NOAA Great Lakes Environmental Research Laboratory

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


Leave a comment

GLERL receives two RTAP awards for transitioning HABs and ice forecast model to operations

GLCFS_nextgenf1

The 3rd generation NOAA GLERL Great Lakes Coastal Forecasting System (GLCFS) uses an unstructured grid (i.e., triangular shapes of adaptable size) to better model physical processes

GLERL’s Dr. Eric Anderson has recently been awarded funding from the Research Transition Acceleration Program (RTAP), placing two of GLERL’s FVCOM modeling projects on the fast track to transition from research to operations (R2O).  R2O is the pathway by which fundamental research is developed into a useful tool or product and implemented into an automated or operational environment accessible for use by the public. RTAP, a highly competitive grants program, prioritizes projects based on their ability to advance NOAA’s mission and benefit society with the ultimate goal of accelerating the transition of promising NOAA research to operations and applications.

Anderson focuses primarily on hydrodynamics, using computer modeling to study how forcing conditions, such as meteorological (weather) events, affect the motion and energy of a body of water. His research on the physical nature of the Great Lakes in response to natural forces is improving our ability to make predictions on currents, temperature, water levels, waves, harmful algal blooms (HABs), and ice characteristics. The RTAP awards will provide Anderson and his collaborative team of researchers the resources needed to advance the following two projects: “Implementation of a 3D HAB forecast model for Lake Erie using FVCOM” and “Implementation of the FVCOM-Ice model for the Great Lakes Operational Forecasting System (GLOFS).” Project outcomes will support services such as safe drinking water, recreation, and navigation.

GLCFS_FVCOM vs POM grid

Notably, both forecast models are built upon the Finite Volume Community Ocean Model (FVCOM), an open-source community model that uses an unstructured grid (triangular shapes of adaptable size) to represent the Great Lakes and connecting channels (such as the coastline illustrated above) with increased grid resolution and model accuracy.  FVCOM solves the three-dimensional (3-D), integral form of the equations of motion.   This modeling approach also provides for an established framework for coupled modules (interconnection between the biological and physical components in the ecosystem, such as biological processes, currents, sediment, ice, etc.).  The seminal research paper explaining the structure and function of the FVCOM is provided in the Oceanography journal article, “An Unstructured Grid, Finite Volume Coastal Ocean Model FVCOM System” (Chen, et al., 2006) with further background on the FVCOM and its research application available on GLERL’s webpage, Great Lakes Coastal Forecasting: Next Generation.

HABcast122

Example of the HAB Tracker forecast showing surface extent and intensity of the bloom from 2015

The first of the RTAP awards listed above will enable Anderson with a group of NOAA partners to accelerate the implementation of a 3-D harmful algal bloom (HAB) forecast model by at least two years— providing decision makers with unprecedented real-time information on HAB extent, vertical distribution, and concentration. The experimental version of the model, known as the “HAB Tracker,” was first developed by GLERL in 2014 and has since been improved in collaboration with the National Ocean Service (NOS) National Centers for Coastal Ocean Science (NCCOS) as a tool that combines remote sensing and modeling to produce daily 5-day forecasts of bloom transport and concentration. The HAB Tracker is based on the 3-D FVCOM Lagrangian particle model, a sub-component of the FVCOM hydrodynamic model system currently being transitioned to operations. This transition will occur on NOAA’s high performance computing system for the NOAA production suite by NOS’ Center for Operational Oceanographic Products and Services (CO-OPS) as part of the next-generation Lake Erie Operational Forecasting System (LEOFS).

LakeErieIce.png

Example of the FVCOM-Ice model forecast of ice concentration from winter 2017

The second RTAP grant awarded to GLERL will facilitate incorporation of an ice model (FVCOM-Ice) in the Great Lakes Operational Forecasting System (GLOFS) by directly coupling it with the hydrodynamic FVCOM model.  RTAP funding will provide the personnel and infrastructure needed to support the development, validation, and implementation of the FVCOM coupled hydrodynamic-ice model and accelerate transition as part of the GLOFS upgrade. This transition to operations will provide the first-ever ice forecasts of extent/concentration, thickness, and velocity for the Great Lakes. The process will occur first for the Lake Michigan-Huron Operational Forecast System (LMHOFS) and then add to the existing Lake Erie Operational Forecast System (LEOFS). The coupled hydrodynamics-ice modeling systems for Lakes Michigan, Huron, and Erie will provide users with operational 120-hour forecast guidance of ice conditions, water temperature, currents, and water levels, updated four times per day during the winter as well as spring months.

Anderson recognizes the value of these RTAP awards by providing “the resources and personnel we need across Line Offices to validate and transition these models into operations, and avoid the so-called ‘valley of death’ between fundamental research and operational applications.”


Leave a comment

A message from the Director: Integrating science-based adaptive management into GLERL research

One thing that can be said with certainty about the Great Lakes ecosystem, is that it is in a constant state of change. The primary question for NOAA’s Great Lakes Environmental Research Laboratory (GLERL) is, how can we most effectively research and manage the lakes given their changing biological, physical, and chemical conditions? The answer, in part, is to build our capabilities in taking an integrated, science-based adaptive management approach in the conduct of research and ecosystem management.

Adaptive management—a concept that has been evolving in the Great Lakes region since enactment of the 1972 Great Lakes Water Quality Agreement (GLWQA)—integrates well-defined feedback loops in the process of doing science-based research and management, thus providing a way to respond to ecosystem changes. The ultimate goal of using an adaptive approach is to continually evolve the research and management of the Great Lakes ecosystem while accounting for uncertainty in the conduct of science. Though it could be said that adaptive management is a common sense, verify as you go approach, in order to render a significant impact in the mitigation of problems/stressors threatening the Great Lakes, an integrated, science-based, adaptive management approach must be purposefully executed and institutionalized on a long-term basis with reliable funding.

So what do we really mean by taking a science-based, adaptive management approach? And how are we doing it?  The International Joint Commission (IJC), established by the United States and Canada to prevent and resolve disputes about the use and quality of the Great Lakes boundary waters, has played an important role in shaping adaptive management as an approach to protect and restore the Great Lakes. Through the lens of the IJC, “Adaptive management is a planning process that provides a structured, iterative approach for improving actions through long-term monitoring, modelling, and assessment. Adaptive management allows decisions to be reviewed, adjusted, and revised as new information and knowledge becomes available, and/or as conditions change.”  (Upper Great Lakes Lakes Study, IJC 2012).  There is growing awareness that we need to be adaptive in our approach given that managed resources will always change as a result of human intervention, that surprises are inevitable, and that new uncertainties will emerge. Adaptive management should not be considered a ‘trial and error’ process but rather one that is built on “learning while doing.” (Williams et al., 2007).

At GLERL, we are striving to integrate adaptive management in a deliberate way in the design, conduct, and overall management of our research projects. On the most basic level, adaptive management provides a framework upon which research is structured, using measurable goals and objectives to assess and evaluate outcomes with each cycle of research. The role that adaptive management is expected to play in GLERL research is delineated in GLERL’s 2016 Strategic Plan (pp. 17-23). This approach is exemplified by research on the causes and impacts of harmful algal blooms (HABs) and hypoxia (a condition when oxygen levels within the water become extremely low) in western Lake Erie as conducted by GLERL, in conjunction with Cooperative Institute for Great Lakes Research (CIGLR, formerly CILER). Further information on GLERL’s HABs and hypoxia research is available on GLERL’s webpage, Great Lakes HABs and Hypoxia.

We view the process of adaptive management guiding Great Lakes scientific research and ecosystem management as a coupled feedback loop (see below graphic, Adaptive Integrated Research Framework) driven by water quality/quantity problems, stakeholder engagement, and existing policy (e.g., NOAA/GLERL mission and vision, 2012 amended GLWQA). As an example, it has been well established that HABs and hypoxia threaten the Great Lakes ecosystem and ecological services provided by the lakes as well as pose human health risks and socio-economic impacts. Importantly, stakeholder engagement continues to play a key role in articulating these problems and guiding priorities in the conduct of HABs/hypoxia research, such as the following:

  • Reducing nutrient loading of phosphorus and nitrogen.
  • Understanding impacts of HABs on food web structure and potential impacts on fisheries, increased water treatment costs, lost opportunity costs for recreation, and shoreline property values.
  • Understanding toxicity level impacts on human health.

The next step in an adaptive management approach is formulating research goals, objectives and questions—based on identified priorities—that are measurable and can result, in part, from stakeholder engagement. A measurable goal established for HABs research and management is a 40 percent target reduction in spring loads of phosphorus to minimize the size and impact HABs in western Lake Erie. Fundamental to an adaptive management approach is the measurement of progress toward reaching the research and management goals and making adjustments accordingly.

Another important driver in the adaptive management cycle is feedback based on the assessment and evaluation of research and management results and other outcomes. The transfer of results/outcomes to the scientists, managers, as well as stakeholders, provides an opportunity for the adaptive approach to refine and improve the next round of HABs research. For example, recent HABs research has pointed to nitrogen as an important driver of bloom toxicity; these findings have played an important role in shaping GLERL’s future research agenda.

In our ongoing commitment to serve the Great Lakes community through our research, GLERL’s efforts can only be strengthened through adaptive management by ensuring that stakeholders—such as water intake managers, fisheries managers, land use managers, public health agencies, environmental groups, and the general public—are given the products and tools needed to mitigate the sources and impacts related to HABs and hypoxia (see story on hypoxia stakeholder workshop). This approach holds great promise in improving the ecological as well as economic health of the Great Lakes region.

Deborah H. Lee, PE, PH, D.WRE
Director, NOAA GLERL

Adaptive Integrated research framework at GLERL

This diagram was developed to depict the adaptive, integrated approach that characterizes GLERL’s scientific research. The iterative, longterm, systematic process of using an adaptive integrated research framework provides an opportunity to refine research and ecosystem management approaches. The cycle of an adaptive integrated research framework used in conjunction with the best available science, provides iterative feedback loops incorporated as part of GLERL’s research methodology. The coupled feedback loops depicted above show the interrelationship between research management and ecosystem management, both driven by assessment and evaluation as well as stakeholder input.


4 Comments

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.

This slideshow requires JavaScript.


Leave a comment

NOAA GLERL staff participate in community education events

Two opportunities to highlight NOAA’s mission of Science, Service and Stewardship

This slideshow requires JavaScript.

NOAA’s Great Lakes Environmental Research Laboratory (GLERL) remote sensing scientist, Dr. George Leshkevich, and Information Services team member, Katherine Glassner-Shwayder, participated in Washtenaw Community College’s (WCC) Earth Day Month Celebration on April 6 on campus in Ann Arbor Michigan. In celebration of Earth Month, information was presented about a diverse array of solutions to today’s environmental challenges by local non-profit, business, and government organizations, student clubs (nursing and sustainability) and WCC departments. One such featured challenge was on “Our Amazing Earth” to help students understand the science behind the intricate balance of nature, protect the Great Lakes, help green our campus, and find a green career.

In representing GLERL At the event, George and Kathe shared information on our research, focused on NOAA’s Great Lakes CoastWatch animations, illustrating retrospective satellite observations and in-situ Great Lakes data; research on Great Lakes ecosystem dynamics and threats posed by aquatic invasive species; Great Lakes geography; and information on the laboratory’s Summer Fellows Program (coordinated by the University of Michigan’s Cooperative Institute for Great Lakes Research (CIGLR), in conjunction with GLERL).

The WCC Earth Month Celebration provided a valuable opportunity for GLERL to raise awareness and understanding for the science driving the Great Lakes ecosystem among a diverse group of college students and community members. WCC faculty member and event coordinator, Dale Petty, commented that NOAA GLERL played an important role at WCC’s Earth Month Celebration, presenting the science about what’s happening in our environment.

IMG_9240

Father and son show off their artwork from the GLERL/CIGLR “Create an Invasive Species” activity during Huron Intermediate Schools STEAM Showcase on May 13.

In addition to the event at WCC, NOAA GLERL Information Services team member, Nicole Rice, along with Michele Wensman from CIGLR spent the day in Bad Axe, Michigan, participating in the Huron Intermediate School District’s Thumb Area STEAM Showcase on May 13. During the event, students from Huron County schools showcased their work with the Science Olympiad, the MDOT Bridge Building Challenge, robotics, drones, music, art, and more. Exhibits included a trebuchet, an interactive planetarium, a petting zoo, live music, and a variety of hands-on activities.

The GLERL/CIGLR exhibit featured information about the lab’s science programs and CIGLR’s student opportunities, and interactive activities such as 3D bathymetry maps, a Great Lakes quiz, and a crafting area where kids could create their own invasive species or make a monster from a Great Lake. Check out the photo album on Facebook full of eager participants and their creative creatures!


Leave a comment

NOAA GLERL collaborating with partners to monitor the Lake Huron ecosystem

This slideshow requires JavaScript.

The NOAA Great Lakes Environmental Research Laboratory (GLERL) is participating in an international, multi-agency effort to study invasive species, water quality, fisheries, and climate change in Lake Huron this field season—pursuing key knowledge gaps in the ecosystem. The Coordinated Science and Monitoring Initiative (CSMI) coordinates across U.S. and Canadian agencies to conduct intensive sampling in one Great Lake per year, on a five-year cycle. The Great Lakes Restoration Initiative, which is administered by the U.S Environmental Protection Agency (EPA), is funding this research.

“While GLERL has had a long-term research program focused on Lake Michigan, we are using this initiative to advance long-term research on Lake Huron,” said GLERL Director Deborah Lee. “Invasive species, warming temperatures, and changes in nutrient loading are putting as much stress on Lake Huron as on Lake Michigan. We want to better understand the Lake Huron ecosystem and develop modeling tools to predict how the lake is changing.”

Henry Vanderploeg, Ph.D., chief of GLERL’s Ecosystem Dynamics research branch and lead researcher for GLERL’s efforts in the pelagic (open water) portion of the initiative comments, “GLERL plays a critical role in the CSMI, addressing key science questions. GLERL’s high frequency temporal and spatial sampling will help determine nutrient and energy flows from tributaries, nearshore to offshore. This type of data is critical to effectively manage Lake Huron for water quality and fish production.” Frequent spatial surveys are key to understanding food web connections throughout the seasons.

Researchers from GLERL  will expand upon their recent work in Lake Michigan (CSMI 2015) and past work in Huron (2012) to determine fine-scale food-web structure and function from phytoplankton to fishes along a nutrient-rich transect (from inner Saginaw Bay out to the 65-m deep Bay City Basin) and along a nutrient-poor transect (from inner Thunder Bay out to the Thunder Bay basin) during May, July, and September. GLERL will collect additional samples of fish larvae and zooplankton along both transects in June to help estimate larvae growth, diet, density, and mortality and to identify fish recruitment bottlenecks.

“GLERL was instrumental in establishing the long-term monitoring efforts that provide the foundation for current CSMI food-web studies,” said Ashley Elgin, Ph.D., research ecologist in the Ecosystem Dynamics research branch. Elgin serves as the NOAA representative on the CSMI Task Team, part of the Great Lakes Water Quality Act Annex 10, alongside partners from the U.S. Geological Survey (USGS), EPA, the U.S. Fish & Wildlife Service, Environment and Climate Change Canada, and the Ontario Ministries of Natural Resources and the Environment and Climate Change. This year, Elgin is conducting critical mussel growth field experiments in Lake Huron, expanding upon work she developed in Lake Michigan.  She will be addressing the following questions: (1) How does quagga mussel growth differ between regions with different nutrient inputs?; and (2) How do growth rates compare between Lakes Michigan and Huron? Elgin will also coordinate a whole-lake benthic survey, which will update the status of dreissenid mussels and other benthic-dwelling organisms in Lake Huron.  

GLERL’s key research partner, the Cooperative Institute for Great Lakes Research (CIGLR), will deploy a Slocum glider for a total of sixteen weeks to collect autonomous measurements of temperature, chlorophyll, colored dissolved organic matter (CDOM), and photosynthetically active radiation (PAR) between outer Saginaw Bay and open waters of the main basin.  Deployment times and coverage will be coordinated with other glider deployments by the EPA Office of Research and Development (ORD) and/or USGS Great Lakes Science Center, spatial research cruises, and periods of expected higher nutrient loads (i.e., following runoff events).  

CSMI research cruises began in late April and will continue through September. Researchers are using an impressive fleet of research vessels, including EPA’s 180-foot R/V Lake Guardian, GLERL’s 80-foot R/V Laurentian and 50-foot R/V Storm, and two large USGS research vessels, the R/V Articus and R/V Sterling. Sampling missions will also be conducted aboard Environment Canada’s Limnos across Lake Huron. The Laurentian is fitted out with a variety of advanced sensors and sampling gear, making it especially suitable for examining fine-scale spatial structure.

Scientists from the USGS Great Lakes Science Center, the Michigan Department of Natural Resources, and the University of Michigan are also participating in the Lake Huron CSMI.


Leave a comment

“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.


Leave a comment

NOAA research to be highlighted at bi-national Conference on Great Lakes Research

Researchers from NOAA GLERL and the Cooperative Institute for Great Lakes Research (CIGLR), know formerly as CILER, along with other collaborators, will showcase their work on observations, advanced technologies, harmful algal blooms, modeling, forecasting, and more at the 60th Conference on Great Lakes Research on May 15-19th in Detroit, Michigan.

This year’s conference, held by the International Association for Great Lakes Research (IAGLR), is titled “From Cities to Farms: Shaping Great Lakes Ecosystems” and will feature over 660 presentations, plus a poster social with over 160 posters. GLERL and CIGLR authors will showcase their research in more than 50 presentations. In addition, GLERL scientists George Leshkevich, Ashley Baldridge Elgin; Henry Vanderploeg, Philip Chu, Eric Anderson, Brent Lofgren, Jia Wang, Steve Ruberg and Andrew Gronewold; CIGLR’s Dima Beletsky and Tom Johengen; and Great Lakes Sea Grant’s Rochelle Sturtevant will co-chair sessions throughout the week. These sessions include:

Here’s a schedule of where you’ll find our scientists and their research throughout the week (presentation titles linked to online abstract and poster titles linked to .pdf of poster, if available.) And, don’t forget to swing by the NOAA and CIGLR exhibitor tables for more information on our research programs, collaborations, or to grab a copy of one of our recent publications.

For more information you can find .pdf of the full abstract book on the IAGLR conference website.


GLERL and CIGLR posters and presentations during IAGLR 2017

PRESENTATIONS

Tuesday May 16, 2017

8:00A HALL, D.K., NGHIEM, S.V., GUNN, G.E., LESHKEVICH, G., HELFRICH, S.R., CRAWFORD, C.J., KEY, J.R., CZAJKOWSKI, K.P., RIGOR, I.G. and KIM, E.J. GLAWEX’17 – Snow and Ice Field and Aircraft Experiment in Michigan and the Great Lakes
8:40A NGHIEM, S.V., LESHKEVICH, G. and JACKSON, C. Great Lakes Satellite SAR Ice Type Classification and Its Relation to ICECON
9:00A SAYERS, M.J., RUBERG, S.A., LESHKEVICH, G., STUART, D.G., SHUCHMAN, R.A. and ADEN, S.T. Spatial and Temporal Patterns of Inherent Optical Properties in Western Lake Erie for 2015 and 2016
9:40A BOSSE, K.R., SHUCHMAN, R.A., SAYERS, M.J., SCHWAB, D.J. and LESHKEVICH, G. Developing A Daily Composite Product for Water Quality Parameters in the Great Lakes
2:40P LESHKEVICH, G. and LIU, S. Great Lakes CoastWatch – New Data Sets and New Data Servers
9:00A GILL, D.G., JOSHI, S.J., and ROWE, M. Understanding the Potential Utility of the HAB Tracker Forecast Model for Western Lake Erie Anglers
10:00A STOW, C.A., ROWE, M.D., RUBERG, S.A., JOHENGEN, T.H., ZHANG, H., BELETSKY, D., JOSHI, S.J., COLLINGSWORTH, P., MASON, D.M., KRAUS, R.T. and ANDERSON, E.J. Lake Erie Hypoxia Forecasting for Public Water Systems Decision Support
4:20P GLYSHAW, P., VANDERPLOEG, H.A., CAVALETTO, J.F., RUTHERFORD, E.S., WELLS, D.J., NASH, R.D.M. and GEFFEN, A.J. Potential effects of UV radiation on vertical distribution of zooplankton in Southeast Lake Michigan
4:40P CARRICK, H., RUDSTAM, L.G., WARNER, D. and VANDERPLOEG, H.A. Plankton dynamics in Lake Michigan along a near to offshore gradient in Lake Michigan
4:40P QIAN, S.S. and STOW, C.A. A Risk Forecasting Model of Cyanobacterial Toxin for Western Lake Erie

Wednesday, May 17, 2017

10:00A MARINO, J.A., PEACOR, S.D., BUNNELL, D.B., VANDERPLOEG, H.A., POTHOVEN, S.A., ELGIN, A.K., and IONIDES, E.L. Fitting models to field time series data to quantify Bythotrephes effects in Lake Michigan
10:20A ELGIN, A.K., BURLAKOVA, L.E., KARATAYEV, A.Y., MEHLER, K. and NALEPA, T.F. Quagga Mussel Body Condition and Size Distribution Inform Recent Lake Michigan Population Trends
11:20A VANDERPLOEG, H.A., SARNELLE, O., DENEF, V.J., CARRICK, H., ELGIN, A.K., ROWE, M.D., RUTHERFORD, E.S. and POTHOVEN, S.A. Food-web impacts of Dreissena are Context-dependent: Mapping out a New Research Agenda
2:00P STURTEVANT, R.A., BERGERON, D. and BUNTING-HOWARTH, K.E. Stakeholder Engagement in a Wicked World:Crude Oil Transport in the Great Lakes Region
8:20A ROWE, M.D., ANDERSON, E.J., RUBERG, S.A., MOEGLING, S., VERHAMME, E.M., BELETSKY, D., ZHANG, H., JOHENGEN, T.H. and STOW, C.A. Modeling Dissolved Oxygen Dynamics Near Drinking Water Intakes in the Central Basin of Lake Erie
8:40A HAWLEY, N., BELETSKY, D., WANG, J. and CHU, P. Time series measurements of ice thickness in Lake Erie, 2010-2011
10:00A ANDERSON, E.J., LANG, G.A., CHU, P., FUJISAKI-MANOME, A. and WANG, J. Development of the Next-Generation Lake Michigan-Huron Operational Forecast System (LMHOFS)
11:00A HOFFMAN, D.K., MCCARTHY, M.J., DAVIS, T.W., GOSSIAUX, D.C., BURTNER, A.M., JOHENGEN, T.H., PALLADINO, D.A., GARDNER, W.S., MYERS, J.A. and NEWELL, S.E. Water Column Ammonium Dynamics Affecting Harmful Cyanobacterial Blooms in Lake Erie
2:00P ZHANG, H., ROWE, M.D., JOHENGEN, T.H., ANDERSON, E.J. and RUBERG, S.A. Modeling succession of algal functional groups associated with Lake Erie harmful alga blooms
3:40P LIU, Q., ANDERSON, E.J. and BIDDANDA, B.A. A Physical-Biogeochemical Simulation of Muskegon Lake
3:40P LOFGREN, B.M. and XIAO, C. Influence of Greenhouse Gas Concentrations on Lake Phenology and Temperature Profiles
4:00P HU, H., WANG, J., LIU, H. and GOES, J. Simulation of Phytoplankton Distribution and Variation in the Bering-Chukchi Sea using a 3D Physica
4:20P RUTHERFORD, E.S., GEFFEN, A.J., NASH, R.D.M., WELLS, D.J., GLYSHAW, P., VANDERPLOEG, H.A., CAVALETTO, J.F. and MASON, D.M. Have Invasive Species Caused Changes in Larval Fish Density and Distribution in SE Lake Michigan?
4:40P FUJISAKI-MANOME, A., WANG, J. and ANDERSON, E.J. Modeled ice thickness in Lake Erie with different parameterizations of the ice strength
5:00P KESSLER, J.A., WANG, J., MANOME, A.F. and CHU, P. Modeling the Great Lakes with FVCOM+UGCICE

