NOAA Great Lakes Environmental Research Laboratory

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Underwater robots significantly advance our ability to study Lake Erie’s harmful algal blooms

Newly published research from the NOAA Great Lakes Environmental Research Laboratory (GLERL), the Cooperative Institute for Great Lakes Research (CIGLR), and partners reveals that using underwater robots could significantly advance scientists’ ability to study the harmful algal blooms (HABs) that appear in the Great Lakes and oceans every summer. You may remember reading about NOAA’s collaborative fieldwork in 2019 that used these robots to detect toxins in Lake Erie’s harmful algal bloom. Three years later, the findings from this pioneering research come bearing good news!

This autonomous underwater vehicle (AUV), known as “Makai,” visited the Great Lakes from the Monterey Bay Aquarium Research Institute (MBARI) to help scientists study Lake Erie’s harmful algal bloom. Credit: Steve Ruberg, NOAA GLERL

What are HABs, and how do we study them?

HABs occur when colonies of algae grow out of control and produce toxic or harmful effects on people, fish, shellfish, marine mammals and birds. Western Lake Erie in particular has been plagued by intensified HABs over the past decade. These blooms consist of cyanobacteria, or blue-green algae, which are capable of producing toxins that endanger human and animal health, compromise drinking water supplies, foul coastlines, and impact communities and businesses that depend on the lake. 

Harmful algal bloom in western Lake Erie in October, 2011. Credit: NOAA Great Lakes CoastWatch

The underwater robot used in this research project is known as a long-range autonomous underwater vehicle, or LRAUV. As the name suggests, the LRAUV is built to travel long distances beneath the water’s surface, collecting data for an extended period of time. LRAUVs are useful research tools, as they can collect high-quality data more efficiently and cost-effectively than scientists taking samples from a ship or along the shore. They can be deployed day and night in all weather conditions, and can provide more detailed information to researchers and drinking water managers than other monitoring methods.

For this project, NOAA and CIGLR teamed up with the Monterey Bay Aquarium Research Institute and university partners to equip an LRAUV with a 3rd Generation (3G) Environmental Sample Processor (ESP) — a mobile version of what has previously been known as NOAA’s “lab in a can.” The 3G ESP’s job is to measure microcystin, a potent liver toxin produced by the cyanobacteria that cause harmful algal blooms in the Great Lakes. In just a few hours, the 3G ESP can collect and analyze water samples from the bloom with the same methods that scientists use to analyze samples back at the lab. It does this with the use of ‘omics, a collective suite of technologies used to analyze biological molecules such as DNA, RNA, proteins, or metabolites. These technologies can be used to identify the algal species that produce HABs, understand their behavior, and predict shifts in their population structure.

NOAA and partners deployed the LRAUV-3G ESP in Lake Erie to autonomously measure microcystin, a potent liver toxin produced by the cyanobacteria that cause harmful algal blooms in the Great Lakes. Photo credit: NOAA

Did this robot step up to the challenge?

Before widely adopting the use of the LRAUV-3G ESP to study Lake Erie HABs, scientists had to ensure that the data these instruments collect is accurate and reliable. A main goal of the new publication was to assess how dependable the LRAUV-3G ESP’s data is compared to data that was collected and analyzed by humans.

The authors used a variety of parameters to assess the vehicle’s performance of ‘omics tests on samples it collected from the HAB. They ultimately found that the LRAUV-3G ESP successfully performed flexible, autonomous sampling across a wide range of HAB conditions, and the results indicated equivalency between autonomous and manual methods. In fact, no significant differences were found between LRAUV-3G ESP and manual sample collection and handling methods in the 12 parameters tested. In other words, this robot passed the test!

Left: Scientists retrieve the LRAUV-3G ESP from its mission to measure algal toxins in Lake Erie. Photo credit: NOAA AOML. Right: First author Paul Den Uyl (CIGLR) CIGLR retrieves 3G ESP filters for analysis of Lake Erie microbial community DNA. Photo Credit: Kelly Godwin.

One of the most exciting aspects of this research is that it shows that scientists can use an autonomous sampling platform to replicate traditional ship-based sampling, and they can do so in a particularly challenging environment (Lake Erie’s shallow western basin) where HABs are a serious health concern. Using this instrument in Lake Erie’s shallow waters presented another challenge for the scientists involved. In response to the lake’s challenges, researchers worked on the LRAUV’s buoyancy to ensure that the instrument didn’t drag across the ground. With this technology – sampling DNA and measuring toxins on an autonomous platform – NOAA and partners may be able to provide an early warning system for HABs in the future.

The 3rd Generation Environmental Sample Processor demonstrates engineering advancements from the first and second generation ESPs. Photo credit: NOAA GLERL.

