of blooms in these areas. Milford Lake is a reservoir that typically develops
blooms in the summer (Ziegler et.al 2012) and will be analyzed in this project. The project
fulfills a need for exploration of the effect of land use on bloom growth (specifically in Milford).
Spatial data gathered from GIS sources will help determine what areas of the lake are affected by
blooms and which areas have thus far deterred the growth of them. Using this information and
agricultural land use data, a relationship between the bloom areas and non-bloom areas was then
assessed. Initially a more descriptive land cover datum was usable, allowing for comparison
between specific crops, but the scale became unrealistic and a new (more broadly categorized)
raster was obtained that contained more current data than the previous data set. The primary
research questions became: Which areas of Milford Lake are prone to contamination by the
growth and spread of cyanobacteria and what is the spatial relationship of these areas to
land used for agriculture? Does the land use data in surrounding regions provide insight
into the likelihood of dense cyanobacteria populations?
Study Area:
The region being studied was Milford Lake, as well as the rivers, drainages and
agricultural practices surrounding this region. The HUC 12 Delineations in the spatial reference
being studied were Rush Creek, Cane Creek, Tarnum Creek, Timber Creek, and Madison Creek.
This was determined to be the most relevant study area after a discussion about the project Dr.
Dodds, a professor in the biology department at Kansas State University whose research interests
include water quality and algae in freshwater ecosystems. He discussed that the amount of
sedimentation in each separate delineation would have an effect on how the blooms formed and
that it would be difficult to determine how much mixing was occurring throughout the lake
(Dodds, 2014).
Data and Methods:
Data Layers and Procedure:
The geodatabase formed for examination consists of several layers. The National Land
Cover Database 2011-Land Cover-State Extent for Kansas was obtained from the national map
viewer and added to the geodatabase. This layer is a descriptive raster that defines land use
across the state of Kansas in 14 land cover categories. Next the USGS National Hydrography
Dataset for the Lower Republican watershed was added to determine the HUC 12 Delineation of
the Lower Republican Watershed. This is a critical layer which allows for the comparison
between each HUC 12 Delineations in our analysis. Finally a Landsat image showing the
location of cyanobacterial algal blooms was gathered from NASA’s Glovis extension. The
image selected for examination was taken with Landsat 7 SLC-off in October of 2014. It was
chosen because it was a clear day with visible blooms and extremely low cloud cover as shown
in Appendix C . The data from this source was unzipped using a program designed for
decompressing files and was then imported into the ArcGIS model. Then the image was
reprojected (Project Raster Tool) so that it matched the projection (GCS_1984) used for the other
layers.
In order to complete the data analysis required for this project the involved watersheds
were identified, selected in the watershed attribute table , and exported into a separate shapefile
by using the create file from selected features. The HUC 12 Delineations used in the analysis are
shown in Appendix A. Next the clip (data management) was performed for each HUC 12
Delineation that spatially overlaps Milford, geometrically limiting the landcover layer to the
extent of the involved HUC 12 Delineation by creating a separate raster layer for each HUC 12
Delineations, the combination of these raster data layers is shown in Appendix B.
Data Analysis:
Once the data layers were completed for studying, calculations were made to aid in the
review of the gathered material. First a new attribute field was created in each of the HUC 12
Delineations landcover attribute table. The field calculator tool was used to calculate the percent
of each individual land cover of the total land area in the HUC 12 Delineation. The following
equation was used to complete this calculation, [COUNT]*100/(Sum of [COUNT]). The
[COUNT] attribute field contained the number of cells containing a type of land cover. The sum
of [COUNT] attribute field was determined by using statistics option in the attribute table. This
original calculation included open water land cover in its calculation, after analysis of this data it
was determined that another attribute field which did not include open water would aid in the
analysis of the data. Therefore another attribute field was created and the field calculator tool
was used again to create the equation, [COUNT]*100/(Sum of [COUNT]-Open Water [COUNT]
value). The values determined from both of these calculations were used to find the results of
this spatial analysis.
Results:
The watersheds in decreasing order of percentage of cultivated land are Cane Creek,
Rush Creek, Farnum Creek, Timber Creek, and Madison Creek. A qualitative rating was given
to each watershed extent describing its ranking of bloom presence (1 being the most blooms).
The following table describes the findings associated with the precedent rating.
Table 1: Fraction of land covered with cultivated crops and corresponding qualitative rating of visible harmful blooms.
