Great Lakes Observing System

With increasing pressure placed on natural systems by growing human populations, both scientists and resource managers need a better understanding of the relationships between cumulative stress from human activities and valued ecosystem services. Societies often seek to mitigate threats to these services through large-scale, costly restoration projects, such as the over one billion dollar Great Lakes Restoration Initiative currently underway. To help inform these efforts, we merged high-resolution spatial analyses of environmental stressors with mapping of ecosystem services for all five Great Lakes. Cumulative ecosystem stress is highest in near-shore habitats, but also extends offshore in Lakes Erie, Ontario, and Michigan. Variation in cumulative stress is driven largely by spatial concordance among multiple stressors, indicating the importance of considering all stressors when planning restoration activities. In addition, highly stressed areas reflect numerous different combinations of stressors rather than a single suite of problems, suggesting that a detailed understanding of the stressors needing alleviation could improve restoration planning. We also find that many important areas for fisheries and recreation are subject to high stress, indicating that ecosystem degradation could be threatening key services. Current restoration efforts have targeted high-stress sites almost exclusively, but generally without knowledge of the full range of stressors affecting these locations or differences among sites in service provisioning. Our results demonstrate that joint spatial analysis of stressors and ecosystem services can provide a critical foundation for maximizing social and ecological benefits from restoration investments.

Careful definition and illustrative case studies are fundamental work in developing a Blue Economy. As blue research expands with the world increasingly understanding its importance, policy makers and research institutions worldwide concerned with ocean and coastal regions are demanding further and improved analysis of the Blue Economy. Particularly, in terms of the management connotation, data access, monitoring, and product development, countries are making decisions according to their own needs. As a consequence of this lack of consensus, further dialogue including this cases analysis of the blue economy is even more necessary. This paper consists of four chapters: (I) Understanding the concept of Blue Economy, (II) Defining Blue economy theoretical cases, (III) Introducing Blue economy application cases and (Ⅳ) Providing an outlook for the future. Chapter (II) and Chapter (III) summarizes all the case studies into nine aspects, each aiming to represent different aspects of the blue economy. This paper is a result of knowledge and experience collected from across the global ocean observing community, and is only made possible with encouragement, support and help of all members. Despite the blue economy being a relatively new concept, we have demonstrated our promising exploration in a number of areas. We put forward proposals for the development of the blue economy, including shouldering global responsibilities to protect marine ecological environment, strengthening international communication and sharing development achievements, and promoting the establishment of global blue partnerships. However, there is clearly much room for further development in terms of the scope and depth of our collective understanding and analysis.

The Great Lakes Acoustic Telemetry Observation System (GLATOS), organized in 2012, aims to advance and improve conservation and management of Great Lakes fishes by providing information on behavior, habitat use, and population dynamics. GLATOS faced challenges during establishment, including a funding agency-imposed urgency to initiate projects, a lack of telemetry expertise, and managing a flood of data. GLATOS now connects 190+ investigators, provides project consultation, maintains a web-based data portal, contributes data to Ocean Tracking Network’s global database, loans equipment, and promotes science transfer to managers. The GLATOS database currently has 50+ projects, 39 species tagged, 8000+ fish released, and 150+ million tag detections. Lessons learned include (1) seek advice from others experienced in telemetry; (2) organize networks prior to when shared data is urgently needed; (3) establish a data management system so that all receivers can contribute to every project; (4) hold annual meetings to foster relationships; (5) involve fish managers to ensure relevancy; and (6) staff require full-time commitment to lead and coordinate projects and to analyze data and publish results.

Harmful algal blooms (HABs) produce local impacts in nearly all freshwater and marine systems. They are a global problem that require integrated and coordinated scientific understanding leading to regional responses and solutions. Given that these natural phenomena will never be completely eliminated, improved scientific understanding of HAB dynamics coupled with monitoring and ocean observations facilitates new prediction and prevention strategies. Regional efforts are underway worldwide to create state-of-the-art HAB monitoring and forecasting tools, vulnerability assessments, and observing networks. In the United States, these include Alaska, Pacific Northwest, California, Gulf of Mexico, Gulf of Maine, Great Lakes, and the U.S. Caribbean islands. This paper examines several regional programs in the United States, European Union, and Asia and concludes that there is no one-size-fits-all approach. At the same time, successful programs require strong coordination with stakeholders and institutional sustainability to maintain and reinforce them with new automating technologies, wherever possible, to ensure integration of modelling efforts with multiple regional to national programs. Recommendations for scaling up to a global observing system for HABs can be summarized as follows: 1) advance and improve cost-effective and sustainable HAB forecast systems that address the HAB-risk warning requirements of key end-users at global and regional levels; 2) design programs that leverage and expand regional HAB observing systems to evaluate emerging technologies for Essential Ocean Variables (EOVs) and Essential Biodiversity Variables (EBVs) in order to support interregional technology comparisons and regional networks of observing capabilities; 3) fill the essential need for sustained, preferably automated, near real-time information from nearshore and offshore sites situated in HAB transport pathways to provide improved, advanced HAB warnings; 4) merge ecological knowledge and models with existing Earth System Modelling Frameworks to enhance end-to-end capabilities in forecasting and scenario-building; 5) provide seasonal to decadal forecasts to allow governments to plan, adapt to a changing marine environment, and ensure coastal industries are supported and sustained in the years ahead; and 6) support implementation of the recent calls for action by the United Nations Decade 2010 Sustainable Development Goals (SDGs) to develop indicators that are relevant to an effective and global HAB early warning system.

Advances in ocean observations and models mean increasing flows of data. Integrating observations between disciplines over spatial scales from regional to global presents challenges. Running ocean models and managing the results is computationally demanding. The rise of cloud computing presents an opportunity to rethink traditional approaches. This includes developing shared data processing workflows utilizing common, adaptable software to handle data ingest and storage, and an associated framework to manage and execute downstream modeling. Working in the cloud presents challenges: migration of legacy technologies and processes, cloud-to-cloud interoperability, and the translation of legislative and bureaucratic requirements for ‘on-premises’ systems to the cloud. To respond to the scientific and societal needs of a fit-for-purpose ocean observing system, and to maximize the benefits of more integrated observing, research on utilizing cloud infrastructures for sharing data and models is underway. Cloud platforms and the services/APIs they provide offer new ways for scientists to observe and predict the ocean’s state. High-performance mass storage of observational data, coupled with on-demand computing to run model simulations in close proximity to the data, tools to manage workflows, and a framework to share and collaborate, enables a more flexible and adaptable observation and prediction computing architecture. Model outputs are stored in the cloud and researchers either download subsets for their interest/area or feed them into their own simulations without leaving the cloud. Expanded storage and computing capabilities make it easier to create, analyze, and distribute products derived from long-term datasets. In this paper, we provide an introduction to cloud computing, describe current uses of the cloud for management and analysis of observational data and model results, and describe workflows for running models and streaming observational data. We discuss topics that must be considered when moving to the cloud: costs, security, and organizational limitations on cloud use. Future uses of the cloud via computational sandboxes and the practicalities and considerations of using the cloud to archive data are explored. In conclusion, visions of a future where cloud computing is ubiquitous are discussed.

Oxygen depletion in bottom waters of lakes and coastal regions is expanding worldwide. To examine the causes of hypoxia, we quantified the drivers of benthic oxygen uptake in Green Bay, Lake Michigan, USA, using 2 techniques, aquatic eddy covariance and sediment core incubation. We investigated benthic oxygen uptake along a gradient in C deposition, including shallow water near the riverine source of eutrophication and deeper waters of lower Green Bay where high net sediment deposition occurs. Time-averaged eddy covariance oxygen uptake was high near the source of eutrophication (11.5 mmol m−2 d−1) and at the shallower of the high deposition sites (9.8 mmol m−2 d−1). The eddy covariance technique revealed a decrease in benthic oxygen uptake with depth at the high deposition sites. These patterns were consistent with benthic uptake being driven by the deposition of autochthonous production. Additionally, eddy covariance revealed a nearly proportional relationship between benthic oxygen uptake and current velocity at all sites. Specifically, because of the lake seiche, water velocity typically varied 3× at a site and caused a 3× variation in benthic oxygen uptake. A summer storm also doubled bottom-water velocities and caused a further doubling of uptake to 28 mmol m−2 d−1. This high sensitivity of benthic oxygen uptake to seiche-driven water velocities indicates that redox conditions in surficial cohesive sediments are highly dynamic.

Tomorrow’s smart lake will able to predict what could happen and to identify actions that affect the trajectory of the lakes. Smart Lake Erie – the proof of concept – will integrate data from distributed sensors using resilient networks to feed adaptive, predictive analytics that define and perhaps even perform necessary management actions. This paper describes Smart Lake Erie pilot as a series of steps including convening innovation challenges, engaging stakeholders, securing the core observation system, and designing and operationalizing a sustainable Harmful Algae Bloom Early-Warning System. The technology platform of the pilot will be a window into what is needed to serve new contributors, new service providers, new stakeholders and consumers of the data and information service paradigm. Lessons learned are drawn from the early implementation of the pilot which are applicable to the larger Great Lakes region, other Region Associations within the U.S. Integrated Ocean Observing System, and the Global Ocean Observing System.

High-frequency water level fluctuations (HFWLF), such as infragravity waves, meteotsunamis, and wind-generated waves, are significant coastal hazards for the Great Lakes and coastal communities. These fluctuations occur over periods of 30 seconds to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{1 2}$</tex> minutes and impact infrastructure, navigation, and public safety. Current observational networks struggle to capture these events, especially in the infragravity frequency band (30 seconds to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{5}$</tex> minutes), due to limitations in monitoring systems and numerical models. To address this gap, we deployed an innovative network of Sofar Ocean's Spotter buoys with bottom-mounted pressure sensors, sampling at 2 Hz. This network provides high-resolution data across Lake Michigan, Lake Huron, and Lake Superior, enabling detailed analysis of HFWLF. By detecting water level oscillations at unprecedented frequencies, we identify infragravity waves, meteotsunamis, and wind waves, often overlooked in previous efforts. Our analysis focuses on multiple Great Lakes locations, examining temporal and spatial characteristics of these fluctuations. We study event persistence, amplitude, duration, and frequency, linking them with local meteorological conditions and coastal processes. This data enhances risk assessment and forecasting of coastal hazards like erosion, flooding, and structural damage. Ultimately, integrating high-frequency data into operational models improves early detection and mitigation of HFWLF events, supporting infrastructure resilience and public safety in the Great Lakes region.

Ocean observing systems, including the Great Lakes, are critical for monitoring environmental conditions that impact humans. A network of Regional Associations has developed observing systems that are responsive to regional priorities and meet national data management standards. As part of the continued evolution of these observing systems, there are more pan-regional coordinated projects moving forward that are providing societal benefits across many sectors.

Geospatial inventories and identification of gaps in bathymetric data coverage often rely primarily on the absence or presence of data—if depth measurements exist in a particular location, that area is classified as “mapped”. These assessments typically analyze coverage at a fixed resolution, without considering how characteristics such as depth, density or recency of the source data may change across spatial scales. Consequently, these methods have the potential to misrepresent coverage. Additionally, when applied to the Great Lakes, analysis is usually segregated between Canada and the United States. To help address these shortcomings, the Great Lakes Observing System (GLOS) has developed an innovative method to assess bi-national bathymetric data coverage in the Great Lakes based on hierarchical hexagonal spatial indexing. This approach considers variables such as water depth and data density, resulting in an accurate spatial depiction of bathymetric coverage extensible across multiple spatial scales. This approach is particularly relevant across large areas where the characteristics of data can vary significantly due to different acquisition methods, survey dates and bottom morphologies.