Partners on this research came from far and wide to conduct this important research:

  • National Oceanic and Atmospheric Administration (NOAA)
    • NOAA Atlantic Oceanographic and Meteorological Laboratory (AOML), Ocean Chemistry and Ecosystems Division
    • NOAA Great Lakes Environmental Research Laboratory
    • NOAA National Centers for Coastal Ocean Science (NCCOS)
    • NOAA Southwest Fisheries Science Center
  • Cooperative Institute for Great Lakes Research (CIGLR), University of Michigan
  • Northern Gulf Institute, Mississippi State University
  • Monterey Bay Aquarium Research Institute (MBARI)
  • Department of Earth and Environmental Sciences, University of Michigan

Explore more photos of this research on NOAA GLERL’s Flickr page.


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From safe drinking water to sustainable fisheries, NOAA GLERL’s Experimental Lake Erie Hypoxia Forecast is even more useful than anticipated

Four years ago, NOAA’s Great Lakes Environmental Research Laboratory (GLERL) and the Cooperative Institute for Great Lakes Research (CIGLR) began providing an Experimental Lake Erie Hypoxia Forecast Model to warn stakeholders of low-oxygen upwelling events that can cause water quality problems for over 2 million residents of northern Ohio. Now in its fifth year, this forecast model has turned out to serve additional purposes that NOAA’s scientists hadn’t even considered – including maintaining sustainable fisheries and solving a smelly mystery!

Water intake crib off the coast of Lake Erie in Cleveland, Ohio. By forecasting potential hypoxic upwelling events that could impact water quality, NOAA GLERL’s Experimental Hypoxia Forecast Model helps drinking water plant managers be prepared to adjust their treatment processes as needed.

Providing critical warnings to keep drinking water safe

Hypoxia – a state of low oxygen – occurs in the deep waters of Lake Erie’s central basin in July through September of most years. Low-oxygen water is an unfavorable habitat for fish, and may kill bottom-dwelling organisms that provide food for fish. While the hypoxic water generally stays near the lake floor, changes in wind and water currents can create upwelling events, in which this zone of low oxygen is brought to the surface along the coast.

Once it creeps into shallower parts of the lake, hypoxic water can upset drinking water treatment processes at water intakes along the shoreline. Hypoxic upwelling events cause rapid changes in water quality variables such as temperature, pH, dissolved organic matter, iron, and manganese. To maintain the quality of treated water, plant managers must adjust treatment in response to these changes. NOAA GLERL’s Experimental Hypoxia Forecast Model provides several days of advance notice that water quality is changing, so that drinking water plant managers can be prepared to adjust their treatment processes as needed.

This infographic from NOAA GLERL describes how hypoxia occurs in large bodies of water like the Great Lakes.

Plot twist: Benefiting more than just our water supply

NOAA GLERL’s Experimental Lake Erie Hypoxia Forecast has proven to be incredibly successful in its original goal – but our scientists were surprised to learn that its usefulness didn’t stop there. Recent stakeholder interviews conducted by CIGLR Stakeholder Engagement Specialist Devin Gill revealed that, in addition to helping manage the drinking water treatment process, the forecast has also become an unexpectedly vital tool for managing Lake Erie’s fisheries. 

One agency that makes use of the experimental hypoxia forecast is the Ohio Department of Natural Resources (DNR). The Ohio DNR is responsible for generating population estimates for Lake Erie’s yellow perch and walleye – estimates that ultimately help determine official catch limits to maintain the lake’s sustainable fisheries. 

“Large aggregations of fish may seek refuge at the edges of the hypoxic zone,” says Ann Marie Gorman, a fisheries biologist with the Ohio DNR’s Fairport Harbor Fisheries Research Station. “Our office tracks the location of the lake’s cold bottom water using the NOAA GLERL Hypoxia Forecast Model, and we may modify the timing of some of our surveys to minimize the potential impact of hypoxia on the results. Overall, the NOAA GLERL Hypoxia Forecast Model has become an integral tool for our survey planning.”

Understanding fish behaviors in response to hypoxia is important to conducting accurate population surveys of Lake Erie’s fish species. The ability of NOAA GLERL’s hypoxia forecast to warn fisheries managers of potential survey bias from these hypoxic events helps to save time, money, and energy that may have otherwise been used to conduct unsuccessful trawling surveys in hypoxic zones.

NOAA GLERL’s Experimental Hypoxia Forecast Model helps to guide the planning of trawling surveys like this one conducted by the Ohio Department of Natural Resources. Consulting the forecast helps the Ohio DNR to minimize the potential impact of hypoxia on survey results, which are used to set catch limits that keep Lake Erie’s fisheries sustainable. Photo credit: Ohio Department of Natural Resources.

Richard Kraus, a supervisory research fish biologist with the United States Geological Survey (USGS) Great Lakes Science Center Field Station in Ohio, also uses the experimental hypoxia forecast for his work with Lake Erie’s fisheries. Kraus explains that in Lake Erie, several cold-water fish species rely on finding refuge in colder, deeper waters of the lake – waters that are not impacted by warmer summer air temperatures. However, the presence of hypoxic zones in these deeper waters can impact how much refuge is available for these fish. As hypoxia reduces refuge habitats for cold-water species, chronic effects on growth and reproduction may develop, and in severe circumstances fish kills sometimes occur. The NOAA GLERL Hypoxia Forecast Model is instrumental in predicting where these potential ecosystem impacts could occur, in turn helping fisheries managers determine sustainable catch limits for each fish species in question.

The experimental forecast was also found to be useful to commercial and recreational fishers, who use the forecast to gauge the distribution of yellow perch in relation to hypoxic zones. Fishers can utilize the forecast on a daily basis to determine where to launch their boats, and where to search for aggregations of fish, depending on the hypoxia forecast for that day.

Plus, it’s not just routine fisheries management and recreation that the Experimental Hypoxia Forecast helps improve. In early September, it helped solve the mystery of a strange, foul smell coming from Lake Erie near Cleveland, Ohio, and fish kills associated with it. These phenomena resulted in many public inquiries regarding suspected gas leaks or pollutant spills. Thanks to the forecast, public officials knew that an upwelling of hypoxic water had recently occurred, likely carrying sulfur and nitrogen compounds that caused the stench, and were able to quickly eliminate other possible causes.

Half a decade in the making

Since it began in 2017, this NOAA project has grown into much more than just a computer model. The Experimental Lake Erie Hypoxia Forecast model was developed as a five-year project (2017-2021) with funding from NOAA’s Coastal Hypoxia Research Program, and is an extension of the Lake Erie Operational Forecasting System at NOAA’s Center for Operational Oceanographic Products and Services. Co-led by NOAA GLERL research scientists Drs. Mark Rowe and Craig Stow, and CIGLR’s Dr. Casey Godwin, project scientists provide an email update to public water systems, fisheries managers, and other stakeholders ahead of likely hypoxic events that contains links to the experimental forecast website and other useful NOAA webpages.

Map from the NOAA GLERL Experimental Lake Erie Hypoxia Forecast Model showing predicted change in Lake Erie temperature (top) and dissolved oxygen (bottom) during a three-day hypoxic upwelling event from August 31 to September 2, 2021.

Partners on this project include Ohio public water systems (including the cities of Cleveland and Avon Lake), NOAA’s National Ocean Service, and the Great Lakes Observing System. Special thanks to Devin Gill from the Cooperative Institute for Great Lakes Research for contributing stakeholder interview findings for this article.


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Looking back: The ups and downs of Great Lakes ice cover in 2021

Ice formations cover a pier on the Lake Michigan shoreline in Holland, MI. February 27, 2021. Credit: Clarice Farina.

It’s no secret that the Great Lakes had a wild ride in terms of ice cover this past winter. From a slow start that led to near-record low ice cover in January, to the sudden widespread freeze just a few weeks later, here’s a look back at how ice cover on the lakes has fluctuated during the 2020-2021 ice season.

As we highlighted in our last blog post on historic ice data, January 2021 had the second-lowest overall Great Lakes ice cover on record since 1973 (with the very lowest being January 2002). For all five individual lakes, January 2021 was in the top five lowest ice-cover Januarys since 1973.

This graph shows average Great Lakes ice cover for the month of January every year from 1973 to 2021, organized by lowest ice cover (far left) to highest ice cover (far right). Credit: NOAA GLERL.

Starting out at 10.65% on February 1st, ice cover rose dramatically over the next three weeks with the region’s extreme cold weather. Growing quickly and steadily, total Great Lakes ice cover finally topped out at 45.84% on February 19th. But with air temperatures warming back up shortly afterwards, this spike was short-lived. Within a week it was back down to around 20% and continued to taper off, falling below 1% on April 3rd and reaching 0.1% on April 20.

This graph shows Great Lakes ice cover in 2021 (black line) compared to the historical average ice cover from 1973-2020 (red line). Credit: NOAA GLERL.

This Winter vs. The Long-Term Average

While all five lakes were far below their January average, each one did something a little different during February, when compared to its 1973-2020 average. The following graphs show this winter’s ice cover (black line) vs. the 1973-2020 average (red line) for each lake.⁣

Lake Erie ice cover jumped dramatically up to 81% in the second week of February, well above its average seasonal peak of around 65%. It stayed above 75% for about two weeks until falling back down below its average at the beginning of March.


Lake Michigan ice cover increased steadily throughout February, with its highest percentage being 33% on February 18th — only briefly staying above its average for that time period. It dropped off quickly the following week, then decreased gradually throughout March.