Watershed
Percent Cultivated Land (%)
Bloom Rating
Rush Creek
26.1381
2
Farnum Creek
21.6688
1
Cane Creek
33.2195
4 (Almost None)
Timber Creek
17.8941
4 (Almost None)
Madison Creek
3.1936
3
The watershed with the highest amount of cultivated crops (Cane Creek) did not show
large amount of blooms for undetermined reasons. However, because the two watersheds that
share the majority of the spatial extent of Milford had the highest bloom rating, a new calculation
was performed. The calculation was a percentage of land used for cultivated crops not counting
land covered by water in the denominator. This calculation was designed to take into account
the spatial dependence of each watershed. The table below compares the percent of several land
covers of each shed and the bloom rating associated.
Table 2: Land use by percentage (Excluding Open Water)
Rush
Farnum Mall
Cane
Timber
Madison
Herbercaus
44.8
58.5
48.26
46.26
65.24
81.32
Cultivated Crops
32.73
25.76
37.87
35.39
18.9
3.28
Deciduous Forest
12.3
8.08
5.29
6.64
8.31
6.66
Developed, Open Space
5
4.21
4.49
3.89
4.57
4.52
Hay/Pasture
1.62
0.89
2.15
0.31
0.46
0.24
Emergent Herbaceous
0.45
0.59
.26
4.83
0.36
1.28
Developed, Low Intensity
1.7
1
.61
1.31
1.15
1.12
Bloom Rating
2
1
N/A
4
4
3
Wetlands
Even with the adjusted calculation, the order of magnitudes of percent cultivated land remained
unchanged with Cane Creek showing the most land cover used for agriculture.
Based on our research, there has been no particular correlation between the spatial
distribution of agricultural practices and the presence of densely populated algal blooms
experienced at Milford Lake. The spatial analysis shows that Farnum creek, one of the
watersheds with the least amount of agriculture land use as a percentage of the total, yields the
highest amount of algae blooms recorded. Likewise, over 35% of the land cover surrounding
Cane Creek is used for agricultural practices. This is the second highest agricultural land use
recorded in the testing area and still yields a bloom ranking of 4, the lowest ranking possible.
These blooms, which were originally thought to have been located in watersheds connecting to
high agricultural practices, seem to be un-related. This has changed the reasoning behind the
causes of these blooms and will need to be looked at from a different point of view.
When analyzed with special consideration to water cover the data remained delineated.
The bloom ratings remained constant and the new percentages were relationally the same as the
initially calculated values, that is to say the ranking of agricultural use did not change either.
Conclusions:
The determination that was made is the likelihood of a relationship between agricultural
land use and bloom presence is null. Other factors need to be assessed in order to find the true
cause of the algae blooms. The blooms recorded in Farnum Creek have contradicted the original
hypothesis that agricultural practices are causing these severe and sometimes fatal blooms, by the
excess of nutrients present in the water. Phosphorus and Nitrogen are key elements in the
success of plant growth and reproduction, but they can be released into runoff when land is
fertilized in excess. The analysis did not show a correlation between percentages of agricultural
land in the HUC 12 delineations with location of the cyanobacterial blooms. Further research
was performed on Milford Lake and it was discovered that the residence time of water in Milford
Lake is 12 months (Cunha, 2014). This means that water cycles through the reservoir not
incredibly quickly, but it is still moving through the reservoir. This movement of water would
cause the mixing of nutrients and the source of these nutrients from their respective watersheds
would not contribute to a specific location in the lake. Overall, this spatial analysis has shown
that other factors contribute to the cyanobacterial blooms, such as mixing and sedimentation,
other than small-scale watershed land use. Further research for small-scale watershed land use
could be done on the smaller arms of Milford Lake where mixing with the main waters of the
lake does not occur and the location of the blooms would correspond more closely with the
respective watershed. Comparisons between lakes and their respective watersheds in the same
biome could also be done as well as analyses on soils, slope, and flow accumulation. Additional
areas of study regarding possible promoters of heavy blooming include any local runoffs, lake
conditions, and wind patterns. Lastly, individual biome-altering species should be considered as
a possible source of bloom aggregation.
Zebra mussels in particular, contribute to the high level of water clarity in the reservoir.
These aquatic intruders are filter feeders, which attach to everything and clean the water as they
feed. This high level of water clarity could contribute to greater photic penetration into the water
column, therefore, increasing photosynthesis and reproductive rates. With vast amounts of
essential nutrients, high water clarity for sunlight penetration, and many pockets that are
protected from the wind at all times, Milford Lake seems to be the perfect storm for dangerous
algal blooms. These blooms are caused by a number of factors but research concludes that the
stars have aligned in this particular reservoir, making it one of the most dangerous in the country.
Appendix A:
Appendix B:
Appendix C:
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