An analysis of the state of water quality and ecosystem services in the rivers (St. Marys, St. Clair, Detroit, Niagara, St. Lawrence), fluvial lake (St. Clair), and strait (Mackinac) that connect and drain the Laurentian Great Lakes was conducted by the Great Lakes Science Advisory Board of the International Joint Commission (Canada, United States of America). Although these boundary waters are defined under the Great Lakes Water Quality Agreement and are associated with lakewide management plans, they have historically received inadequate attention regarding surveillance and monitoring. As a result, the data and knowledge bases for connecting waters are far less complete than for the open water and nearshore regions of the lakes, given the intensity of threats and the ecosystem services that intersect in connecting waters. This commentary reviews and discusses the current status of monitoring infrastructure and activities in the Great Lakes connecting waters. Several specific recommendations are made to support development of integrated connecting water research and practice: the development of highly qualified personnel trained to conduct research on large moving waters; establishment of well-equipped and staffed research vessels with appropriate sampling gear; support for shore-based university and agency laboratories to focus on a mix of long-term reference stations and experimental studies; expanded use of real-time monitoring systems using evolving technologies; and increase in Indigenous community technical capacity for environmental monitoring and management to collectively improve ecological and human health knowledge and management in a more coordinated fashion.

The Great Lakes and connecting waters encompass a vast and diverse ecosystem that presents scale challenges for management similar to those of the coastal ocean. Technological approaches to overcome the scale challenges have primarily been adapted from oceanographic applications and technologies, and from upscaling inland lake methods designed for shallower and calmer water bodies. Many standard methods for studying Great Lakes habitat and biota have long lag times between field collection and data availability. Many also miss much of the dynamics, three-dimensional complexity, and spatial variability needed to manage the system effectively. Even baseline conditions are not well characterized for many parts of the Great Lakes ecosystem (e.g. bathymetry and critical habitat, life cycles and food webs, night and winter movement and activity of organisms). Emerging technologies are beginning to address these needs but require coordination, consistent investment, training, and governance linkages. Here we survey recent technological advances and show how they are contributing to improved adaptive management of the Great Lakes ecosystem by reducing uncertainty and increasing understanding of physical, biological, and chemical processes, and the human dimensions of resource management and restoration.

Microcystin-producing cyanobacterial blooms are a global issue threatening drinking water supplies and recreation on lakes and beaches. Direct measurement of microcystins is the only way to ensure waters have concentrations below guideline concentrations; however, analyzing water for microcystins takes several hours to days to obtain data. We tested LightDeck Diagnostics' bead beater cell lysis and two versions of the quantification system designed to give microcystin concentrations within 20 min and compared it to the standard freeze-thaw cycle lysis method and ELISA quantification. The bead beater lyser was only 30 % effective at extracting microcystins compared to freeze-thaw. When considering freeze-thaw samples analyzed in 2021, there was good agreement between ELISA and LightDeck version 2 (n = 152; R2 = 0.868), but the LightDeck slightly underestimated microcystins (slope of 0.862). However, we found poor relationships between LightDeck version 2 and ELISA in 2022 (n = 49, slopes 0.60 to 1.6; R2 < 0.6) and LightDeck version 1 (slope = 1.77 but also a high number of less than quantifiable concentrations). After the quantification issues are resolved, combining the LightDeck system with an already-proven rapid lysis method (such as microwaving) will allow beach managers and water treatment operators to make quicker, well-informed decisions.

Cyanobacteria biomass models are routinely used in Lake Erie to predict the occurrence and location of algal blooms. However, current forecasts do not predict the microcystin toxins produced by these blooms. In this study, we used an extensive dataset of microcystin concentrations to generate weekly distribution maps in Lake Erie for the summers of 2018 and 2019. Using a 3D Eulerian tracer model (ETM) initialized with these maps, we simulated microcystin transport over 7 days, under two conditions: (1) the initial microcystin is mixed within the surface-mixed layer; (2) the initial microcystin is distributed throughout the entire water column. Two scenarios were tested for each condition: one incorporating microcystin production rates into hydrodynamic transport and one excluding them. Model performance was evaluated against weekly sample data in predicting whether microcystin concentrations surpassed specific thresholds (0.3, 1.0, 5.0, 10.0, and 20.0 lg/L), and in predicting trend directionality over each week. Overall, the ETM with hydrodynamics alone captured the transport of microcystins and predicted microcystin concentrations in 69% of the simulations. Incorporating microcystin production into the model increased the accuracy of forecasts by an additional 10%. Moreover, models with microcystin production successfully predicted microcystin concentrations greater than 5 lg/L during a large bloom, highmicrocystin year (2019), while incorrectly forecasting concentrations above 5 lg/L during a small bloom year (2018). With limited data to initialize the ETM, no single model configuration consistently outperformed others. It is necessary to consider the full range of model configurations when utilizing their outputs for making management decisions. (c) 2023 International Association for Great Lakes Research. Published by Elsevier B.V. All rights reserved.

Groundwater historically has been a critical but understudied, underfunded, and underappreciated natural resource, although recent challenges associated with both groundwater quantity and quality have raised its profile. This is particularly true in the Laurentian Great Lakes (LGL) region, where the rich abundance of surface water results in the perception of an unlimited water supply but limited attention on groundwater resources. As a consequence, groundwater management recommendations in the LGL have been severely constrained by our lack of information. To address this information gap, a virtual summit was held in June 2021 that included invited participants from local, state, and federal government entities, universities, non-governmental organizations, and private firms in the region. Both technical (e.g., hydrologists, geologists, ecologists) and policy experts were included, and participants were assigned to an agricultural, urban, or coastal wetland breakout group in advance, based on their expertise. The overall goals of this groundwater summit were fourfold: (1) inventory the key (grand) challenges facing groundwater in Michigan; (2) identify the knowledge gaps and scientific needs, as well as policy recommendations, associated with these challenges; (3) construct a set of conceptual models that elucidate these challenges; and (4) develop a list of (tractable) next steps that can be taken to address these challenges. Absent this type of information, the sustainability of this critical resource is imperiled.

Monitoring of cyanobacterial bloom biomass in large lakes at high resolution is made possible by remote sensing. However, monitoring cyanobacterial toxins is only feasible with grab samples, which, with only sporadic sampling, results in uncertainties in the spatial distribution of toxins. To address this issue, we conducted two intensive "HABs Grabs" of microcystin (MC)-producing Microcystis blooms in the western basin of Lake Erie. These were one-day sampling events during August of 2018 and 2019 in which 100 and 172 grab samples were collected, respectively, within a six-hour window covering up to 2,270 km2 and analyzed using consistent methods to estimate the total mass of MC. The samples were analyzed for 57 parameters, including toxins, nutrients, chlorophyll, and genomics. There were an estimated 11,513 kg and 30,691 kg of MCs in the western basin during the 2018 and 2019 HABs Grabs, respectively. The bloom boundary poses substantial issues for spatial assessments because MC concentration varied by nearly two orders of magnitude over very short distances. The MC to chlorophyll ratio (MC:chl) varied by a factor up to 5.3 throughout the basin, which creates challenges for using MC:chl to predict MC concentrations. Many of the biomass metrics strongly correlated (r > 0.70) with each other except chlorophyll fluorescence and phycocyanin concentration. While MC and chlorophyll correlated well with total phosphorus and nitrogen concentrations, MC:chl correlated with dissolved inorganic nitrogen. More frequent MC data collection can overcome these issues, and models need to account for the MC:chl spatial heterogeneity when forecasting MCs.

