NOAA Great Lakes Environmental Research Laboratory

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


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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.

Related news articles and blog posts:

From the CIGLR Winter 2020 eNewsletter – Improving Lake Effect Snow Forecasts

NOAA Research News, April 2019 NOAA research yields better lake-effect snow forecasts

NOAA GLERL Blog, 2018 – Improving lake effect snow forecasts by making models talk to each other

Related research papers: 

Fujisaki-Manome et al. (2020) Improvements to lake-effect snow forecasts using a one-way air-lake model coupling approach. 

Anderson et al. (2019) Ice Forecasting in the Next-Generation Great LakesOperational Forecast System (GLOFS) 

Fujisaki-Manome et al. (2017) Turbulent Heat Fluxes during an Extreme Lake-Effect Snow Event

Xue et al. (2016) Improving the Simulation of Large Lakes in Regional Climate Modeling: Two-Way Lake–Atmosphere Coupling with a 3D Hydrodynamic Model of the Great Lakes

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.

habtracker2018

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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GLERL Ocean(lake)ographer Eric Anderson on watching the Straits of Mackinac

Eric Anderson, GLERL oceanographer, used to study the movement of fluid inside bone tissue — now he studies the movement of water in the Great Lakes.

Eric Anderson is NOAA GLERL’s resident oceanographer (but his Twitter handle is @lakeographer—you should trademark that one, Eric). At its core, his research centers around the movement of water. You might have seen our animations of currents in the Straits of Mackinac, or of meteotsunamis coming across Lake Michigan — he’s the guy behind those computer models.

Some cool things about Eric are that he plays the banjo, that he used to study the movement of fluid inside bone tissue, and that he’s quick to remind us people were watching the Straits of Mackinac millennia before his computer models existed. Read on to learn more cool things!

How would you describe your job?

My research is on hydrodynamics, which is a fancy way of saying the moving physical aspects of the water in the Great Lakes—things like currents, temperatures, ice, and waves. Most of my day is built around looking at measurements of the water and air and then developing computer models that simulate how the lakes respond to different weather conditions. This field of science is particularly helpful in safe navigation of the lakes, responding to contaminant spills, search and rescue operations, and understanding how the ecosystem responds to different lake conditions.

What is the most interesting thing you’ve accomplished in your job?

Maybe the most rewarding has been working on the Straits of Mackinac. It’s one of the most beautiful spots in the Great Lakes, but also one of the most dynamic, with high-speed currents changing every few days, if not hours. A groundswell of attention to the Straits in the last several years has pushed the public to get more engaged and learn about the conditions in the Straits, and I’ve been glad to help where I can.

As part of this work, we’ve found some 1600’s-era [settler] written accounts of the currents in the Straits. We also know that [Indigenous] people have been watching the Straits for thousands of years, and it’s rewarding to continue this thread of knowledge.

What do you feel is the most significant challenge in your field today?

It seems like the hardest thing is to communicate the science. People are starved for information, and there’s a real love out there for learning about the Great Lakes. All we can do is to try and keep the flow of information getting out to the folks who care, and just as important, to those who don’t think they care. When you see environmental science covered in the news, it’s usually reporting on something negative or even catastrophic, which is certainly important, but there are pretty cool discoveries being made routinely, big and small, and those don’t often seem to make it to the headlines. We have to keep working hard to make sure these stories make it out, and at the same time keep our ears open to the concerns that people have for the lakes.

Where do you find inspiration? Where do your ideas come from in your research or other endeavors in your job?

Inspiration is everywhere. Try to hike up to a good vantage point overlooking the lake, like the dunes or a bluff, and not feel inspired. More often, though, inspiration comes from talking with other people, whether scientists, students, or interested members of the public. I can’t think of a time where I’ve given a public seminar and not walked away with a new question or idea to investigate. People’s enthusiasm and bond with the Great Lakes is infectious, and so I try to tap into that as often as I can.

Two meteotsunamis, large waves caused by storm systems, came across Lake Michigan on April 13, 2018. Eric Anderson models meteotsunamis in his role as oceanographer at NOAA GLERL.

How would you advise high school students interested in science as a career path, or someone interested in your particular field?