Thursday, May 18, 2017

8:00A WANG, J., KESSLER, J.A., HU, H., FUJISAKI-MANOME, A., CLITES, A., LOFGREN, B.M. and CHU, P. Seasonal forecast of Great Lakes ice cover using multi-variable regression and FVCOM+ice models
8:20A XUE, P., CHU, P., YE, X. and LANG, G.A. Improve Lake Erie Thermal Structure Predictions using Data Assimilative Hydrodynamic Model
8:40A CHU, P., ANDERSON, E.J., LOFGREN, B.M., GRONEWOLD, A.D., WANG, J., STOW, C.A., LANG, G.A., HUNTER, T. and CLITES, A. Towards an Integrated Environmental Modeling System for the Great Lakes
9:00A YE, X., XUE, P., PAL, J.S., LENTERS, J.D. and CHU, P. Coupling a Regional Climate Model with a 3-D Hydrodynamic Model over the Great Lakes
10:00A LUCIER, H.M., HAWLEY, N. and CHU, P. Developing a long-term database of water temperature measurements in the Great Lakes
2:00P PEI, L., HUNTER, T., BOLINGER, R. and GRONEWOLD, A.D. Applying Climate Change Projections in Great Lakes Regional Water Management Decisions
2:40P DAVIS, T.W., ROWE, M.D., ANDERSON, E.J., VANDERWOUDE, A., JOHENGEN, T.H., RUBERG, S.A., STUMPF, R.P. and DOUCETTE, G. Combining advanced technologies to develop an early warning system for HABs in western Lake Erie
3:00P MUZZI, R.W., RUBERG, S.A., BEADLE, K.S., CONSTANT, S.A., DAVIS, T.W., JOHENGEN, T.H., LUCIER, H.M. and VERHAMME, E.M. Observations of the distribution of phytoplankton during cyanobacteria blooms using an AVP
3:00P STURTEVANT, R.A., MARTINEZ, F., RUTHERFORD, E.S., ELGIN, A.K., SMITH, J.P. and ALSIP, P. Update on the Great Lakes Aquatic Nonindigenous Species Information System (GLANSIS)
3:40P VANDER WOUDE, A.J., MILLER, R.J., JOHENGEN, T.H. and RUBERG, S.A. Variability in Lake Erie by Integrating Hyperspectral Imagery, AUV’s and a Shipboard Underway System
4:00P STUART, D.G., BURTNER, A.M., JOHENGEN, T.H., MILLER, R.J., PALLADINO, D.A. and RUBERG, S.A. Trends In Nitrate, Phosphate And Bloom Indicators During The 2016 Western Lake Erie Field Season
4:20P RUBERG, S.A., CONSTANT, S.A., MUZZI, R.W., MILLER, R.J. and SMITH, J.P. Utilization of PostgreSQL Database for Real-Time Western Lake Erie Data Storage and Dissemination
4:40P JOHENGEN, T.H., PAIGE, K., RUBERG, S.A., TWISS, M.R. and PEARSON, R. State of the Science for Great Lakes Observations: Conclusions from the 2016 CILER Symposium
4:40P MASON, L., SAMPSON, K., DUGGER, A., GOCHIS, D., RISENG, C.M. and GRONEWOLD, A.D. Development of a new geospatial hydrofabric to support advanced hydrological modeling
5:00P LEE, D.H. The application of hydroclimate science to Lake Ontario-St. Lawrence River System regulation

Friday, May 19, 2017

8:00A FRY, L.M., GRONEWOLD, A.D., BOLINGER, R. and MUELLER, R. Assessment of Probabilistic 5-year Forecasts of Great Lakes Levels and Outflows for Hydropower
10:00A GRONEWOLD, A.D. and SMITH, J.P. Great Lakes water budget modelling and uncertainty estimation under a Bayesian MCMC framework
10:40A QUINN, F.H., CLITES, A. and GRONEWOLD, A.D. Reconciling Discontinuity of Temporal Flow Measurements for the Detroit River
11:20A LABUHN, K.A., CALAPPI, T.J., GRONEWOLD, A.D., ANDERSON, E.J. and KOWALSKI, P.J. Optimizing Water Levels in the Grass Island Pool for Hydropower Production on the Niagara River

POSTERS 

Wednesday, May 17, 2017

Using the Fluoromarker Calcein to Assess Growth Rates of Quagga Mussels in situ.

MABREY, K. (1), GLYSHAW, P. (1) and ELGIN, A.K. (2) – (1) CILER University of Michigan, G110 Dana 440 Church St, Ann Arbor, MI, 48109, USA; (2) NOAA Great Lakes Environmental Research Laboratory, 1431 Beach St., Muskegon, MI, 49441, USA.

The quagga mussel (Dreissena r. bugensis) exerts a profound effect on the Lake Michigan food web. Understanding quagga mussel growth habits in situ will help us to better predict population growth and anticipate ecosystem effects. One technique used to mark mollusk species for growth experiments is exposure to the fluoromarker calcein. Studies on calcein are more common in marine conditions; less has been reported for freshwater species, let alone in natural environments. We conducted a field experiment at 45m in Lake Michigan to assess if calcein is an effective and noninvasive marking technique for quagga mussels. Mussels from two size groups were assigned to calcein or no calcein treatments, measured, then placed in replicate mesh cages attached to a tripod mooring platform 0.4m above the lakebed. We removed cages to remeasure mussels after 5 and 12 months. We captured fluorescent images using a dark box with a blue filter on the light source and a yellow filter on the camera. New shell growth beyond the calcein mark was visually delineated and measured using ImagePro Premier(v9.1) software. Preliminary results indicate that small and large quagga mussels respond differently to exposure to calcein. Studies using calcein will need to take these artifacts into account when measuring dreissenid mussel growth rates

Shock and Awe! Estimating Mysis Density and Catch Avoidance using the MOCNESS in SE Lake Michigan

WELLS, D.J.(1), PSAROUTHAKIS, Z.(1), RUTHERFORD, E.S.(2), CHIN, T.(1), VANDERPLOEG, H.A.(2), CAVALETTO, J.F.(2) and GLYSHAW, P.1(1) – (1) Cooperative Institute for Limnology and Ecosystems Research, 440 Church Street, Ann Arbor, MI, 48109, USA; (2) NOAA GLERL, 4840 S. State Rd., Ann Arbor, MI, 48108, USA

Mysis diluviana is a key member of Great Lakes aquatic food webs and is important prey for pelagic planktivores. Mysis are visual predators of zooplankton, and migrate diurnally from the lake bottom into the water column. Mysis biomass estimates are highly variable but critical for food web models that inform salmonid stocking decisions. In 2016, we evaluated catch efficiency of Mysis in a Multiple Opening/Closing Net and Environmental Sensing System (MOCNESS) with LED strobe lighting that is used to sample plankton and fish larvae in marine waters. We sampled Mysis density at a mid-depth (45 m) station in June and July, and at an offshore (110m) station in September off Muskegon, MI. We made replicate tows in thermally stratified depth layers during day and night, and compared Mysis densities sampled with the LED strobe on vs strobe off. There were no significant differences in Mysis density among depths in samples with strobe on or off in any month. Mysis were concentrated in dense layers in the metalimnion at night, and were highest in the hypolimnion during day. We conclude that the strobe flash light had no effect on catch avoidance by Mysis.

Forecasting Lake Levels Under Climate Change: Implications of Bias Correction.

CHANNELL, K.E.(1), GRONEWOLD, A.D.(2), XIAO, C.(3), ROOD, R.B.(1), LOFGREN, B.M.(2) and HUNTER, T.(2) – (1) University of Michigan Climate and Space Sciences and Engineering, Ann Arbor, MI, USA; (2) NOAA Great Lakes Environmental Research Laboratory, Ann Arbor, MI, USA; (3) Cooperative Institute for Limnology and Ecosystems Research, Ann Arbor, MI, USA.

Numerical models can provide a basis for projecting future water levels for the Great Lakes under climate change. Hydrological components critical to generating water supplies were extracted from the WRF/GFDL-CM3 downscaled climate model, and were then used to drive a routing model to produce water levels for the 21st century. A new method of bias correction was used to provide a more consistent representation of seasonality, trends, and variability, when compared to more conventional methods. Here we demonstrate the relative differences in hydrology projections from our method and previously used methods. Our results indicate that the bias correction method used is an important source of variability in water level projections. This is a source of variability that is perhaps just as important as choice of models, emission scenarios, etc., but is commonly overlooked.

Simulating and forecasting seasonal ice cover

JI, X.(1), ROOD, R.B.(1), DAHER, H.(2), GRONEWOLD, A.D.(2) and BOLINGER, R.(2)
(1) Climate and Space Science and Engineering, University of Michigan, 2455 Hayward St, Ann Arbor, MI, 48109, USA; (2) NOAA Great Lakes Environmental Research Laboratory, 4840 S State Rd, Ann Arbor, MI, 48108, USA.

Over the past several decades, dramatic changes in the spatial extent of seasonal and long-term ice cover have been documented for both marine and continential water bodies. Successfully projecting future changes in global ice cover requires an understanding of the drivers behing these historical changes. Here, we explore relationships between continental climate patterns and regional ice cover across the vast surface waters of the Great Lakes. Our findings indicate that abrupt historical changes in Great Lakes seasonal ice cover are coincident with historical changes in teleconnections, including both the El Nino Southern Oscillation (ENSO) and Pacific Decadal Oscillation (PDO). We find, in particular, that these teleconnections explain much of the ice cover decline in the late 1990s (coincident with the strong 1997-1998 winter El Nino) and the following persistent period of below-average period of ice that followed. We encode these relationships in a probabilistic model that provides seasonal projections of ice cover areal extent across the Great Lakes, as well as specific spatiotemporal patterns in ice cover at resolutions that align with critical regional human health and safety-related management decisions.

Implementation of the WRF-Hydro Model in the Great Lakes Region

XIAO, C.(1), LOFGREN, B.M.(2), GRONEWOLD, A.D.(2), GOCHIS, D.(3), MASON, L.(1) and PEI, L.(3) – (1) Cooperative Institute for Limnology and Ecosystems Research (CILER), University of Michigan, 4840 S. State Rd., Ann Arbor, MI, 48108, USA; (2) NOAA Great Lakes Environmental Research Laboratory, 4840 S. State Rd., Ann Arbor, MI, 48108, USA; (3) National Center for Atmospheric Research (NCAR), P.O. Box 3000, Boulder, CO, 80307, USA.

As a physics-based, spatially-distributed hydrologic modeling system, the community Weather Research and Forecasting model (WRF) hydrological extension package (WRF-Hydro) has been used in several streamflow prediction applications in the U.S. and around the world, including the National Water Model (NWM) at the newly established NOAA National Water Center. However, because of lack of consistency of the geofabric data along the U.S. and Canada borders, the Great Lakes basin is not entirely included in NWM, leaving a substantial gap for applying the national model to the water-dominated region. Thus, a specific effort has been devoted to implementing the WRF-Hydro modeling system in the Great Lakes basin, including preparing high-resolution terrain datasets, parameterizing lakes and reservoirs, and calibrating the model. Two experiments have been carried out to support implementation of the NWM in the Great Lakes basin: an offline WRF-Hydro simulation forced by NLDAS2 and a coupled WRF/WRF-Hydro simulation. The model results are validated against observations in terms of precipitation, runoff, soil moisture, channel flow, and land surface heat fluxes. Our preliminary study presented here shows that the WRF-Hydro model is capable of reproducing the land-hydro-air feedbacks in the Great Lakes region.

Investigation into Recent Meteotsunami Events in the Great Lakes.

ANDERSON, E.J.(1), BECHLE, A.J.(2), WU, C.H.(2), CHU, P.(1), MANN, G.E.(3), SCHWAB, D.J.(4) and LOMBARDY, K.(5) –  (1) NOAA GLERL, 4840 S. State Rd, Ann Arbor, MI, 48104, USA; (2)University of Wisconsin-Madison, Madison, WI, USA; (3) NWS WFO-Detroit, Detroit, MI, USA; (4) University of Michigan, Water Center, Ann Arbor, MI, USA; (5) NWS WFO-Cleveland, Cleveland, OH, USA.

Meteotsunami events have been documented in several countries around the world in the coastal ocean, semi-enclosed basins, and in the Great Lakes. In particular, investigations in the Great Lakes have raised the issue of dangers posed by enclosed basins due to the reflection and interaction of meteotsunami waves, in which the destructive waves can arrive several hours after the atmospheric disturbance has passed. This disassociation in time and space between the atmospheric disturbance and resultant meteotsunami wave can pose a significant threat to the public. In recent events in the Great Lakes, atmospheric conditions have induced meteotsunami waves in Lake Erie and Lake Superior. The resulting waves impacted swimmers, inundated a marina, flooded coastal communities. In this work, we attempt to explain the processes that led to these conditions through a combination of atmospheric and hydrodynamic modeling and an analysis of the observed meteorology. Results from a high-resolution atmospheric model and hydrodynamic model reveal the formation of destructive waves resulted from a combination of wave reflection, focusing, and edge waves, though important differences have been found between recent events in the Great Lakes.

Reconstructing evaporation over Lake Erie during the historic November 2014 lake effect snow event

FITZPATRICK, L.E.(1), FUJISAKI-MANOME, A.(1), GRONEWOLD, A.D.(2), ANDERSON, E.J.(2), SPENCE, C.(5), CHEN, J.(4), SHAO, C.(4), POSSELT, D.(3), WRIGHT, D.(3), LOFGREN, B.M.(2) and SCHWAB, D.J.(3),- (1)Cooperative Institute for Limnology and Environmental Research, Ann Arbor, MI, USA; (2)Great Lakes Environmental Research Laboratory, Ann Arbor, MI, USA; (3)University of Michigan, Ann Arbor, MI, USA; (4)Michigan State University, East Lansing, MI, USA; (5)Environment Climate Change Canada, Gatineau, QC, CANADA.

The extreme North American winter storm of November 2014 triggered a record lake effect snowfall (LES) event in southwest New York. This study examined the evaporation from Lake Erie during the record LES event between November 17th-20th, 2014, by reconstructing heat fluxes and evaporation rates over Lake Erie using the unstructured grid, Finite-Volume Community Ocean Model (FVCOM). Nine different model runs were conducted using combinations of three different flux algorithms and three different meteorological forcings. A few non-FVCOM model outputs were also included for further evaporation analysis. Model-simulated water temperature and meteorological forcing data were validated with buoy data at three locations in Lake Erie. The simulated sensible and latent heat fluxes were validated with the eddy covariance measurements at two offshore sites. The evaluation showed a significant increase in heat fluxes over three days, with the peak on the 18th of November. Snow water equivalent data from NOAA’s NOHRSC showed a spike in water content on the November 20th. The ensemble runs presented a variation in spatial pattern of evaporation, lake-wide average evaporation, and resulting cooling of the lake, however, the overall analysis showed significant evaporation from Lake Erie appeared to be the main contribution to the LES event.

MCMC modelling with JAGS and applications in the Great Lakes

SMITH, J.P.(1), MASON, L.(2), QIAN, S.S.(3) and GRONEWOLD, A.D.(4) – (1)CILER, G110 Dana Building 440 Church Street, Ann Arbor, MI, 48109-1041, USA; (2)University of Michigan, School of Natural Resources, 440 Church St., Ann Arbor, MI, 48109-1041, USA; (3)The University of Toledo, Department of Environmental Sciences, 2801 West Bancroft St., Toledo, OH, 43606-3390, USA; 4NOAA Great Lakes Environmental Research Laboratory, 4840 S. State Rd., Ann Arbor, MI, 48108-9719, USA.

Markov Chain Monte Carlo (MCMC) methods have made realistic modeling possible and are widely used in areas such as genetics, ecology, biostatistics, economics. Software packages, such as Just Another Gibbs Sampler (JAGS), have made MCMC accessible to many scientists, engineers, and other professionals looking to utilize the method. This high-level talk discusses the theory, the JAGS package, and a few applications including analyzing the effects of climate change on Great Lakes ice cover and water budget modelling.

Effect of light exposure and nutrients on buoyancy of Microcystis colonies.

MING, T.(1), VANDERPLOEG, H.A.(2), ROWE, M.D.(3), FANSLOW, D.L.(2), STRICKLER, J.R.(4), MILLER, R.J.(3), JOHENGEN, T.H.(3), DAVIS, T.W.(2) and GOSSIAUX, D.C.(2), – (1)School of Natural Resources & Environment, University of Michigan, 440 Church St., Ann Arbor, MI, 48109, USA; (2)NOAA Great Lakes Environmental Research Laboratory, 4840 S. State Rd., Ann Arbor, MI, 48108, USA; (3)Cooperative Institute for Limnology and Ecosystems Research, 440 Church St., Ann Arbor, MI, 48109, USA; (4)Great Lakes WATER Institute, University of Wisconsin – Milwaukee, 600 E. Greenfield Ave., Milwaukee, WI, 53204, USA.

Understanding the vertical distribution of Microcystis spp. is important for improving satellite-derived estimates of bloom biomass and for predicting the transport of blooms. For example, the Lake Erie HAB Tracker forecast model is initialized from satellite imagery, then predicts the transport and vertical distribution of harmful algal blooms (HABs) in Lake Erie over a five-day period. To improve vertical distribution predictions, we used novel videographic methods to determine effects of light intensity, colony size, as well as dissolved and particulate nutrient concentrations on the buoyant velocities of Microcystis colonies collected from western Lake Erie. We incubated whole water samples in two 2L borosilicate bottles in an outdoor incubator maintained at ambient lake temperatures. Light levels were varied to represent day and night conditions for a surface scum or turbulent mixed layer distributions. After an overnight dark adaption, subsamples from each bottle were collected in the morning and evening, then buoyant velocities were measured. In general, colonies were positively buoyant with rates increasing with colony size. However, varying nutrient and light conditions differentially impacted buoyancy rates of Microcystis colonies.

Skill Assessment of the Lake Erie HAB Tracker Forecast Model using Variable Spatial Neighborhoods.

OUYANG, W.(1), ROWE, M.D.(2) and ZHANG, H.(2), – (1)School of Natural Resources and Environment, University of Michigan, Dana Building 440 Church Street, Ann Arbor, MI, 48108, USA; (2) Cooperative Institute for Limnology and Ecosystems Research, University of Michigan, G110 Dana Building 440 Church Street, Ann Arbor, MI, 48108, USA.

Forecasts of harmful algal bloom (HAB) spatial distribution are useful to public water systems, anglers, and recreational boaters. The Lake Erie HAB Tracker model is initialized from satellite-derived HAB spatial distribution, then uses a hydrodynamic forecast to predict the transport and vertical distribution of HABs in Lake Erie over a 5-day period. The model was assessed previously using pixel-by-pixel skill statistics; however, such statistics produce large penalties for small spatial mismatch between simulated and observed fields. Here, we used an alternative approach, fractions skill score (FSS), which has been used in precipitation forecast skill assessment. FSS assesses model skills over a series of increasing spatial neighborhood sizes. Model skill may improve with increasing neighborhood size if spatial mismatch between simulated and observed fields is a problem. We calculated FSS for a series of 26 hindcast simulations from 2011. We compared model skill to a benchmark persistence forecast, which assumed no change from the initial satellite image. Model skill exceeded that of the persistence forecast initially, but the advantage decreased at day 7. Model skill was greatest at 1 km neighborhood size for days 1-2, but improved at neighborhood size of 3-5 km for days 3-6, a period when the forecast is more challenging.

Hydrodynamics of Western Lake Erie.

BELETSKY, D.(1), ANDERSON, E.J.(2) and BELETSKY, R.(1) – (1)University of Michigan, Ann Arbor, MI, USA; (2) NOAA GLERL, Ann Arbor, USA

Prediction of harmful algal blooms in Lake Erie depends on the accuracy of hydrodynamic models that provide information on lake circulation and temperature. Until recently, long-term observations of circulation in the western basin were practically non-existent, but in summer 2015 GLERL deployed 4 ADCPs that revealed highly variable circulation patterns. To evaluate model skill and better understand the dynamics of Lake Erie’s western basin, we compare FVCOM model results with current and temperature observations conducted in Lake Erie.

The Great Lakes Aquatic Nonindigenous Species Information System Watchlist.

ALSIP, P.(1), RICE, N.M.(2), IOTT, S.(1), STURTEVANT, R.A.(3), MARTINEZ, F.(4) and RUTHERFORD, E.S.(2), – (1)Cooperative Institute for Limnology and Ecosystems Research, 4840 South State Road, Ann Arbor, MI, 48108, USA; (2)NOAA Great Lakes Environmental Research Laboratory, 4840 South State Road, Ann Arbor, MI, 48108, USA; (3)Great Lakes Sea Grant Network, 4840 South State Road, Ann Arbor, MI, 48108, USA; (4)NOAA NCCOS, 4840 South State Road, Ann Arbor, MI, 48108, USA.

The Laurentian Great Lakes is one of the most heavily invaded aquatic systems in the world with over 180 documented aquatic nonindigenous species, the peak invasion rate was estimated to be 2.8 species introduced per year (1990-1995). While invasion rates have slowed in recent years, prevention remains the best defense. The Great Lakes Aquatic Nonindigenous Species Information System (GLANSIS) currently serves information for 67 species which have been identified through the peer-reviewed scientific literature as having some likelihood of invading the Great Lakes. This poster was developed primarily as an outreach tool to help scientists and citizens know what to look for in monitoring for the presence of these species. Monitoring is essential both to determine the success of prevention programs and to support early detection – early enough to make true rapid response and eradication feasible. The poster links back to the GLANSIS database, which includes further information about the potential for introduction, establishment, and impact of these species as well as more detailed information on how to identify the species and information on management options in the event they are detected.