1. Spatial Early Warning Indicators of
Blue Green Algae Blooms (BGA)
ANDERS UPPGAARD
AUGUST 18, 2015
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2. Protecting our inland lakes
• Our inland waters are very
valuable
• Drinking water, fishing, diverse
organisms
• Lake Mendota, Great Lakes are
having algae bloom issues
• Want to predict and stop harmful
algae blooms
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http://blooms.uwcfl.org/wp-
content/uploads/2009/12/mendota-monona.jpg
4. Project Focus: Statistical Early
Warning Indicators of Harmful
Algae Blooms
Methods:
◦ Fertilizing Peter and Tuesday lake
with nutrients, and comparing their
ecological health to Paul lake
(reference)
Data collection:
Temporally: using sondes and
manual water collection
(stationary buoys)
Spatially: using boat equipped
with sonde to collect spatial data
from each lake (FLAMe)
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5. My Research Focus
◦ Can the spatial data
from FLAMe be used
to predict a regime
shift?
◦ Will the variance shift
as the lake shifts
toward an algae
bloom?
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7. 7
Signal Component
Wave A
Component
Wave B
…N
Component
Waves
…
DFT (discrete fourier transform)
The signal we investigated was the spatial variance of
BGA in Peter Lake as a bloom was induced
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High
Amplitude
Component Wave A
Component Wave B
Low
Amplitude
Low
Frequency
High
Frequency
Each component wave has
a characteristic frequency
and spectrum
From only physical
processes, we expect
a relationship
between spectrum
and frequency to have
a -5/3 slope
Frequency
Spectrum
A
B
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How does the relationship between
frequency and spectrum change as the
lake approaches an algal bloom?
Frequency
Spectrum
Hypothesis: Red shift
a move to lower
frequencies as
spectral density
increases
Spectrum: signal
strength at a given
frequency
t1
t2 t3
10. Red Shift = Higher Spectrum at
lower frequency
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11. Conclusion
1. There is a lot of spatial variability for BGA, Dissolved
Oxygen, and Chlorophyll even in small lakes
2. During the bloom, there was a red shift in variance,
and Peter lake became less patchy
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12. Acknowledgments
A huge thank you to everyone who made my project possible!
•Lee Zinn – Funding
•Vince Butitta and Patrick Dowd – FLAMe Crew
•Steve Carpenter, Mike Pace, Jon Cole – Pis
•Cal Buelo, Jason Kurtzweil, Grace Wilkinson – Cascade Supervisors
•Shannon Long, Rachel Meulman, Colin Dassow – Cascade Undergrads
•Everyone else who helped with FLAMe!
•UNDERC
•University of Wisconsin, Madison
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Editor's Notes
My project focused on finding early warning indicators of harmful algae blooms, specifically blue green algae
-our inland waters are very valuable- hold many diverse organisms, are used by humans for fishing, watersports, drinking water
-places like Lake Mendota, the great lakes, are experiencing algae blooms that kill fish and make water undrinkable
-Predicting algae regime shifts is the first step toward learning how to stop them
To study harmful algae, we needed to grow it. We added nitrogen and phosphorous nutrients everyday to Peter Lake, and did not add any nutrients to Paul lake (the reference lake). Adding these nutrients stimulated algae to bloom, which can be seen in the photos above. Peter became filled with blue green and green algae. The pictures were taken from our drone 500+ feet in the air.
-Using sondes, which are automated measuring devices that can detect concentrations of dissolved oxygen, phycocyanin (blue green algae), etc. We used two different methods of sonde location. First, temporally, using sondes mounted on stationary buoys in the middle of each lake. Secondly, we took spatial measurements using a sonde mounted inside a boat. The boat was then driven all over the lake, collecting data of each lake’s variability. This second method was done with the FLAMe project.
Is FLAMe able to predict algae blooms? FLAMe is the spatial way of measuring the lakes, with the sonde in the boat. The featured picture is Patrick Dowd and myself on the FLAMe boat flaming Peter Lake.
Will variance shift toward lower frequencies as the lake moves toward an algae bloom, this is called a red shift. This will be answered using statistics.
-Flame focuses on capturing variability in space
-at time zero, is flame on (when we start flaming the lake)
-light colors show higher concentrations of BGA
-at 8 minutes, you can see a rise in BGA- shows boat is hitting high concentration patch
-in this video, you can see how the heat map is created. The sonde time stamps each data point, which is then matched up to the time stamped GPS coordinates.
-we drive the boat left to right, and then up and down. This overlap is done to double check that the data we collected on the first pass is accurate.
-it would be expected that this circular, 300 yard wide lake would have little to no variability, but in fact there is variability (patches) throughout the lake. These patches can be seen in the heat map, where some areas have higher or lower concentrations of BGA, dissolved oxygen, chlorophyll, etc.
The signal that I investigated was the spatial variance in BGA in Peter Lake measured by the FLAMe twice a week over the course of this experiment.
A discrete fourier transform identifies component waves, that when superimposed, replicate the signal. There can be many component waves, but in the example moving forward, I will just be using 2 waves.
Each component wave of the signal has a characteristic frequency, or period, and spectrum, or amplitude. Component wave A has a low frequency and a high spectrum, and component wave B has a high frequency and a low.
If the characteristics of these waves were plotted on a graph of frequency vs. spectrum, it would look like this. Again, there are many component waves, but in this example I am only using two. The relationship between frequency and spectrum among all of the component waves at a time point can be fit with a line and we can track the change in the slope over time.
As a lake moves toward a bloom, the slope of each line is expected to shift toward lower frequencies. This gives it a steeper slope, causing a red shift. We can see it here in this example, but would it work in real life?
-Theory suggests that a -5/3 slope relates to physical mixing properties only
-what we saw both before and after the bloom were these bumps in frequency that relate to patches in the lakes
-when the algae bloom began, the green line (177) had a higher spectrum at lower frequencies
-The DFT theory works, and it showed up as a red shift in our measurements
Takeaway- we found that flame does capture variability, even in these tiny, circular lakes. We also discovered that we can use DFT with actual data to find a red shift