I took somewhat of a winding career path to get where I’m at with GLERL, working in car assembly plants and then on the nano-fluidic flow inside bone tissue before ending up in physical oceanography. I didn’t really know what I wanted in high school or college, but I knew physics and math were where I felt at home. So I found a way to learn the fundamentals that I’ve been able to apply in each of these jobs, and that allowed me to explore different parts of science and engineering. Not everyone will have the same chances or opportunities, but if you can find a way to really solidify the fundamentals and just as importantly seek out a breadth of experiences, you’ll be in a better position when those opportunities do come along.

What do you like to do when you AREN’T sciencing?

I’m either hanging out with family, playing music, or talking with someone about how I wish I was playing more music.

What do you wish people knew about scientists or research?

By and large, science is curiosity driven, often fueled by the scientist’s own enthusiasm, and in my case also by the interests of the public. Whether it’s a new discovery, or re-codifying or quantifying something that others have observed for millennia, there’s no agenda here other than to understand what’s happening around us and share whatever pieces we can make sense of. I’ll add a sweeping generalization that scientists love to talk about their research, so don’t be afraid to ask.


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Great Lakes in winter: Water levels and ice cover

The Great Lakes, along with their connecting waterways and watersheds, make up the largest lake system on the planet—more than 20% of the world’s surface freshwater! Water levels on the lakes change in response to a number of factors, and these changes can happen quickly. Changing water levels can have both positive and negative impacts on shipping, fisheries, tourism, and coastal infrastructure like roads, piers, and wetlands.

Currently, water levels on all of the Great Lakes are above their monthly averages, and have been developing since the spring of 2013, when a record-setting two-year rise in water levels began on the upper Great Lakes. Extreme conditions in spring of 2017 produced flooding and widespread damage at the downstream end of the basin—Lake Ontario and the St. Lawrence River. In case you missed it, check out our infographic on this flooding event.

So, what’s happening now that it’s winter?

As we entered the late fall-early winter of 2017-2018, a warm weather pattern had forecasters looking toward a fairly warm winter. However, in late December, the conditions changed and a much colder than normal weather pattern took many folks living in the Great Lakes by surprise. Much like how water levels can change quickly in the Great Lakes, so can ice cover. Due to frigid air temperatures, between December 20 and January 7, total ice cover on the lakes jumped 26.3%. Lake Erie alone jumped up to nearly 90%!

 

 

After January 7th, ice coverage dropped a bit as the air temperatures warmed, then rose again as temperatures went back down, showing again how vulnerable the lakes are to even the slightest changes. Compare where we are now to where we were 2 years ago at this time, and you’ll easily see how variable seasonal ice cover can be in the Great Lakes.

Image depicting Great Lakes total ice cover on on January 15, 2018, compared to 2017 and 2016.

What’s the outlook for ice and water levels?

Below, you’ll find what GLERL researchers expect to see for ice cover this winter, as well as the U.S. Army Corps’ water levels forecast into Spring 2018. Be sure to read further to find out more about the science that goes into these predictions!

—GLERL’s 2018 Seasonal Ice Cover Projection for the Great Lakes—

On 1/3/2018, NOAA’s Great Lakes Environmental Research Laboratory updated the maximum 2018 Great Lakes basinwide ice cover projection to 60%. The long-term average is 55%. The updated forecast reflects changes in teleconnection patterns (large air masses that determine our regional weather) since early December 2017—movement from a strong to a weak La Nina, a negative to a positive Pacific Decadal Oscillation, and a positive to a negative North Atlantic Oscillation. These patterns combine to create colder than average conditions for the Great Lakes.

—Water Levels forecast into spring 2018—

According to the most recent weekly water level update from the U.S. Army Corps, water levels for all of the Great Lakes continue to be above monthly average levels and above last year’s levels at this time. All of the lakes have declined in the last month.  Note that ice developing in the channels and on the lake surface can cause large changes in daily levels during the winter, especially for Lake St. Clair. Over the next month, Lake Superior and Lake Michigan-Huron are expected to continue their seasonal decline. Lake St.Clair, Lake Erie, Lake Ontario are expected to begin their seasonal rise.


 

More information on water levels and ice cover forecasting

How are water levels predicted in the Great Lakes?

