NOAA Great Lakes Environmental Research Laboratory

The latest news and information about NOAA research in and around the Great Lakes

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Scientists Work Around the Clock During Seasonal Lake Michigan Cruise

Last month, scientists from GLERL, the Cooperative Institute for Limnology and Ecosystems Research (CILER), and other university partners took the research vessel Laurentian for a multi-day cruise on Lake Michigan as part of seasonal sampling to assess the spatial organization of the lower food web—spatial organization simply means the vertical and horizontal location where organisms hang out at different times of day, and the lower food web refers to small organisms at the bottom of the food chain.

The research goes on around the clock. Scientists work in shifts, taking turns sleeping and sampling. The Laurentian spends a full 24 hours at each monitoring station, sampling vertical slices of the water column. Sampling at these same stations has been going on since 2010, providing a long-term dataset that is essential for studying the impact of things like climate change and the establishment of invasive species.

Sampling focuses on planktonic (floating) organisms such as bacteria, phytoplankton (tiny plants), zooplankton (tiny animals), and larval fishes which feed on zooplankton. Many of the zooplankton migrate down into deep, dark, cold layers of the water column during the day to escape predators such as fish and other zooplankton. They return unseen to warm surface waters at night to feed on abundant phytoplankton. Knowing where everything is and who eats whom is important for understanding the system.

Our researchers use different sampling tools to study life at different scales. For example, our MOCNESS (Multiple Opening Closing Net Environmental Sampling System) is pretty good at catching larger organisms like larval fish, Mysis (opossum shrimp), and the like. The MOCNESS has a strobe flash system that stuns the organisms, making it easier to bring them into its multiple nets.

The PSS (Plankton Survey System) is a submersible V-Fin (vehicle for instrumentation) that is dragged behind the boat and measures zooplankton, chlorophyll (a measure of phytoplankton), dissolved oxygen, temperature, and light levels. Measurements are made at a very high spatial resolution from the top to the bottom of the water. At the same time fishery acoustics show where the fish are. Together, these two techniques allow us to see where much of the food web is located.

Water samples are taken at various depths and analyzed right on the boat. This is a good way to study microbes such as bacteria and very small phytoplankton. The lower food web has been pretty heavily altered by the grazing of quagga and zebra mussels. Specifically, the microbial food web (consisting of microbes such as bacteria and very small phytoplankton) makes up a larger component of the food web than before mussel invasion, and scientists are working to find out exactly how this has happened.

Check out the photos below for a glimpse of life in the field!


Central Michigan University students Anthony and Allie are all smiles as they prepare to head out!


Getting the MOCNESS ready.


Chief scientist Hank Vanderploeg looks at some data.


Filtering a water sample—filtering out the big stuff makes it easier to see microbes.


Paul prepares the fluoroprobe.


Taking a water sample in the presence of a beautiful sunset!


Using Airplanes for Algal Bloom Prediction in Lake Erie

How can airplanes help predict harmful algal blooms (HABs)?

For several years the National Oceanic and Atmospheric Administration (NOAA) has been using satellites to guide HAB forecasts. But, satellites have their limitations. For example, the Great Lakes region can be cloudy and satellite “cameras” can’t see through clouds. In western Lake Erie there are typically only about 20-30 usable cloud-free images during the HAB season, which limits our ability to make bloom predictions. Another challenge with satellites is that the resolution of images makes it difficult for scientists to “see” differences in the types of algae floating on the Lake Erie surface. After a big rainstorm, for instance, it is difficult to distinguish between muddy water flowing in from the Maumee River and algae that is already in the western basin.


The resolution of satellite images makes it difficult to distinguish the types of algae floating on the surface of the water. We can detect different algae in the lake because each algae group (shown above) releases a different color pigment that we can ‘see’/ measure from the hyperspectral sensor.

To improve HABs forecasts, during the past two summers,  GLERL has been partnering with the Cooperative Institute for Limnology and Ecosystems Research (CILER) and Skypics to use a special hyperspectral sensor on an airplane-mounted camera. This weekly airborne campaign is coordinated with the weekly Lake Erie monitoring program. The monitoring program collects samples at multiple stations around western Lake Erie and the hyperspectral sensor captures images from those sampling stations on the same day. Comparing the field collected samples with what the sensor “sees” helps us to understand how well the sensor is working for HAB detection. Additionally, we coordinate with researchers at NASA’s Cleveland office, who are also flying their own airborne imaging sensor, to cross check our results with theirs for even more robust hyperspectral data validation and quality control.

Check out this short video clip of a HAB, taken by pilot, Zach Haslick, from Skypics, as seen from the window of his airplane, while flying the hyperspectral sensor over an area of Lake Erie.

Like satellites, hyperspectral sensors collect information on HAB location and size, but since our weekly hyperspectral flyovers are done below the clouds, the images are much higher resolution compared to satellites. Because of this, the hyperspectral sensors provide more accurate and detailed information on bloom concentration, extent, and even the types of algae present in the lake.

Hyperspectral sensors measure wavelengths, or color bands, released from chlorophyll color pigments in the HAB to detect color pigments that represent different types of algal groups. The process is similar to how the human eye detects wavelengths to create images but the hyperspectral sensor detects bands of wavelengths, or colors, at greater frequencies than what the human eye, or even satellites, can detect. The pigment detection information helps us determine what type of algae is present within blooms and whether or not toxins are present. In the long run, this will help us develop even more accurate HAB forecasts.

Success! This year the hyperspectral sensor detected a bloom that was not detected by a satellite!

On September 19, the hyperspectral flyover captured a HAB scum near a drinking water intake in Lake Erie that wasn’t visible from the satellite. Using the hyperspectral images, along with our HAB Tracker forecast tool to assess the potential of the scum to mix down into the lake (see images below), we were able to provide the drinking water intake manager with an early warning of a potential HAB moving near the intake.



Hyperspectral sensing imagers offer drinking water intake managers a key resource for identifying the type and location of algal blooms near water intake systems, as was demonstrated on September 19. Now that the field season is over we have begun pouring over our data and will incorporate what we learned to improve our HAB Tracker forecast tool and, ultimately, provide better information to decision makers.

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GLERL scientists are also teaming up with other partners to test a variety of ways in which hyperspectral sensors can be useful in detecting HABs. In addition to the manned airplane studies, recently, along with a team from NASA Glenn Research Center and Sinclair Community College, researchers flew a UAS (Unmanned Aircraft System) with a hyperspectral sensor over the lower Maumee River/Maumee Bay area in Lake Erie (see the photo gallery above). Concurrently, researchers from the University of Toledo collected water samples for comparison. Not only useful for tracking HABs, this also demonstrates the successful use of a UAS for other types of environmental monitoring.



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Retrieval of new data from instruments in Manistique River will inform research and decision making

During recent fieldwork, Dr. Philip Chu, scientist at NOAA’s Great Lakes Environmental Research Laboratory (GLERL) and Professor Chin Wu, from the University of Wisconsin Madison, retrieved six water level sensors and one Acoustic Doppler Current Profiler (ADCP) from the Manistique River—a 71.2 mile long river in the Upper Peninsula of Michigan that drains into Lake Michigan.

An ADCP measures water currents with sound by using the Doppler effect— sound wave has a higher frequency, or pitch, when it moves toward you than it does when it moves away. Think of the Doppler effect in action the next time you hear a speeding train pass you by. As the train moves toward you, the pitch of its whistle will be higher. As it moves away, it will be lower. The same effect happens as sound moves through water. The ADCP emits pulses of sounds that bounce off of particles moving through the water. Particles that are moving toward the sensor will produce a higher frequency than those moving away from the sensor. This effect allows the profiler to record data about sediment transport in the river.

After quality control and assurance procedures back in the lab, currents and water level data collected during this deployment, scientists will use the information to research the impacts of meteotsunamis, seiches, and flooding events on sediment transport through the river. The outcomes of this research will then will be used by organizations, such as the U.S. Army Corps of Engineers, for dredging operations on the river with the ultimate goal of improving water quality. (See the Great Lakes Water Quality Agreement for more on why the Manistique River is considered an “Area of Concern.”)

In addition, researchers will use this valuable field data while validating the NOAA next generation Lake Michigan-Huron Operational Forecasting System, one of the forecast systems within the Great Lakes Operational Forecasting System, or GLOFS. GLOFS is a prediction system that provides timely information to lake carriers, mariners, port and beach managers, emergency response teams, and recreational boaters, surfers, and anglers through both nowcast and forecast guidance.

Nowcast vs. Forecast: What’s the difference?

A nowcast is a description of the present lake conditions based on model simulations using observed meteorology. Nowcasts are generated every 6 hours and you can step backward in hourly increments to view conditions over the previous 48 hours, or view animations over this time period.

A forecast is a prediction of what will happen in the future. Our models use current lake conditions and predicted weather patterns to forecast the lake conditions for up to 5 days in the future. These forecasts are run twice daily, and you can step through these predictions in hourly increments, or view animations over this time period.

Professor Wu, along with Dr. Eric Anderson from GLERL, deployed these sensors earlier this summer. As with the majority of GLERL’s projects, this is a collaborative effort. Through the Cooperative Institute for Limnology and Ecosystems Research (CILER), this work is supported by NOAA National Marine and Fishery Service and funded by EPA Great Lakes Restoration Initiative. The University of Wisconsin is one of ten CILER Consortium partners.