Lake Superior spent about a week in mid-February above its average ice cover for those days, peaking at about 51% on February 19th. Similar to Lake Michigan, it only stayed above its average for a short interval before rapidly falling back down under 20%.

Lake Ontario ice cover took a while to ramp up, staying below 10% until mid-February. It reached maximum ice cover on February 18th, topping out at about 21% – slightly higher than its average for that day.


Lake Huron was the only lake that did not reach above-average ice cover for the entire winter. Its peak ice cover was 48% on February 20th, which was about the same as its average for that time of year.

Melting into Spring

Throughout March, ice cover on all five lakes continued to decrease steadily, with the exception of a spike in ice cover around the second week of the month likely due to fluctuations in air temperature. For Lakes Erie and Ontario, this short-lived jump was enough to get them back up near their average early March ice cover for a few days. 

As for the timing of each lake’s peak 2021 ice cover compared with the average, Lakes Erie, Michigan, Huron, and Ontario all peaked later than their average, while Lake Superior is the only one that peaked earlier than its average.

Ice covers the Lake Huron shoreline in Oscoda, MI on February 15, 2021. Credit: G. Farina, NOAA GLERL.

This winter’s maximum seasonal ice cover of 45.8% is just 7.5% less than the long-term average of 53.3%. While it’s below the average, it’s still more than double the 2020 seasonal maximum of 19.5% ice cover, but is just over half the 2019 seasonal maximum of 80.9%. With so much year-to-year variability, forecasting ice cover each year can be incredibly difficult. NOAA GLERL’s experimental ice forecast, updated in mid-February, predicted Great Lakes ice cover in 2021 to peak at 38% – not too far off from what it really was. NOAA GLERL continues to analyze both current and historical data to refine the ice forecast model, working to actively improve our experimental Great Lakes ice forecast each year.

This graph shows annual maximum ice cover on the Great Lakes each year from 1973 to 2021. Credit: NOAA GLERL.

For more on NOAA GLERL’s Great Lakes ice cover research and forecasting, visit our ice homepage here: https://go.usa.gov/xsRnM⁣

⁣Plus, access these graphs plus more Great Lakes CoastWatch graphs & data here: https://go.usa.gov/xsRnt⁣

Flat, jagged pieces of ice float in Lake Huron near Oscoda, MI on February 15, 2021. Credit: G. Farina, NOAA GLERL.
photo of building in water with skyline of city in backgroun


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NOAA and partners team up to prevent future Great Lakes drinking water crisis

A new video SMART BUOYS: Preventing a Great Lakes Drinking Water Crisis released by Ocean Conservancy describes how NOAA forecast models provide advance warnings to Lake Erie drinking water plant managers to avoid shutdowns due to poor water quality.

An inter-agency team of public and private sector partners, working with the Cleveland Water Department, are addressing drinking water safety for oxygen depleted waters (hypoxia). By leveraging NOAA’s operational National Weather Service and National Ocean Service forecast models and remote sensing for the Great Lakes, NOAA’s latest experimental forecast models developed by its Great Lakes Environmental Research Laboratory can predict when water affected by harmful algal blooms and hypoxia may be in the vicinity of drinking water intake pipes. Advance notice of these conditions allows water managers to change their treatment strategies to ensure the health and safety of drinking water.  

“Hypoxia occurs when a lot of organic material accumulates at the bottom of the lake and decomposes. As it decomposes, it sucks oxygen from the water, can discolor the water and allow for metals to concentrate,” explains Devin Gill, stakeholder engagement specialist for NOAA’s Cooperative Institute for Great Lakes Research, hosted at the University of Michigan.

Low dissolved oxygen on its own is not a problem for water treatment. However, low oxygen is often associated with a high level of manganese and iron in the bottom water that then leads to drinking water color, taste, and odor problems. In addition, the same processes that consume oxygen also lower pH and, if not corrected, could cause corrosion in the distribution system, potentially elevating lead and copper in treated water.

“Periodically, this water with depleted oxygen gets pushed up against the shoreline and the drinking water intakes pipes,” said Craig Stow, senior research scientist for NOAA’s Great Lakes Environmental Research Laboratory. “We have buoys stationed at various places and those guide our models to let us know when conditions are right for upwellings that would move this hypoxic water into the vicinity of the drinking water intakes.”

NOAA provides advanced warning of these events so that drinking water plant managers can effectively change their treatment strategies to address the water quality, which is a huge benefit in the water treatment industry.

For more information on NOAA GLERL’s harmful algal blooms and hypoxia research, visit www.glerl.noaa.gov/res/HABs_and_Hypoxia.

