NOAA Great Lakes Environmental Research Laboratory

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


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Sounds of the storm and coral reef recovery following Hurricanes Irma and Maria in Puerto Rico

By Dr. Doran Mason (NOAA Great Lakes Environmental Research Laboratory) and Felix Martinez (National Centers for Coastal Ocean Science)

2017-10-23-PHOTO-50

University of Puerto Rico grad students servicing a hydrophone at the Weinberg site at La Parguera Natural Reserve on the southwest coast of Puerto Rico.  Photo Credit:  Rebecca Becicka, Ph.D. student at University of Puerto Rico, Mayagüez

Researchers at NOAA’s Great Lakes Environmental Research Laboratory (GLERL) are exploring the use of sound to monitor and assess the health of coastal ecosystems, most recently focusing on the soundscape created by Hurricanes Irma and Maria in Puerto Rico. In collaboration with the University of Puerto Rico at Mayagüez, Purdue University (a partner university in the Cooperative Institute for Great Lakes Research consortium), and the National Centers for Coastal Science (NCCOS), GLERL has launched a pilot study on developing the long-term use of soundscape. To implement this new approach to monitoring, hydrophones, an instrument in measuring sound, are used to track the response of ecosystems to natural (e.g., tropical storms) and human-induced (e.g., stressors such as excess nutrients, sedimentation, fishing pressure, climate change) disturbances.

In this pilot project, hydrophones have been in place for six months at three sites (see below for Google Earth Map of Magueyes Island, La Parguera, Puerto Rico) at La Parguera Natural Reserve on the southwest coast of Puerto Rico prior to and during the two category 4 hurricanes that pummeled the island. Miraculously, the recorders and data survived the storms and were recovered, providing us with a unique opportunity to listen to the hurricanes and to evaluate how quickly reefs recover from a natural disaster.  

What is a soundscape?  Soundscapes are created by the aggregation of sounds produced by living organisms (invertebrates, fish, marine mammals), non-biological natural sounds (waves, rain, movement of the earth), and sounds produced by humans (boats, coastal roads). Changes in the biological portion of soundscape can provide us with the quantitative data to assess the health of the ecosystem in response to natural and human-induced disturbance.  Thus, our overall goal is to develop quantitative indices of coastal ecosystem health, based on the soundscape to assess the state of the environment, and to understand and predict changes, with application towards ecosystem restoration and conservation efforts. The utility of this approach is the use of a low-cost, remote autonomous technology that holds potential in expanding NOAA’s long-term observational capacity to monitor and assess coastal habitats.

Why GLERL?  As part of a long history of monitoring and research in the Great Lakes, GLERL scientists have cultivated a unique expertise in the development of autonomous remote sensing technology. In the last two decades, Purdue University (a CIGLR partner) has been one of the leaders in the development of terrestrial soundscapes as a critical tool to monitor ecosystem change. More recently, interest has grown in expanding this approach into the aquatic realm.  Building on our relationship with Purdue, GLERL and partners are well positioned to advance use of soundscape ecology to meet NOAA’s mission to protect, restore, and manage the use of coastal and ocean resources. In addition to the pilot study, GLERL is partnering with NCCOS to reach out to other NOAA Line Office programs in efforts to formalize the use of soundscapes within NOAA as a scientific program.  For example, efforts are underway to plan an international workshop to establish the foundational principles and identify research and technology gaps for the use of soundscape ecology.

Why Puerto Rico? Original support for this pilot study came from a congressional allocation for enhancing relationships with the cooperative institutes for the benefit of coral reef restoration and conservation. Given the scientific knowledge accrued from NCCOS’ prior investments in La Parguera, GLERL and its NCCOS partner recognized that Puerto Rico would be a prime location to test and develop the use of soundscapes technology to track and quantify the health of coastal ecosystems.

Google Earth Map-MagueyesIsland-PR

Google Earth Map of Magueyes Island, La Parguera, Puerto Rico showing coral reef locations where the hydrophones were deployed at different depths: Weinberg (shelf-edge) – 75′; Media Luna (mid-shelf) – 45′; Pelotas (inner-shelf) – 35′.  Provided by: Prof. Richard Appeldoorn, University of Puerto Rico, Mayagüez

IMG_3548

Colleagues from Purdue University and University of Puerto Rico deploy Media Luna reef site hydrophone for the first time.  Photo credit: Steve Ruberg, NOAA GLERL

IMG_3539

View of La Parguera from Media Luna reef site. Photo credit: Steve Ruberg, NOAA GLERL


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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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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.


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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.


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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.

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: