Research | NOAA Great Lakes Environmental Research Laboratory

December 19, 2022
by Gabrielle Farina Leave a comment

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.

December 14, 2022
by Gabrielle Farina Leave a comment

Decades in the making, NOAA’s newest Lake Superior and Lake Ontario forecast systems become fully operational

Did you know that NOAA operates a forecasting system that predicts water conditions on the Great Lakes? Whether you’re wondering about a lake’s temperature, currents, or water level changes, NOAA’s got you covered! This fall, NOAA implemented newly updated versions of the Lake Superior and Lake Ontario portions of this system, and added ice forecasts to all five lakes.

*Lake Michigan waves at the St. Joseph North Pier Lighthouses following superstorm Sandy. October 29, 2012. Credit: S. Lashley, NOAA NWS.*

GLOFS forecasts Great Lakes conditions

The publicly available Great Lakes Operational Forecast System (GLOFS) is a NOAA automated, model-based prediction system aimed at providing improved predictions of these conditions in the five Great Lakes (Erie, Michigan, Superior, Huron and Ontario) for the commercial, recreation, and emergency response communities. GLOFS models use current lake conditions and predicted weather patterns to forecast the lake conditions for up to five days (120 hours) in the future. GLOFS predictions enable users to increase the margin of safety and maximize the efficiency of commerce throughout the Great Lakes.

NOAA’s National Weather Service (NWS) and National Ocean Service (NOS) work together to run GLOFS operationally on NOAA’s High Performance Computing System. By running on NOAA’s High Performance Computing System, GLOFS has direct access to National Weather Service operational meteorological products that are required for reliable and timely operations.

A major update for Lakes Ontario and Superior

A key goal of NOAA’s Research branch is to continually make forecasts better, and GLERL scientists play a major role in improving the models that constitute GLOFS. Like the rest of GLOFS, the Lake Ontario and Lake Superior portions – Lake Ontario Operational Forecast System (LOOFS) and Lake Superior Operational Forecast System (LSOFS) – were originally based on the Princeton Ocean Model. As of October 2022, they’ve now been upgraded with higher-resolution versions that are based on a newer computer model.

*MODIS satellite images of Lakes Superior (left) and Ontario (right) in March 2021.*

The new LOOFS and LSOFS use the Finite Volume Community Ocean Model (FVCOM), coupled with an unstructured grid version of the Los Alamos Sea Ice model (CICE). The new model provides users with higher resolution of nowcast (near-present conditions) and forecast guidance of water levels, currents, water temperature, ice concentration, ice thickness and ice velocity out to 120 hours in the future, and it updates four times per day. By invoking advanced model schemes and algorithms, LOOFS and LSOFS are expected to generate a more accurate model output than their former versions.

Before they were ready to become operational, the new versions of LOOFS and LSOFS were run experimentally at GLERL for several years, where they underwent extensive testing and evaluation. GLERL played a key role in developing these models and ran them as part of the Great Lakes Coastal Forecasting System (GLCFS) – an experimental version of GLOFS that GLERL uses to prepare new models to become operational.

With this transition, the GLOFS models for all five Great Lakes have now been upgraded to FVCOM versions, as the Lake Erie model was upgraded in 2016, and the Lake Michigan-Huron model was upgraded in 2019. A new FVCOM-based model for the Huron-Erie Corridor, which includes Lake St. Clair and both the St. Clair and Detroit Rivers, is scheduled to be added to GLOFS in 2023. Read more about the LOOFS and LSOFS transition here.

*These output maps from the current 3rd generation GLOFS show Lake Superior wind speed*
*and direction (top) and Lake Ontario water temperatures (bottom).*

GLERL has been improving GLOFS for over 30 years

GLOFS is based on the Great Lakes Forecasting System, originally developed by The Ohio State University (OSU) and GLERL in the late 1980s and 1990s under the direction of Dr. Keith Bedford (OSU) and Dr. David Schwab (NOAA GLERL). The original forecasting systems utilized the Princeton Ocean Model (POM) and used a set of uniformly structured bathymetric grids. The first routine nowcast, using a low-resolution grid for Lake Erie, began at OSU in 1992.

Starting in 2002, GLERL’s semi-operational GLCFS was expanded to five lakes using medium-resolution grids (5 – 10 km) and 48-hr forecasts were added. This version was successfully transferred from research to operations at NOAA NOS in 2010. The transition to operations at NOAA NOS was a joint effort between NOAA GLERL, NOS Center for Operational Oceanographic Products and Services (CO-OPS) and NOS Office of Coast Survey (OSC) Coast Survey Development Laboratory (CSDL), private industry, and academia (OSU).

NOAA GLERL has continued to make improvements to the experimental GLCFS; these include increasing the grid resolution (2 – 10 km), adding ice dampening and an ice model, and extending the forecasts to 120 hours during the period of 2006-2014 (generation 2). The current 3rd generation of the GLOFS is what you see run by NOS today, with a resolution of 200m to 2.5km and producing 120-hour forecasts.

The development and implementation of LSOFS and LOOFS is a joint project across several NOAA offices and external partners.

NOAA National Ocean Service Center for Operational Oceanographic Products and Services
NOAA NOS Office of Coast Survey
NOAA Office of Oceanic and Atmospheric Research Great Lakes Environmental Research Laboratory
Finite Volume Community Ocean Model development group at the University of Massachusetts Dartmouth
NOAA National Weather Service National Centers for Environmental Prediction Central Operations

October 26, 2022
by Gabrielle Farina 1 Comment

Lessons from Lake Huron: A look back at NOAA GLERL’s 2022 fieldwork for the Cooperative Science and Monitoring Initiative

Every summer, NOAA GLERL scientists travel far and wide across the Great Lakes region to study the biological, chemical, and physical properties of these amazing lakes. A portion of this fieldwork contributes to a larger project called the Cooperative Science and Monitoring Initiative – or CSMI – which helps us take a deeper dive into studying a different Great Lake each year. Instituted under the 2012 Great Lakes Water Quality Agreement, CSMI is a multi-agency, international effort to coordinate science and monitoring activities in one of the five Great Lakes each year to generate data and information for environmental management agencies.

*MODIS satellite image of Lake Huron on May 18, 2021. Credit: NOAA Great Lakes CoastWatch Node.*

Each Great Lake gets a “CSMI year” once every five years, and 2022 was Lake Huron’s turn to shine. Sitting right at the center of the Great Lakes region, Lake Huron is shared by the state of Michigan and the Canadian province of Ontario. It’s the second largest of the Great Lakes and ranks as the fourth largest lake in the world by surface area. Lake Huron provides economically and culturally important services, including a productive fishery, a source of clean drinking water, and natural beauty that supports a significant tourism industry. It’s also home to Thunder Bay National Marine Sanctuary, the first ever NOAA National Marine Sanctuary to be established in the Great Lakes.

GLERL’s fieldwork for this year’s Lake Huron CSMI efforts focused on benthic and spatial surveys in Thunder Bay and Saginaw Bay. Here’s a look back at some of the highlights!

*GLERL scientists Ashley Elgin and Rachel Orzechowski rinse down sediments collected by a Ponar grab.*

NOAA GLERL has been conducting benthic (lake bottom) research in the Great Lakes since 1980, during which time we have built an unparalleled record of the arrival and expansion of invasive zebra and quagga mussels. CSMI provides the perfect opportunity to expand on this knowledge. Surveying the lake bottom allows us to track the population dynamics of these mussels, follow their impacts on native species, and also monitor for any new invasive benthic species.

*GLERL scientist Paul Glyshaw collects Ponar samples onboard the Fisheries and Oceans Canada/Canadian Coast Guard vessel* Limnos *for mussel length-weight analysis.*

In June, July, and August of this year, GLERL conducted surveys that will allow us to update the status of invasive dreissenid mussels and other benthos of Lake Huron. As an exciting bonus, our benthic surveys in Saginaw Bay and Thunder Bay even received dive support from Thunder Bay NMS to supplement the samples collected with Ponar grabs.

*Thunder Bay NMS divers Stephanie Gandulla and John Bright support GLERL’s benthic survey on board the R/V 5503.*

*The large metal claw used for a Ponar grab is no match for a mussel-covered rock like this, which is why we need NOAA’s Thunder Bay NMS Divers to support the benthic survey.*

In the truly collaborative fashion that CSMI is known for, GLERL scientists maximized time on these cruises by collecting samples for several federal and university collaborators in addition to conducting our mussel survey. For example, mussels and sediments went to the U.S. Geological Survey for mercury analysis, and researchers from the University of Michigan will be looking for mussel environmental DNA in water samples.

*This sediment sample from Saginaw Bay has many benthic inverts present, including dreissenid mussels, chironomids, water mites, amphipods, and a snail.*

*Paul Glyshaw collects and filters Lake Huron water onboard the Fisheries and Oceans Canada/Canadian Coast Guard vessel* Limnos to measure carbon content. This helps us address potential impacts of climate change on the lake, including acidification, changes to production, and altered biogeochemical processes.

Plus, GLERL also teamed up with the U.S. Environmental Protection Agency, Fisheries and Oceans Canada (DFO), and the Canadian Coast Guard in a whole lake-benthic survey, during which GLERL assessed mussel body condition, mussel reproduction, inorganic carbon measures, and collected water for eDNA across the lake. In true CSMI spirit, DFO stepped up and supported the benthic survey when the EPA R/V Lake Guardian became unavailable.

*Fisheries and Oceans Canada/Canadian Coast Guard vessel* Limnos *pulls into Port Huron for the Lake Huron Benthic survey.*

In addition to surveying what’s happening on the lake floor, GLERL also conducted an intensive spatial survey through CSMI to study Lake Huron’s food web in the area between Thunder Bay and Saginaw Bay. With a special focus on studying the interactions between larval fish and plankton, one of the key instruments used was GLERL’s Plankton Survey System (PSS). This high-tech piece of equipment is a towed multi-sensor platform capable of measuring turbidity, chlorophyll a, photosynthetically active radiation (PAR), conductivity, temperature, and zooplankton spatial distributions.

*GLERL scientists use the PSS on Lake Michigan in the mid 2000s.*

The plots below show a nearshore to offshore view of Lake Huron’s biological data measured by the PSS, like water temperature, dissolved oxygen, and chlorophyll, and plankton distribution. Check out more PSS plots from this spatial survey here.

*While the PSS instrument was collecting data below the waves,* *lots of mayflies were catching a ride on this research cruise!*

Now that the fieldwork is complete, the next step for GLERL’s CSMI work is to process our samples and analyze our data to continue building our knowledge of Lake Huron. Stay tuned in 2023, when CSMI heads east to study Lake Ontario!

For more CSMI information, data, and findings, visit greatlakescsmi.org. Plus, check out this related CSMI project in which GLERL and CIGLR developed an Experimental Biophysical Modeling Forecast System for Lakes Michigan and Huron.

August 10, 2022
by Gabrielle Farina Leave a comment

New under-ice observing capabilities could lead to new discoveries in the Great Lakes

***UPDATED DECEMBER 2022***

During the dog days of summer here in the Great Lakes, scientists at NOAA’s Great Lakes Environmental Research Laboratory (GLERL) are already thinking about the ice that will form on the lakes this winter.

This year, NOAA GLERL and a team of federal, university, and industry partners are conducting test deployments of an autonomous underwater vehicle (AUV) in Lake Michigan, with the ultimate goal of using it under lake ice during winter to collect ecological and water quality data. Observations of winter ecology are difficult to obtain compared to observations in the ice-free season, when most fieldwork takes place – which makes this hunt for winter data especially important.

In the world of Great Lakes research, the start of winter traditionally signals the end of fieldwork for the year. Buoys come out of the water, and research vessels – which aren’t designed for use in ice-covered waters – are docked for the season. Scientists get to work analyzing new data from the previous field season and tuning up field equipment for a fresh start in the spring. This break leads to a several-month gap in most of GLERL’s field data, but this project aims to fill that gap using the high-tech SAAB Sabertooth AUV.

Video by Great Lakes Outreach Media.

One underwater robot, many important jobs

One of the AUV’s main tasks will be to collect water quality data, benthic (lake bottom) data, and fish and zooplankton observations. These observations will be collected using an acoustic imaging system, and will contribute to our understanding of important wintertime ecological processes in the Great Lakes. While summer is widely considered to be the peak time of year for biological productivity, biological processes still occur during the winter and are understudied in the Great Lakes. NOAA GLERL’s winter observations may even lead to unexpected discoveries, as new GLERL data suggest that some Great Lakes biological processes may actually accelerate during the winter months. Ice cover is seen as a key variable in the regulation of biological processes in the lakes during winter. Enhancing our understanding of these processes is particularly important as climate change may have implications for the extent, thickness, and duration of seasonal ice cover.

*The Saab Sabertooth is an autonomous underwater vehicle (AUV) that NOAA GLERL will operate under ice during winter in the Great Lakes.*

The AUV also will characterize winter distributions of prey fish using a multi-frequency echosounder. Spawning in prey fish species like bloater also take place during winter, in turn affecting predator stocks (like lake trout) that help underpin a 7 billion-dollar annual Great Lakes fishery. Additionally, the AUV will contribute valuable data on the distribution of invasive mussels to NOAA GLERL’s 25-year ecological monitoring program. The AUV will map the locations of invasive mussel reefs on the lake floor using sonar technology and high-resolution imagery.

World-class technology in the Great Lakes

The SAAB Sabertooth AUV is no average piece of fieldwork equipment; it’s among the most advanced and complex underwater vehicles in the world. Navigation is one of the biggest challenges AUVs face, since GPS signals are unavailable underwater. This AUV contains an Inertial Navigation System, which keeps track of the vehicle’s movements with extreme precision. Based on its known deployment location, the vehicle uses this navigation system to calculate its exact location throughout its mission with minimal error. When within range, acoustic beacons anchored nearby in the lake are also used to confirm the AUV’s location.

*Acoustic beacon is deployed in Lake Michigan for AUV navigation system updates. Brad Hibbard dials in a highly accurate beacon location.*

In addition to the AUV itself, this effort also includes the development of a fully integrated docking station that allows the vehicle to recharge its battery and transfer the data it’s collected during its winter excursion. The vehicle’s ability to safely dock, charge its battery, and transmit data to scientists is a critical component in its ability to function under ice without human help.

AUV docking station built by Hibbard Inshore. The dock allows vehicle recharging and data transfer through a Sonardyne BlueComm optical communications system. Hibbard and Saab used Sonardyne acoustic beacons to enable autonomous docking.

***

DECEMBER 2022 UPDATE

The most recent phase of this project, conducted in December 2022 at the beginning of meteorological winter, tested the AUV’s docking station and charging capabilities for the first time. The highly successful field trials achieved several goals:

The AUV autonomously navigated through the Muskegon Channel and out into Lake Michigan, where it successfully collected ecological data and mapped the lake floor.
Back in the channel, the AUV autonomously docked itself, using Sonardyne acoustic beacons to confirm its location.
Once docked, the AUV transferred the data it had collected and successfully recharged its battery from a Teledyne Energy Systems Subsea Supercharger® hydrogen fuel cell. As a truly groundbreaking outing, this field trial was the first time the Saab AUV has ever been charged underwater using a fuel cell power source.

So, where will all this new data go? The AUV’s data will ultimately be added to GLERL’s Realtime Coastal Observation Network (ReCON).

Lowering the Teledyne Energy System’s Subsea Supercharger® hydrogen fuel cell into the water. The fuel cell was used to recharge Hibbard Inshore’s Saab AUV internal batteries.

After several days of testing the docking and
navigation capabilities, the AUV headed out into
Lake Michigan for a trial to collect ecosystem data.

***

Smooth sailing beneath the surface

The large size of the AUV provides ample space, flotation, and electrical resources for simultaneously carrying a large suite of sensors that make multitasking a breeze for this high-tech vehicle. The various sensors work together to ensure the vehicle stays upright, avoids collisions with boats, and doesn’t accidentally hit the bottom of the lake. Plus, robust propellers allow the AUV to make precise turns and hover at a fixed depth, making it much easier to maneuver than its torpedo-shaped cousins.

*Preparing this AUV for deployment is no small task. Vehicle setup was completed by the Hibbard Inshore and Saab team over several days.*

When it’s not in the Great Lakes helping NOAA with environmental research, this vehicle can usually be found performing tunnel inspections at hydroelectric power facilities around the world – including locations like California, South Korea, and Turkey. The AUV’s ability to navigate through these tunnels allows them to be inspected without being drained, saving considerable time and money. Just as it navigates through these enclosed tunnels, this impressive underwater robot could soon be navigating its way under Great Lakes ice cover.

NOAA GLERL’s partners on this project include Hibbard Inshore, Saab Dynamics AB, Teledyne Energy Systems, the Cooperative Institute for Great Lakes Research (CIGLR), and the United States Geological Survey (USGS).

NOAA Disclaimer: This publication does not constitute an endorsement of any commercial product or intend to be an opinion beyond scientific or other results obtained by NOAA.

September 14, 2021
by Gabrielle Farina 2 Comments

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.

June 22, 2021
by Gabrielle Farina 1 Comment

New wave buoy will provide data to support wave and flood forecasting on Lake Champlain

The NOAA Great Lakes Environmental Research Laboratory (GLERL) and partners recently deployed a buoy in Lake Champlain that will measure the lake’s wave heights to assess the accuracy of a new experimental model for the lake. This is part of a five-year NOAA GLERL project that will improve public safety on Lake Champlain by contributing to flood preparedness and response around the shores of the lake. Wave conditions are critical to public safety both for recreational and commercial activities on the lake – such as for boats, harbors, and beaches – but also for predicting coastal flood impacts at the shoreline where waves can run up and significantly impact infrastructure.

Left: Newly deployed NOAA buoy in Lake Champlain. Credit: University of Vermont FEMC staff. Top right: NOAA GLERL partners at the University of Vermont’s Forest Ecosystem Monitoring Cooperative (FEMC) deployed the buoy on Lake Champlain in May 2021. Credit: University of Vermont FEMC staff. Bottom right: Sunset on Lake Champlain. Credit: Dan Titze, CIGLR.

The project is a major collaborative effort by bi-national, federal, and university partners of NOAA GLERL. Partners at the University of Vermont’s Forest Ecosystem Monitoring Cooperative (FEMC) deployed the seasonal buoy in May, and the Coastal Data Information Program (CDIP) at the University of San Diego Scripps Institute of Oceanography receives the data, manages its quality control, and posts it to NOAA’s National Data Buoy Center (NDBC) website. Researchers at the Cooperative Institute for Great Lakes Research (CIGLR) are currently leading the development of a wave model for Lake Champlain, which is providing experimental forecasts on the GLERL website.

The buoy is located in the middle of the lake near Schuyler Reef, where it will remain until late fall, and is collecting wave height observations that will be used to validate NOAA’s WAVEWATCH III model for Lake Champlain. The experimental model’s output data will be compared to the buoy’s observed data, which will help scientists assess how well the model performs.

Location of the new NOAA Lake Champlain wave buoy, depicted by a yellow diamond. Map credit: NOAA National Data Buoy Center.

The buoy’s environmental data can be found on the CDIP website, and on the buoy’s page on the NOAA NDBC website. The buoy and the experimental wave model will be a helpful new tool for the region’s National Weather Service Weather Forecast Office in Burlington, Vermont, which provides lake forecasts including wave data to mariners in the region.

In addition to regional weather forecasters and local mariners, this buoy’s data will also be useful to emergency managers in the counties and cities around Lake Champlain and the Richelieu River, as well as the NOAA National Centers for Environmental Prediction which will transition the WAVEWATCH III model to operations.