A buoy near the Cleveland Water intake—approximately 3.5 miles off the Cleveland shoreline—gives researchers at NOAA GLERL the ability to incorporate water temperature, pH, turbidity, dissolved oxygen and other parameters into their Experimental Hypoxia Forecast model. This allows water treatment managers enough time to anticipate and respond to changes in lake water quality before it is drawn into their treatment plants. Photo Credit: Ed Verhamme, Limnotech
CIGLR Mechanical Technician, Russ Miller, and Research Associate, Dack Stuart retrieve a sediment core from Lake Erie, near the Cleveland Water Intake Crib. Photo Credit: CIGLR
CIGLR Mechanical Technician, Russ Miller, prepares to deploy temperature and dissolved oxygen sensors in Lake Erie, near the Cleveland Water Intake Crib. Photo Credit: CIGLR
Retrospective animation of the Experimental Lake Erie Hypoxia Forecasts from the 2018 season, showing the predicted change in bottom temperature and dissolved oxygen.
Cleveland skyline and Cleveland Water Intake Crib. Daily, about 165 million gallons of water leaves Lake Erie through the crib to the pump station, where the water will begin treatment before it flows to other water facilities. Photo Credit: Ed Verhamme, Limnotech.
CIGLR Associate Director & Research Scientist, Tom Johengen, works hard to understand more about Lake Erie hypoxia by measuring sediment oxygen demand. Photo Credit: Michele Wensman, CIGLR.
The story of hypoxia
map of great lakes showing colors of model output


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Improving lake effect snow forecasts by making models talk to each other

If you live in the Great Lakes basin and have been on or even near a road recently, you might be feeling unreasonably ragey at the mere mention of lake effect snow. We get it. But bear with us, because we’re doing some cool science we’d like to tell you about. It may even make your commute easier someday, or at least more predictable.

GLERL scientists are working with researchers at the University of Michigan’s Cooperative Institute for Great Lakes Research (CIGLR), the National Weather Service, and NOAA’s Earth Systems Research Laboratory (ESRL) to make lake effect snow forecasts in the Great Lakes better.

NOAA’s high resolution rapid refresh (HRRR) model is the most commonly used weather model for predicting lake effect snow. An experimental version runs on a beastly high-performance computer at ESRL in Colorado, and predicts a whole list of atmospheric variables (including snowfall) every 15 minutes. The model relies on water surface temperature data, collected via satellite, to make its predictions. It’s important to give the model accurate water surface temperatures to estimate evaporation across the Great Lakes, since it is the main driver of lake effect snow.

Unfortunately, satellite temperature data has limitations. If clouds keep satellites from measuring the temperature at a specific location, the weather model will just use the most recent measurement it has. Since it’s especially cloudy in the Great Lakes during the lake effect snow season (late fall and early winter), that data could be days old. Because lake temperatures are changing quite rapidly this time of year, days-old data just doesn’t cut it.

As it turns out, GLERL already has a model that predicts Great Lakes surface temperature pretty well. The Great Lakes Operational Forecast System (GLOFS) spits out lake surface temperatures every hour. If we tell the weather model to use GLOFS output instead of satellite data, it has the potential to do a far better job of forecasting lake effect snow.

Linking two models like this is called “coupling”. GLOFS actually already uses input from HRRR—wind, air temperature, pressure, clouds and humidity data all inform GLOFS’ predictions. We’re just coupling the models in both directions. HRRR will send its output to GLOFS, GLOFS will “talk back” with its own predictions of water surface temperature (and ice cover), and HRRR will produce a (hopefully) more informed prediction of lake effect snow.

Initial results are promising. We used the coupled models to do a ‘hindcast’ (a forecast for the past) to predict lake effect snow for a major event over Lake Erie in November of 2014. They did a significantly better job than without coupling. The figure below shows the difference.

The coupled models improved cumulative snow water equivalent forecasts. Red shows where the model increased snowfall.

You’ll notice a band of blue on the southeastern edge of Lake Erie, indicating that the coupled models predicted less lake effect snow in that area. There’s a band of orange directly to the north of it, where the coupled models predicted more lake effect snow. What you’re seeing is the coupled model predicting the same band of snow, but further north, closer to where it actually fell.

That storm slammed the city of Buffalo, New York, killing 13 people. Better lake effect snow predictions have the potential to save lives.

GLERL and partners will be doing further testing this winter, and if it works out, the model coupling will be carried over in research-to-operations transitions.


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The HAB season is over, but the work goes on

It’s nearly winter here in the Great Lakes—our buoys are in the warehouse, our boats are making their way onto dry land, and folks in the lab are working hard to assess observed data, experiments, and other results from this field season.

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This is a retrospective animation showing the predicted surface chlorophyll concentrations estimated by the Experimental Lake Erie HAB Tracker model during the 2018 season. Surface chlorophyll concentrations are an indicator of the likely presence of HABs. For more information about how the HAB Tracker forecast model is produced and can be interpreted, visit our About the HAB Tracker webpage.

The harmful algal bloom (HAB) season is also long over in the region. The final Lake Erie HAB Bulletin was sent out on Oct. 11, as the Microcystis had declined in satellite imagery and toxins decreased to low detection limits in samples. In the seasonal assessment, sent out by NOAA’s Centers for Coastal Ocean Science on Oct. 26, it was determined that the season saw a relatively mild bloom—despite its early arrival in the lake—and the bloom’s severity was significantly less than that which was predicted earlier in the season. These bulletins and outlooks are compiled using several models. Over the winter, the teams working on the models take what they learn from the previous season, and update their models for future use.

Back in the lab, the HABs team—researchers from both GLERL and the Cooperative Institute for Great Lakes Research (CIGLR)—will spend the winter analyzing data they collected through a variety of observing systems. This summer was packed with the use of new observing technologies, like hyperspectral cameras and the Environmental Sample Processor (in case you missed it, check out this fun photo story of the experimental deployment of a 3rd generation ESP). In addition, GLERL and CIGLR staff maintained a weekly sampling program program, from which scientists are analyzing and archiving samples and conducting experiments.

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Aerial photograph of the harmful algal bloom in Western Basin of Lake Erie on July 2, 2018, (Photo Credit: Aerial Associates Photography, Inc. by Zachary Haslick). Pilots from Aerodata have been flying over Lake Erie this summer to map out the general scope of the algal blooms. In addition to these amazing photos, during the flyovers, additional images are taken by a hyperspectral imager (mounted on the back of the aircraft) to improve our understanding of how to map and detect HABs. The lead researcher for this project is Dr. Andrea VanderWoude, a NOAA contractor and remote sensing specialist with Cherokee Nation Businesses. For more images, check out our album on Flickr.

This lab work is super important for understanding the drivers of toxic algae in the Great Lakes. For instance, in a new study released this month, researchers looking at samples from previous years found that “ . . . the initial buildup of blooms can happen at a much higher rate and over a larger spatial extent than would otherwise be possible, due to the broad presence of viable cells in sediments throughout the lake,” according to the lead author Christine Kitchens, a research technician at CIGLR, who works here in the GLERL lab. This type of new information can be incorporated into the models used to make the annual bloom forecasts.

As you can see, our work doesn’t end when the field season is over.  In spring 2019, when the boats and buoys are back in the water and samples are being drawn from the lakes, researchers will already have a jump on their work, having spent the winter months analyzing previous years, preparing, and applying what they’ve learned to the latest version of the Experimental HAB Tracker, advanced observing technologies, and cutting-edge research on harmful algal blooms in the Great Lakes.


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Photo story: Using an AUV to track algae in Lake Erie

In late July and early September, during the peak of the 2018 harmful algal bloom in the Western Basin of Lake Erie, NOAA GLERL, NOAA National Centers for Coastal Ocean Science (NCCOS), NOAA Atlantic Oceanographic and Meteorological Laboratory (AOML) and CIGLR researchers teamed up with a group of scientists and engineers from the Monterey Bay Research Institute (MBARI). Their mission: to test how well a third-generation environmental sample processor (3GESP), mounted inside a long-range autonomous underwater vehicle (LRAUV), can track and analyze toxic algae in the Western Basin of Lake Erie. You can read more about the purpose of this project in this great news story by MBARI’s Kim Fulton-Bennett.

Below is a photo story showing all (well, much) of the hard work that went into this test deployment.

First, the new gear had to be shipped from California to the GLERL laboratory in Ann Arbor, Michigan.

On the truck and heading to Michigan!
Ready to go.
It’s load out day!

 

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Upon arrival, Jim Birch, Director of the MBARI SURF (Sensors Underwater Research of the Future) Center, & Bill Ussler, MBARI biogeochemist, got straight to work in GLERL’s Marine Instrumentation Lab.

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The inside of the 3G ESP has a lot of moving parts. Since this is the first time the team is testing it in freshwater, before it can go out, everything needs to be fine-tuned to work in a variety of conditions in Lake Erie (more on that later.)

So. Many. Moving. Parts.

 

Running a test sample.
Cartridges that hold the water sample for both real-time processing, as well as an archive sample for further analysis back in the lab.
The propeller end of the long-range AUV in which the 3GESP will be housed.
More testing and tweaking.
The “guts” of the 3GESP prior to inserting the cartridges.

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Once everything is in working order, the 3GESP gets inserted into an LRAUV or long-range autonomous underwater vehicle (the torpedo-looking thing). This gives the 3GESP the ability to move around in the water all by itself once researchers have set parameters for it. The team has named this particular vehicle, Makai, which is Hawaiian for “toward or by the sea.” Seems appropriate! That’s Brian Kieft, MBARI software engineer, on the right. He plays a crucial role in making sure Makai does her job.

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All hands on deck for a few more tweaks.

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Once everything is installed tightly, helium is added into the canister to check for leaks. CIGLR engineer, Russ Miller, is working with Jim to fill it up.

Now, the team is ready to head out to Lake Erie. Here’s where things start to get exciting!

 

A kayak? What do we need that for? You’ll see.
Loading the Makai into the van.
Heading to Toledo Beach Marina in La Salle, Michigan .

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Before the team sets Makai free to track the algal bloom in the Western Basin of Lake Erie, they must first check her ballast and trim. This is especially important for such a shallow lake (relative to where the team has been testing this technology in the deep canyons of of Monterey Bay off the coast of California.)

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Brian has to do all of the hard work.

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Because, science.

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Time to load Makai onto the NOAA vessel, which is stationed in La Salle, Michigan. Captain Kent Baker, a contractor with NOAA, is in the background operating the crane. Kent takes NOAA and CIGLR researchers and technicians out to bi-weekly sampling stations, helps deploy buoys and other instrumentation, and is at the ready for pretty much anything that needs to happen in Lake Erie.

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Once she’s all settled onto the boat, the team takes Makai to the first deployment location.

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The inaugural deployment was set to match up with the bi-weekly sampling stations.

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Look closely and you’ll see Makai off on her way!

Makai and the team spent nearly two weeks tracking, sampling, adjusting, and learning about using this technology to track algal toxins in Lake Erie.

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The team used the images from GLERL’s Experimental Lake Erie Harmful Algal Bloom (HAB) Tracker to determine where to send Makai.

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Then, they would determine how many samples to take, and program her to go to specific waypoints.

Remember when we said this Lake Erie mission will be different than the ones the team has performed in Monterey Bay? Well, here’s one example of how.

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After a few hours of no communication, and a little hunting, this is how the team found Makai. Two problems here: One, with the propellor up and the nose down, Makai cannot transmit data, including her location, as the transmitter only works above water. And, two, well . . .

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The reason she was nose down in the first place is because Lake Erie is pretty shallow, and she’d taken on quite a bit of mud.

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Once she was all cleaned up, the team set Makai out again to complete the rest of her mission.

Once the deployment was over, the research didn’t stop there.

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Archive samples were taken so that folks back in the lab could further analyze them.

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Here’s GLERL’s Observing Systems and Advanced Technology (OSAT) branch chief, Steve Ruberg (left), along with Paul Den Uyl, a researcher with CIGLR, helping Bill extract the sample filters from the cartridges.

Deunyl

The filters are being collected for analysis of DNA. The DNA will be extracted from each filter and analyzed. We’re looking at absolute quantity of known microcystin producing toxin genes in samples collected, information on bacterial community composition, and information on eukaryotic organism community composition. The samples will also analyzed through shotgun sequencing. This is where all of the genes in the sample are turned into human readable information and can be combined to make what can be thought of as an organism’s genetic instruction guide (what genes it has). This information will be very helpful in better understanding what causes the algae to be toxic (not all algae is toxic).

 


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Lake Erie Hypoxia Forecasting Project Kicks Off With Stakeholder Workshop

A collaborative research team, led by Drs. Craig Stow of the National Oceanic and Atmospheric Administration’s Great Lakes Environmental Research Laboratory (NOAA GLERL) and Mark Rowe of the University of Michigan’s Cooperative Institute for Limnology and Ecosystems Research (CILER),  will be holding a workshop with key stakeholders for guidance on how a forecast model could help meet the needs for information on low oxygen conditions—or hypoxia—in Lake Erie. The workshop, coming up later this spring, kicks off a 5-year project that brings together inter-agency and university scientists to produce a forecasting system that will predict the location and movement of hypoxic water in Lake Erie. The project will link a hypoxia model to NOAA’s Lake Erie Operational Forecasting System (LEOFS) hydrodynamic model, which provides daily nowcast and 5-10 day forecasts of temperature and currents in Lake Erie.

HypoxiaDiagram

Hypoxia occurs in the central basin of Lake Erie in July through September of most years. Low-oxygen water is an unfavorable habitat for fish, and may kill benthic organisms that provide food for fish. It is less well known, however, that hypoxic water can also upset drinking water treatment processes. Upwelling or seiche events can bring hypoxic water to water intakes along the shoreline, causing rapid changes in dissolved oxygen and associated water quality variables such as temperature, pH, dissolved organic matter, iron, and manganese. To maintain the quality of treated water, plant managers must adjust treatment in response to these changes. Hypoxia forecasts will provide several days advance notice of changing source water quality so that drinking water plant managers can be prepared to adjust treatment processes as needed.

While the hypoxia forecasting project will help to minimize the negative impacts of hypoxia, a parallel effort is occurring to address the root cause of this problem involving nutrient loading. Universities, state, federal, and Canadian agencies are collaborating to satisfy the goals of the Great Lakes Water Quality Agreement by reducing nutrient loads to Lake Erie, a primary stressor driving hypoxic conditions.

The upcoming stakeholder workshop on hypoxia will bring the research team together with stakeholders consisting of municipal drinking water plant managers from U.S. and Canadian facilities on Lake Erie, as well as representatives of state and local agencies. The group will learn about hypoxia and its effects, hear about the goals of the LEOFS-Hypoxia project, and provide input to the research team on their information needs. As the first in a series of meetings of the project’s Management Transition Advisory Group, this workshop will help identify the most useful data types and delivery mechanisms, laying the groundwork for the research team to design a forecasting tool that specifically addresses the needs of public water systems on Lake Erie.

The workshop will be held at Cleveland Water in Cleveland, Ohio. Representatives from Ohio Environmental Protection Agency (EPA), Ohio Department of Natural Resources, Ohio Sea Grant, townships and other local governments were also invited to attend.  

The LEOFS-Hypoxia project is a collaboration with the City of Cleveland Division of Water, Purdue University, and U. S. Geological Survey, with guidance from a management advisory group including representatives from Ohio public water systems, Ohio EPA, Great Lakes Observing System (GLOS), and NOAA. The work is supported by a $1.4 million award from the NOAA National Centers for Coastal Ocean Science (NCCOS) Center for Sponsored Coastal Ocean Research by a grant to NOAA GLERL and University of Michigan (award NA16NOS4780209).

Getting to the root cause of the problem
As part of an initiative conducted under the auspices of the Great Lakes Water Quality Agreement, Annex 4, the following forums, led by Dr. Craig Stow at GLERL, will focus on the linkage of nutrient loading to water quality degradation problems, such as hypoxia and harmful algal blooms.

  • 4/5-6: Nutrient Load Workshop
  • 5/9-10: Annex 4 (nutrients) Subcommittee Meeting

Scientists attending these workshops will apply long term research results to estimate nutrient inputs to Great Lakes waters and evaluate how well we are doing in reaching phosphorus load reduction targets established under Annex 4 of the GLWQA.

Additional Resources
NOAA GLERL Hypoxia web page: https://www.glerl.noaa.gov/res/HABs_and_Hypoxia/hypoxiaWarningSystem.html

Download the NOAA GLERL hypoxia infographic, here:


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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.

functional-groups

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.

hab-tracker-prediction-of-sept-19-hab-scum

hab-tracker-run-on-sept-22-comparison-with-flyover

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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Analyzing Algal Toxins in Near Real-Time

This morning, along side our partners at the University of Michigan’s Cooperative Institute for Limnology and Ecosystems Research (CILER), we deployed the very first Environmental Sample Processor (ESP) in a freshwater system.

An ESP is an autonomous robotic instrument that works as a ‘lab in a can’ in aquatic environments to collect water samples and analyze them for algal toxins. This allows for near real-time (only a couple of hours for remote analyzation as opposed to a day or more back at the lab) detection of harmful algal blooms (HABs) and their toxins. GLERL’s ESP—named the ESPniagara—will measure concentrations of Microcystin, the dominant algal toxin in the Great Lakes. It will also archive samples, allowing us to genetically detect Microcystis, the predominant HAB in the Great Lakes, back in the laboratory.

There are 17 ESPs throughout the world and the ESPniagara is the only one (so far) being used in freshwater. We’ve placed it near the Toledo drinking water intake in western Lake Erie to collect and analyze water and detect concentrations of toxins that may be a health risk to people swimming, boating or drinking Lake Erie water. We’ll post the data from the on our HABs and Hypoxia webpage  so that drinking water managers and other end users can make water quality/ public health decisions.

The goal of this research is to provide drinking water managers with data on algal toxicity before the water reaches municipal water intakes. ESPniagara will strengthen our ability to both detect and provide warning of potential human health impacts from toxins.

This research proves to be a great collaborative effort for GLERL, CILER, and our partners. The Monterey Bay Aquarium Research Institute (MBARI) first developed the ESP, which is now commercially manufactured by McLane Laboratories. GLERL purchased the ESPniagara with funding from EPA-Great Lakes Restoration Initiative. NOAA-National Centers for Coastal Ocean Science (NCCOS) developed the technology to detect Microcystins (an ELISA assay). NCCOS funding also supported previous work to demonstrate the viability of ESP technology to assist in monitoring and forecasting of HABs and their related toxins in the marine environment.

We plan to have the ESPniagara out in western Lake Erie for the next 30 days. Check back later this week and next for a few videos, photos, and some pretty cool data. For more information, check out our HABs and Hypoxia website and read up on the ESP.