Forecasts of Great Lakes monthly-average water levels are based on computer models, including some from NOAA GLERL, along with more than 150 years of data from past weather and water level conditions. The official 6-month forecast is produced each month through a binational partnership between the U.S. Army Corps of Engineers and Environment and Climate Change Canada.

At GLERL, research on water levels in the Great Lakes analyzes all of the components of the Great Lakes water budget. The information we gather is used to improve forecast models. The infographic below goes into more detail about the Great Lakes water budget.

Image depicting the makeup of water budgets in the Great Lakes

How does winter ice cover affect water levels?

As mentioned in the recently released Quarterly Climate Impacts and Outlook for the Great Lakes, water levels in the Great Lakes tend to decline in late fall and early winter, mainly due to reduced runoff and streamflow combined with higher over-lake evaporation caused by the temperature difference between air and water. Factors such as surface water temperatures, long stretches of cold or warm air temperatures, and winds all impact the amount of lake ice cover as well as extreme winter events, such as lake-effect snow—which we’ve already seen plenty of this winter—and vice versa. All of these factors influence winter water levels in the Great Lakes. The timing and magnitude of snow melt and spring runoff will be major players in the spring rise.

Looking for more info?

You can find more about GLERL’s water levels research, on this downloadable .pdf of the GLERL fact sheet on Great Lakes Water Levels.

View current, historical, and projected water levels on the Great Lakes Water Levels Dashboard at https://www.glerl.noaa.gov/data/dashboard/portal.html.

For more on GLERL’s research on ice in the Great Lakes, check out the Great Lakes Ice fact sheet, or check out our website at https://www.glerl.noaa.gov/data/ice/.

Want to see a really cool graphic showing the extent of the maximum ice cover on the Great Lakes for each year since 1973? You’ll find that here.

 


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New algorithm to map Great Lakes ice cover

Leshkvich sampling ice

GLERL researcher, George Leshkevich, drilling through the ice in Green Bay, Lake Michigan.

NOAA’s Great Lakes Environmental Research Laboratory (GLERL) is on the cutting edge of using satellite remote sensing to monitor different types of ice as well as the ice cover extent. To make this possible, an algorithm—a mathematical calculation developed at GLERL to retrieve major Great Lakes ice types from satellite synthetic aperture radar (SAR) data—has been transferred to NOAA’s National Environmental Satellite, Data, and Information Service (NESDIS) for evaluation for operational implementation.

Once operational, the algorithm for Great Lakes ice cover mapping holds multiple applications that will advance marine resource management, lake fisheries and ecosystem studies, Great Lakes climatology, and ice cover information distribution (winter navigation).  Anticipated users of the ice mapping results include the U.S. Coast Guard (USCG), U.S. National Ice Center (NIC), and the National Weather Service (NWS).

For satellite retrieval of key parameters (translation of satellite imagery into information on ice types and extent), it is necessary to develop algorithms specific to the Great Lakes owing to several factors:

  • Ocean algorithms often do not work well in time or space on the Great Lakes
  • Ocean algorithms often are not tuned to the parameters needed by Great Lakes stakeholders (e.g. ice types)
  • Vast difference exists in resolution and spatial coverage needs
  • Physical properties of freshwater differ from those of saltwater

The relatively high spatial and temporal resolution (level of detail) of SAR measurements, with its all-weather, day/night sensing capabilities, make it well-suited to map and monitor Great Lakes ice cover for operational activities. Using GLERL and Jet Propulsion Lab’s (JPL) measured library of calibrated polarimetric C-band SAR ice backscatter signatures, an algorithm was developed to classify and map major Great Lakes ice types using satellite C-band SAR data (see graphic below, Methodology for Great Lakes Ice Classification prototype).

ICECON (ice condition index) for the Great Lakes—a risk assessment tool recently developed for the Coast Guard—incorporates several physical factors including temperature, wind speed and direction, currents, ice type, ice thickness, and snow to determine 6 categories of ice severity for icebreaking operations and ship transit.  To support the ICECON ice severity index, the SAR ice type classification algorithm was modified to output ice types or groups of ice types, such as brash ice and pancake ice to adhere to and visualize the U.S. Coast Guards 6 ICECON categories. Ranges of ice thickness were assigned to each ice type category based on published freshwater ice nomenclature and extensive field data collection. GLERL plans to perform a demonstration/evaluation of the ICECON tool for the Coast Guard this winter.