This project is funded by the International Joint Commission’s Lake Champlain-Richelieu River (LCRR) Study Board. The International Joint Commission (IJC) is a bi-national organization established by the governments of the United States and Canada under the Boundary Waters Treaty of 1909. It oversees activities affecting the extensive waters and waterways along the Canada–United States border. The IJC’s LCRR Study Board was created in 2016 to undertake a study of the causes, impacts, risks, and potential solutions to flooding in the LCRR basin.

November 20, 2020
by Margaret Lansing Leave a comment

Lake effect snow: What, why and how?

Winter is nearly here — and those who live and work in the Great Lakes region are already wondering what the winter of 2021 has in store. Early indications suggest a La Niña winter pattern, which shifts the odds towards cooler, wetter weather with more ice cover.

More snow and ice can mean more fun, and can be great for winter sports like ice fishing, snowmobiling and skiing. Unfortunately, it can also mean severe weather events involving ice and snow. In the Great Lakes region, snow comes via the usual low pressure systems, but we also can get lake effect snow.

*Average location of the jet stream and typical temperature and precipitation impacts during La Niña winter over North America. Map by Fiona Martin for NOAA Climate.gov.*

What is lake effect snow?

In the Great Lakes region, hazardous winter weather often happens when cold air descends from the Arctic region. Lake effect snow is different from a low pressure snow storm in that it is a much more localized and sometimes very rapid and intense snow event. As a cold, dry air mass moves over the unfrozen and relatively warm waters of the Great Lakes, warmth and moisture from the lakes are transferred into the atmosphere. This moisture then gets dumped downwind as snow.

Lake Effect Snow Can Be Dangerous

Lake effect snow storms can be very dangerous. For example, 13 people were killed by a storm that took place November 17-19, 2014 in Buffalo, New York. During the storm, more than five feet of snow fell over areas just east of Buffalo, with mere inches falling just a few miles away to the north. Not only were lives lost, but the storm disrupted travel and transportation, downed trees and damaged roofs, and caused widespread power outages. Improving lake effect snow forecasts is critical because of the many ways lake effect snow conditions affect commerce, recreation, and community safety.

Lake Effect Snow animation: Mid-December 2016 The lake effect snow EVENT resulted in extremely heavy snow across Michigan, Ohio, upstate New York as well as the province of Ontario east of Lake Superior and Huron.

Why is lake effect snow so hard to forecast?

There are a number of factors that make lake effect snow forecasting difficult. The widths of lake-effect snowfall bands are usually less than 3 miles — a very small width that makes them difficult to pinpoint in models. The types of field measurements scientists need to make forecasts better are also hard to come by, especially in the winter! We would like to take frequent lake temperature and lake ice measurements but that is currently not possible to do during the winter, as conditions are too rough and dangerous for research vessels and buoys. Satellite measurements can also be hard to come by. The Great Lakes region is notoriously cloudy in the winter – it’s not uncommon to go for over a week without usable imagery.

MODIS satellite image of a lake effect snow event in the Great Lakes, caused by extensive evaporation as cold air moves over the relatively warm lakes. November 20, 2014. Credit: NOAA Great Lakes CoastWatch.

GLERL and CIGLR work to improve lake effect snow forecasting

Currently, NOAA Great Lakes operational models provide guidance for lake effect snow forecasts and scientists at NOAA GLERL and CIGLR are conducting studies to improve them.

They use data from lake effect snow events in the past and compare how a new model performs relative to an existing model. One way to improve forecast model predictions is through a model coupling approach, or linking two models so that they can communicate with each other. When they are linked, the models can share their outputs with each other and produce a better prediction in the end.

Our lake effect snow research continues

Our lake effect modeling research is ongoing, and GLERL, CIGLR, NWS Detroit, the NOAA Global Systems Laboratory continue to address the complex challenges and and our studies build upon each other to improve modeling of lake-effect snow events. A new focus will be on running the models on a smaller grid scale and continuing to work to improve temperature estimates as both are key to forecasting accuracy.

A recent study, published by CIGLR and GLERL and other research partners, “Improvements to lake-effect snow forecasts using a one-way air-lake model coupling approach,” is the latest in a recent series of studies* (see list below) that help to make lake effect snow forecasts better. This study takes a closer look at how rapid changes in Great Lakes temperatures and ice impact regional atmospheric conditions and lake-effect snow. Rapidly changing Great Lake surface conditions during lake effect snow events are not accounted for in existing operational weather forecast models. The scientists identified a new practical approach for how models communicate that does a better job of capturing rapidly cooling lake temperatures and ice formation. This research can result in improved forecasts of weather and lake conditions. The models connect and work together effectively and yet add very little computational cost. The advantage to this approach in an operational setting is that computational resources can be distributed across multiple systems.

**Study model run:** This panel of images shows model runs that looks at data from a lake effect snow event from January 2018 with and without the new type of model coupling. The image on the far right labeled Dynamic – Control Jan 06 shows the differences in air temperature (red = warmer, blue = colder) and wind (black arrows) when the models are coupled. The areas in color show how the new model coupling changed the model output considerably and improved the forecast.

Special Two-Day Science Translation Session at IAGLR 2019

This June, fellow researchers from around the world will gather in Brockport, New York, on the shores of the Erie Canal for IAGLR’s 62nd annual Conference on Great Lakes Research. Hosted by The College at Brockport, State University of New York, the conference will feature four days of scientific sessions and speakers focusing on the theme Large Lakes Research: Connecting People and Ideas. Mark your calendars for June 10-14, 2019. You won’t want to miss it!

During the conference there will be a special two-day session that highlights the importance of science translation. The session, Beyond Peer Review: Why You Must Connect Your Science to Stakeholders (and how to do it), will consist of several components—17 formal presentations, a moderated panel discussion, a synthesis discussion with Q&A, as well as a Skills Café. Conference attendees are welcome to join us for any and all portions of this session. We hope to see you there!

Day 1 – Tuesday, June 11th

On Tuesday, the SciComm session will include 12 presentations and a panel moderated discussion with science communication thought leaders Peter Annin (Author), Andrea Densham (Shedd), Sandra Svoboda (DPTV) & TJ Pignataro (Buffalo News). The panelists will explore what they see happening now and what they think the future looks like for connecting people and ideas for large lakes research.

Tuesday Morning (Edwards Hall Room 103)

8:00-9:20 am – 3 presentations
9:20-9:40 am – Break
9:40-11:00 am – Moderated Panel Discussion

Tuesday Afternoon (Edwards Hall Room 103)

1:40-3:20 pm – 5 presentations
3:20-3:40 pm – Break
3:40-5:20 pm – 4 presentations

Day 2 – Wednesday, June 12th

On Wednesday, the SciComm session will continue with 5 formal presentations, and a synthesis discussion in the morning and a Skills Café in the afternoon.

Wednesday Morning (Edwards Hall Room 103)

8:00-9:20 am – 3 presentations
9:20-9:40 am – Break
9:40-10:20 am – 2 presentations
10:20-11:00 am – Interactive Synthesis Discussion and Question & Answer Session with Peter Annin (Author), Andrea Densham (Shedd), Sandra Svoboda (DPTV) & TJ Pignataro (Buffalo News)

Wednesday Afternoon (Edwards Hall Room 102)

1:40 – 5:00 pm – Skills Café – This series of short interactive workshops will allow participants to practice a variety of skills that will make them more effective at communicating the “so what” of their research to lay – but key – audiences.

The Panel: Hear the latest from science communication thought leaders!

Peter Annin, Author and Director of the Mary Griggs Burke Center for Freshwater Innovation

Peter Annin is the director of the Mary Griggs Burke Center for Freshwater Innovation and the author of The Great Lakes Water Wars, the definitive work on the Great Lakes water diversion controversy. Before coming to Northland College in 2015, Peter served as a reporter at Newsweek, the associate director of the Institute for Journalism and Natural Resources, and the managing director of the University of Notre Dame’s Environmental Change Initiative. He continues to report on the Great Lakes water diversion issue and has published a second edition of The Great Lakes Water Wars.

Andrea Densham, Senior Director of Conservation and Advocacy at the Shedd Aquarium

Andrea Densham joined Shedd Aquarium in 2017 to lead the newly launched Conservation Policy and Advocacy team. Created to enhance Shedd’s position as a policy expert, Densham’s team develops and implements the institution’s policy goals. A government affairs thought leader and advisor, she brings more than 20 years of experience in not-for-profit management, strategic planning, research, and public policy and advocacy.

TJ Pignataro, Environmental Reporter for the Buffalo News

T.J. Pignataro has been a staff reporter for The Buffalo News for more than 20 years and the environment and weather reporter since 2013. He holds a juris doctor degree from SUNY Buffalo Law School and is completing his Certificate in Weather Forecasting this spring from the Pennsylvania State University’s Department of Meteorology and Atmospheric Science. TJ uses Twitter to convey Great Lakes environmental news, weather emergencies and Great Lakes science in plain language.

Sandra Svoboda, Program Director, Great Lakes Now, Detroit Public Television

A nine-month stint with The Associated Press brought Sandra to Detroit … 29 years ago. She earned a bachelor’s in journalism from Indiana University and holds two master’s degrees from Wayne State, one in public administration and one in library and information science. The Special Libraries Association IT Division recognized her research with its 2018 Joe Ann Clifton Student Award for her paper on how Detroit voting dynamics can inform citizen engagement strategies. Sandra has worked for The (Toledo) Blade covering education/children’s issues, Detroit’s Metro Times and FEMA, where she deployed to Louisiana to help coordinate/communicate about community rebuilding/planning efforts for/after disasters. Sandra has won awards for broadcast, print, digital and community engagement work from the Michigan Associated Press, the Michigan Association of Broadcasters, Association of Alternative Newsweeklies, State Bar of Michigan, Michigan Press Association and Society of Professional Journalists-Detroit chapter, and Wayne State’s public administration program recognized her with the Distinguished Alumni Award in 2015 for her work covering Detroit’s bankruptcy. She has taught communications, writing, public policy, and political science at Wayne State University and the University of Michigan-Dearborn. As the Great Lakes region has always been her home Sandra has traveled between Minnesota and Tadoussac, Quebec, both on the water and on land. A competitive sailor, she races hundreds of miles each season on the Great Lakes, and once threw out a pitch at a Detroit Tigers game as recognition of her win with her team at the U.S. Women’s Match Racing Championship. She’s also eaten Asian carp as part of her coverage of invasive species.

The Skills Café: Get help communicating your research!

WHO: Do people’s eyes glaze over when you begin to talk about your research? Do you believe your research has the ability to make a difference, but you’re not sure how to get others excited about it too? Then this session is for you! For the researcher looking to improve their accessibility in attaining broader impacts; the early career professional seeking tips on how to set theirselves apart in a competitive market; the passionate scientist looking for ways to ensure their work makes an impact . . . the Skills Cafe is your opportunity to grow and try new things in a fun and supportive setting.

WHAT: This series of short interactive workshops will allow participants to practice a variety of skills that will make them more effective at communicating the “so what” of their research to lay—but key—audiences. Get tips on interacting with the media, hone your speaking skills, get feedback from a mock interview, and learn from the trials and tribulations of your peers!

WHEN: 1:40-5:00 pm on Wednesday, June 12.

WHERE: Edwards Hall, Room 102

For more information and a detailed schedule of activities stop by the NOAA exhibitor booth.

About IAGLR 2019: The 2019 International Association for Great Lakes Research Conference is hosted by The College at Brockport, State University of New York, June 10-14, 2019. The conference will feature four days of scientific sessions and speakers focusing on the theme “Large Lakes Research: Connecting People and Ideas.”

photo of building in water with skyline of city in backgroun

March 4, 2019
by Nicole Rice Leave a comment

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

photo of two man on boat pulling in large metal device on crane

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

photo of many on boat bending over and working on scientific instrument

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.

photo of man looking at container filled with sediment

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

December 6, 2018
by Kaye LaFond 2 Comments

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.

NOAA Great Lakes Environmental Research Laboratory

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

Category Archives: Research