In this study, decadal variability of ice cover in the Great Lakes is investigated using historical airborne and satellite measurements from 1963 to 2017. It was found that Great Lakes ice cover has 1) a linear relationship with the Atlantic multidecadal oscillation (AMO), similar to the relationship of lake ice cover with the North Atlantic Oscillation (NAO), but with stronger impact than NAO; 2) a quadratic relationship with the Pacific decadal oscillation (PDO), which is similar to the relationship of lake ice cover to Nino-3.4, but with opposite curvature; and 3) decadal variability with a positive (warming) trend in AMO contributes to the decreasing trend in lake ice cover. Composite analyses show that during the positive (negative) phase of AMO, the Great Lakes experience a warm (cold) anomaly in surface air temperature (SAT) and lake surface temperature (LST), leading to less (more) ice cover. During the positive (negative) phase of PDO, the Great Lakes experience a cold (warm) anomaly in SAT and LST, leading to more (less) ice cover. Based on these statistical relationships, the original multiple variable regression model established using the indices of NAO and Nino-3.4 only was improved by adding both AMO and PDO, as well as their interference (interacting or competing) mechanism. With the AMO and PDO added, the correlation between the model and observation increases to 0.69, compared to 0.48 using NAO and Nino-3.4 only. When November lake surface temperature was further added to the regression model, the prediction skill of the coming winter ice cover increased even more.

Green Bay has sometimes been referred to as the largest freshwater "estuary" in the world. Its watershed, much of it in intensive agriculture, comprises one-third of the Lake Michigan basin and delivers one-third of the lake's total phosphorus load. At one time, the major tributary, the Fox River, was considered the most heavily industrialized river in North America, primarily from paper manufacturing. Deterioration in water quality and the loss of beneficial and ecological uses have been extensive and began well back into the last century. More recently, the bay has also become a test case for our resolve to remediate and restore ecosystems throughout the Great lakes and elsewhere. Green Bay has stimulated a significant amount of widely relevant research on the fate and behavior of toxics, biogeochemistry, habitat, biodiversity, and ecological processes. The bay represents a true "proving ground" for adaptive restoration. Key findings of the recent summit on the Ecological and Socio-Economic Tradeoffs of Restoration in the Green Bay Ecosystem are summarized here. Foremost among recommendations of the workshop was the creation of a "Green Bay Ecosystem Simulation and Data Consortium" serving as a data clearing house, building upon the significant progress to date, and developing a modeling framework and visualization tools, furthering public outreach efforts, and ensuring a sustained growth in scientific expertise. Funding was estimated to be on the order of similar to$15-20M over the next similar to 5 years - a modest investment relative to the value of the ecosystem and the long-term cost of inaction. (C) 2018 International Association for Great Lakes Research. Published by Elsevier B.V. All rights reserved.

We investigated biogeochemical conditions and watershed features controlling the extent of nitrate removal through microbial dinitrogen (N2) production within the surficial glacial aquifer located on the north and south shores of Long Island, NY, USA. The extent of N2 production differs within portions of the aquifer, with greatest N2 production observed at the south shore of Long Island where the vadose zone is thinnest, while limited N2 production occurred under the thick vadose zones on the north shore. In areas with a shallow water table and thin vadose zone, low oxygen concentrations and sufficient DOC concentrations are conducive to N2 production. Results support the hypothesis that in aquifers without a significant supply of sediment-bound reducing potential, vadose zone thickness exerts an important control of the extent of N2 production. Since quantification of excess N2 relies on knowledge of equilibrium N2 concentration at recharge, calculated based on temperature at recharge, we further identify several features, such as land use and cover, seasonality of recharge, and climate change that should be considered to refine estimation of recharge temperature, its deviation from mean annual air temperature, and resulting deviation from expected equilibrium gas concentrations.

Since the mid-1990s, Lake Erie has experienced re-eutrophication symptoms including harmful algal blooms in the western basin and summer hypoxia in the Central Basin. The 2012 Protocol for the Great Lakes Water Quality Agreement (GLWQA) required phosphorus objectives and management recommendations to be set for all the Great Lakes, beginning with Lake Erie. To inform setting revised loading targets for the Lake Erie portion of the GLWQA, modeling was performed. The development and application of one of those models, the Western Lake Erie Ecosystem Model (WLEEM), is described here. WLEEM is a three dimensional, fine-scale, process-based model that links hydrodynamic, sediment transport, and in-lake biogeochemical and ecological processes. WLEEM was applied here to assess system sensitivity to a range of variables, and ultimately to develop a robust phosphorus load — cyanobacteria response relationship to determine a maximum load of total phosphorus from the Maumee River during the period of March–July that would produce a mild cyanobacteria bloom (<7830MT cyanobacteria biomass) in Western Lake Erie. The maximum total phosphorus load from the Maumee River for that period to produce a mild bloom was determined to be 890metrictons. Given the natural variability of systems like this, tools like WLEEM used in a dynamic operational modeling mode, consistent tributary and lake monitoring, and ongoing research will be essential components of effective mitigation and science-based adaptive management of eutrophication in Lake Erie and other nutrient-impacted water bodies.

Since the late 1990s, the Laurentian Great Lakes have experienced persistent low water levels and above average over-lake evaporation rates. During the winter of 2013-2014, the lakes endured the most persistent, lowest temperatures and highest ice cover in recent history, fostering speculation that over-lake evaporation rates might decrease and that water levels might rise. To address this speculation, we examined interseasonal relationships in Lake Michigan's thermal regime. We find pronounced relationships between winter conditions and subsequent fall heat content, modest relationships with fall surface temperature, but essentially no correlation with fall evaporation rates. Our findings suggest that the extreme winter conditions of 2013-2014 may have induced a shift in Lake Michigan's thermal regime and that this shift coincides with a recent (and ongoing) rise in Great Lakes water levels. If the shift persists, it could (assuming precipitation rates remain relatively constant) represent a return to thermal and hydrologic conditions not observed on Lake Michigan in over 15years.

A record-breaking low ice cover occurred in the North American Great Lakes during winter 2011/2012, in conjunction with a strong positive Arctic Oscillation/North Atlantic Oscillation (+AO/NAO) and a La Niña event. Large-scale atmosphere circulation in the Pacific/North America (PNA) region reflected a combined signal of La Niña and +NAO. Surface heat flux analysis shows that sensible heat flux contributed most to the net surface heat flux anomaly. Surface air temperature is the dominant factor governing the interannual variability of Great Lakes ice cover. Neither La Niña nor +NAO alone can be responsible for the extreme warmth; the typical mid-latitude response to La Niña events is a negative PNA pattern, which does not have a significant impact on Great Lakes winter climate; the positive phase of NAO is usually associated with moderate warming. When the two occurred simultaneously, the combined effects of La Niña and +NAO resulted in a negative East Pacific pattern with a negative center over Alaska/Western Canada, a positive center in the eastern North Pacific (north of Hawaii), and an enhanced positive center over the eastern and southern United States. The overall pattern prohibited the movement of the Arctic air mass into mid-latitudes and enhanced southerly flow and warm advection from the Gulf of Mexico over the eastern United States and Great Lakes region, leading to the record-breaking low ice cover. It is another climatic pattern that can induce extreme warming in the Great Lakes region in addition to strong El Niño events. A very similar event occurred in the winter of 1999/2000. This extreme warm winter and spring in 2012 had significant impacts on the physical environment, as well as counterintuitive effects on phytoplankton abundance.

Jia Wang, Xuezhi. Bai, John Bratton, Bingyi Wu, and Charles H. Greene NOAA Great Lakes Environmental Research Laboratory, Ann Arbor, MI USA; *jia.wang@noaa.gov Cooperative Institute for Limnology and Ecosystems Research, University of Michigan, Ann Arbor, MI Graduate School of Environmental Science, Hokkaido University, Sapporo, Japan Chinese Academy of Meteorological Sciences, Beijing, China Ocean Resources and Ecosystems Program, Cornell University, Ithaca, New York