Mapping and monitoring Great Lakes ice cover advances NOAA’s goals for a Weather-Ready Nation and Resilient Coastal Communities and Economies, and Safe Navigation. Results from this project, conducted in collaboration with Son V. Nghiem (NASA/Jet Propulsion Laboratory), will be made available to the user community via the NOAA Great Lakes CoastWatch website (https://coastwatch.glerl.noaa.gov).

 

ice-types

ICECON Scale

Measuring different ice types on Green Bay used to validate the ICECON (ice type classification) Scale in a RADARSAT-2 synthetic aperture radar (SAR) scene taken on February 26, 2017.

 


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Update on Lake Erie hypoxia forecasting stakeholder workshop (May 23, 2017)

Researchers partner with drinking water plant managers to forecast hypoxia in Lake Erie

By Devin Gill, Cooperative Institute for Great Lakes Research and Kristin Schrader, Great Lakes Observation Systems

Lake Erie’s “dead zone” not only impacts the lake’s ecosystem, but also poses challenges for managers of drinking water treatment facilities. The Lake Erie dead zone is a region of the central basin where oxygen levels within the water become extremely low, creating a condition known as hypoxia. Great Lakes researchers are sharing their scientific expertise to help managers be fully prepared for threats to drinking water resulting from hypoxic conditions.

Scientists from NOAA GLERL, Cooperative Institute for Great Lakes Research (CIGLR) and the Great Lakes Observing System (GLOS) met on May 23 in Cleveland, Ohio with water plant managers from the southern shore of Lake Erie for a stakeholder engagement workshop to discuss the hypoxia issue. An important focus of the workshop was the development of a new hypoxia forecast model that will act as an early warning system when hypoxic water has the potential to enter intakes of water treatment facilities. The depletion of oxygen in hypoxic water occurs when the water column stratifies (separates into warm and cold layers that don’t mix). Oxygen in the lower, cold layer becomes depleted from the lack of mixing with the upper (warm) layer that is exposed to air, as well as from the decomposition of organic matter (dead plants and animals) in the lower layer. The process of hypoxia is illustrated by GLERL’s infographic, The Story of Hypoxia.

Stakeholders who attended the workshop explained that water treatment operators must be prepared to respond quickly during a hypoxic event to ensure that drinking water quality standards are met. Hypoxic water often is associated with low pH and elevated manganese and iron. Manganese can cause discoloration of treated water, while low pH may require adjustment to avoid corrosion of water distribution pipes, which can introduce lead and copper into the water.

At the workshop, researchers shared information on lake processes that contribute to hypoxia and on development of the Lake Erie Operational Forecasting System that provides nowcasts and forecasting guidance of water levels, currents, and water temperature out to 120 hours, and is updated 4 times a day. Information was also shared on preliminary hypoxia modeling results that simulated an upwelling event (wind-driven motion in the Great Lakes, pushing cooler water towards the lake surface, replacing the warmer surface water) that brought hypoxic water to several water plant intakes in September, 2016. Water plant managers reported that advance notice of a potential upwelling event that could bring hypoxic water to their intakes would be useful to alert staff and potentially increase the frequency of testing for manganese.

Dr. Mark Rowe from University of Michigan, CIGLR, researcher and co-lead on this initiative, comments on the value of this hypoxia stakeholder engagement workshop: “At both NOAA and the University of Michigan, there is an increasing focus on co-design of research, which refers to involving the end-users of research results throughout the entire project, from concept to conclusion. If we succeed, a new forecast model will be developed that will be run by the operational branch of NOAA. This can only happen if there is a group of users who request it. This workshop provided critical information to the researchers regarding the needs of the water plants, while also informing water plant managers on how forecast models could potentially help them plan their operations, and on the latest scientific understanding of hypoxia in Lake Erie. ”

Stakeholder Scott Moegling, Water Quality Manager at City of Cleveland Division of Water, also recognizes the value of  engagement between the stakeholders and the Great Lakes researchers. Moegling points out that “the drinking water plant managers not only benefit from sharing of operational information and research, but also by establishing lines of communication between water utilities and researchers that help identify common areas of interest. The end result—researchers providing products that can be immediately used by water utilities—is of obvious interest to the water treatment industry on Lake Erie.”  Moegling also views the GLERL/CIGLR research on the hypoxia forecast model as holding great potential in predicting hypoxic conditions in Lake Erie and believes that once the model is developed and calibrated, there may be a number of other possibilities for highly useful applications.

In addition to sharing the latest research on hypoxia, the stakeholder engagement workshop provided a forum for water plant managers to share information with each other on how to recognize hypoxic events and efficiently adjust water treatment processes. Researchers at CIGLR and NOAA GLERL are committed to conduct research that serves society, and will continue to work with this stakeholder group over the course of the five-year project to develop a hypoxia forecast model that meets their needs.

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“Just Because the Blooms in Lake Erie Slow Down, Doesn’t Mean We Do”

NOAA GLERL harmful algal blooms research program featured on Detroit Public Television

As part of a series on The Blue Economy of the Great Lakes, NOAA’s Great Lakes Environmental Research Laboratory (GLERL) is featured in a short video, produced by Detroit Public Television (DPTV) and published on the DPTV website. The video, which features GLERL and its partners from the Cooperative Institute for Great Lakes Research (CIGLR, known formerly as CILER), describes the advanced technology GLERL uses to monitor, track, predict, and understand harmful algal blooms (HABs) in the Great Lakes. More specifically, the video focuses on efforts in Lake Erie, where over 400,000 people were affected by a 3-day shutdown of the Toledo drinking water treatment facility in 2014. Since then, GLERL and CIGLR have enhanced their HABs research program—much of which is made possible by funding from the Great Lakes Restoration Initiative, or GLRI—to include cutting-edge technologies such as the hyperspectral sensors and an Environmental Sample Processor (ESP), as well as experimental forecasting tools like the Lake Erie HAB Tracker.

In addition to online coverage, the video will be broadcast via DPTV at a future time, yet to be determined.

View the video above, or visit http://bit.ly/2pK2g0J.

Aerial photo survey improves NOAA GLERL’s Lake Erie ice model

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Understanding the duration, extent, and movement of Great Lakes ice is important for the Great Lakes maritime industry, public safety, and the recreational economy. Lake Erie is ice-prone, with maximum cover surpassing 80% many winters.

Multiple times a day throughout winter, GLERL’s 3D ice model predicts ice thickness and concentration on the surface of Lake Erie. The output is available to the public, but the model is under development, meaning that modelers still have research to do to get it to better reflect reality.

As our scientists make adjustments to the model, they need to compare its output with actual conditions so they know that it’s getting more accurate. So, on January 13th of this year, they sent a plane with a photographer to fly the edge of the lake and take photos of the ice.

The map below shows the ice model output for that day, along with the plane’s flight path and the location of the 172 aerial photos that were captured.

NOAA GLERL Lake Erie ice model output with all aerial photo survey locations -- January 13, 2017. Credit NOAA GLERL/Kaye LaFond.

NOAA GLERL Lake Erie ice model output with all aerial photo survey locations — January 13, 2017. Map Credit NOAA GLERL/Kaye LaFond.

These photos provide a detailed look at the sometimes complex ice formations on the lake, and let our scientists know if there are places where the model is falling short.

Often, the model output can also be compared to images and surface temperature measurements taken from satellites. That information goes into the GLSEA product on our website (this is separate from the ice model). GLSEA is useful to check the ice model with. However, it’s important to get this extra information.

“These photographs not only enable us to visualize the ice field when satellite data is not available, but also allow us to recognize the spatial scale or limit below which the model has difficulty in simulating the ice structures.” says Eric Anderson, an oceanographer at GLERL and one of the modelers.

 “This is particularly evident near the Canadian coastline just east of the Detroit River mouth, where shoreline ice and detached ice floes just beyond the shoreline are not captured by the model. These floes are not only often at a smaller spatial scale than the model grid, but also the fine scale mechanical processes that affect ice concentration and thickness in this region are not accurately represented by the model physics.”

Click through the images below to see how select photos compared to the model output. To see all 172 photos, check out our album on Flickr. The photos were taken by Zachary Haslick of Aerial Associates.

 

This gallery contains 10 photos


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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: