January 2020 - Present
ALSIP, P.J., H. ZHANG, M.D. ROWE, E.S. RUTHERFORD, D.M. MASON, C.M. Riseng, and Z. Su. Modeling the interactive effects of nutrient loads, meteorology, and invasive mussels on suitable habitat for Bighead and Silver Carp in Lake Michigan. Biological Invasions 22:2763-2785 (DOI:10.1007/s10530-020-02296-4) (2020). https://www.glerl.noaa.gov/pubs/fulltext/2020/20200025.pdf
Anthropogenic stressors that affect ecological processes in the Laurentian Great Lakes can impact their susceptibility to bioinvasions. Bighead Hypophthalmichthys nobilis and Silver Carp H. molitrix, collectively ‘bigheaded carps’ (BHC), are planktivorous fishes threatening to invade Lake Michigan. While previous studies indicate the lake contains habitat suitable for BHC growth, there is a need to understand how anthropogenic-driven changes to the abiotic and biotic environment could alter its vulnerability to BHC. We applied a spatially explicit model of BHC growth rate potential (GRP; g g−1 d−1) to nine biophysical model scenarios to evaluate changes in habitat suitability in Lake Michigan. Scenarios differed in meteorology (cool, reference, warm), annual tributary phosphorus loads (0, 3300, and 5600 MTA), and the presence/absence of invasive dreissenid mussels. Mussel effects on BHC GRP relied on their contact with the surface mixed layer (SML), the depth of which was affected by meteorology. The warm year advanced the expansion of Bighead Carp habitat by increasing temperature-dependent foraging rates and lessening the time of competitive interaction with mussels due to earlier stratification separating mussels from the SML. Phosphorus loads were the most influential driver of the lake’s suitability. Compared to present conditions, we estimate BHC could have grown an additional 8–40% annually in the 1980s when mussels were not in the lake and phosphorus loads were higher. Our study demonstrates how climate change and nutrient enrichment can increase Lake Michigan’s vulnerability to BHC by affecting thermal regime and productivity, thereby limiting negative effects of dreissenid mussels on BHC growth.
ANDERSON, E.J., and G. Mann. A high-amplitude atmospheric inertia– gravity wave-induced meteotsunami in Lake Michigan. Natural Hazards (DOI:10.1007/s11069-020-04195-2) (2020). https://www.glerl.noaa.gov/pubs/fulltext/2020/20200026.pdf (IN PRESS)
On Friday, April 13, 2018, a high-amplitude atmospheric inertia–gravity wave packet with surface pressure perturbations exceeding 10 mbar crossed the lake at a propagation speed that neared the long-wave gravity speed of the lake, likely producing Proudman resonance. A set of meteotsunami waves struck the shores near Ludington, Michigan, a coastal community along the sandy dunes of Lake Michigan. During the event, harbor walls were overtopped, damage occurred to shoreline homes and boat docks, and water intake pumps were impacted due to the large change in water level. To fully understand the generation of this event and the impacts to the coastal community, we have carried out atmospheric and hydrodynamic model simulations of the inertia–gravity and meteotsunami waves. Atmospheric simulation of the inertia–gravity waves was performed using a high-resolution model for the Great Lakes region that mimics the National Oceanic and Atmospheric Administration High-Resolution Rapid Refresh operational model. Surface meteorological conditions were supplied to the Lake Michigan-Huron Operational Forecast System, an operational model used for hydrodynamic forecast guidance. This is the first documented case of a meteotsunami generated by an atmospheric inertia–gravity wave in the Great Lakes, and it provides an evaluation of existing and proposed operational infrastructure as it pertains to meteotsunami forecasting in the USA.
Bai, P., J. WANG, P. CHU, N. HAWLEY, A.F. MANOME, J. KESSLER, B.M. LOFGREN, D. BELETSKY, E.J. ANDERSON, and Y. Li. Modeling the ice-attenuated waves in the Great Lakes. Ocean Dynamics 70:991-1003 (2020). https://www.glerl.noaa.gov/pubs/fulltext/2020/20200023.pdf
A partly coupled wave-ice model with the ability to resolve ice-induced attenuation on waves was developed using the Finite-Volume Community Ocean Model (FVCOM) framework and applied to the Great Lakes. Seven simple, flexible, and efficient parameterization schemes originating from the WAVEWATCH III® IC4 were used to quantify the wave energy loss during wave propagation under ice. The reductions of wind energy input and wave energy dissipation via whitecapping and breaking due to presence of ice were also implemented (i.e., blocking effect). The model showed satisfactory performance when validated by buoy-observed significant wave height in ice-free season at eight stations and satellite-retrieved ice concentration. The simulation ran over the basin-scale, five-lake computational grid provided a whole map of ice-induced wave attenuation in the heavy-ice year 2014, suggesting that except Lake Ontario and central Lake Michigan, lake ice almost completely inhibited waves in the Great Lakes under heavy-ice condition. A practical application of the model in February 2011 revealed that the model could accurately reproduce the ice-attenuated waves when validated by wave observations from bottom-moored acoustic wave and current profiler (AWAC); moreover, the AWAC wave data showed quick responses between waves and ice, suggesting a sensitive relationship between waves and ice and arguing that accurate ice modeling was necessary for quantifying wave-ice interaction.
Baracchini, T., P.Y. CHU, J. Sukys, G. Lieberherr, S. Wunderle, A. Wuest, and D. Bouffard. Data assimilation of in situ and satellite remote sensing data to 3D hydrodynamic lake models: a case study using Delft3D-FLOW v4.03 and OpenDA v2.4. Geoscientific Model Development 13:1267-1284 (DOI:10.5194/gmd-13-1267-2020) (2020). https://www.glerl.noaa.gov/pubs/fulltext/2020/20200007.pdf
The understanding of physical dynamics is crucial to provide scientifically credible information on lake ecosystem management. We show how the combination of in situ observations, remote sensing data, and three-dimensional hydrodynamic (3D) numerical simulations is capable of resolving various spatiotemporal scales involved in lake dynamics. This combination is achieved through data assimilation (DA) and uncertainty quantification. In this study, we develop a flexible framework by incorporating DA into 3D hydrodynamic lake models. Using an ensemble Kalman filter, our approach accounts for model and observational uncertainties. We demonstrate the framework by assimilating in situ and satellite remote sensing temperature data into a 3D hydrodynamic model of Lake Geneva. Results show that DA effectively improves model performance over a broad range of spatiotemporal scales and physical processes. Overall, temperature errors have been reduced by 54 %. With a localization scheme, an ensemble size of 20 members is found to be sufficient to derive covariance matrices leading to satisfactory results. The entire framework has been developed with the goal of near-real-time operational systems (e.g., integration into meteolakes.ch).
Breck, J.T., D.P. Simon, E.S. RUTHERFORD, B.S. Low, P.J. Lamberson, and M.W. Rogers. The geometry of reaction norms yields insights on classical fitness functions for Great Lakes salmon. Plos One (DOI:10.1371/journal.pone.0228990) (2020). https://www.glerl.noaa.gov/pubs/fulltext/2020/20200008.pdf
Life history theory examines how characteristics of organisms, such as age and size at maturity, may vary through natural selection as evolutionary responses that optimize fitness. Here we ask how predictions of age and size at maturity differ for the three classical fitness functions–intrinsic rate of natural increase r, net reproductive rate R0, and reproductive value Vx−for semelparous species. We show that different choices of fitness functions can lead to very different predictions of species behavior. In one’s efforts to understand an organism’s behavior and to develop effective conservation and management policies, the choice of fitness function matters. The central ingredient of our approach is the maturation reaction norm (MRN), which describes how optimal age and size at maturation vary with growth rate or mortality rate. We develop a practical geometric construction of MRNs that allows us to include different growth functions (linear growth and nonlinear von Bertalanffy growth in length) and develop two-dimensional MRNs useful for quantifying growth-mortality trade-offs. We relate our approach to Beverton-Holt life history invariants and to the Stearns-Koella categorization of MRNs. We conclude with a detailed discussion of life history parameters for Great Lakes Chinook Salmon and demonstrate that age and size at maturity are consistent with predictions using R0 (but not r or Vx) as the underlying fitness function.
Choi, J., C.D. Troy, N. HAWLEY, M.J. McCormick, and M.G. Wells. Lateral dispersion of dye and drifters in the center of a large lake. Limnology and Oceanography 65(2):336-348 (DOI:10.1002/lno.11302) (2020).
To better understand lateral dispersion of buoyant and nonbuoyant pollutants within the surface waters of large lakes, two lateral dispersion experiments were carried out in Lake Michigan during the stratified period: (1) a dye tracking experiment lasting 1 d; and (2) a drifter tracking experiment lasting 24 d. Both the dye patch and drifters were surface‐released at the center of Lake Michigan's southern basin. Near‐surface shear induced by near‐inertial Poincaré waves partially explains elevated dye dispersion rates (1.5–4.2 m2 s−1). During the largely windless first 5 d of the drifter release, the drifters exhibited nearly scale‐independent dispersion ( K ∼ L0.2), with an average dispersion coefficient of 0.14 m2 s−1. Scale‐dependent drifter dispersion ensued after 5 d, with K ∼ L1.09 and corresponding dispersion coefficients of 0.3–2.0 m2 s−1 for length scales L = 1500–8000 m. The largest drifter dispersion rates were found to be associated with lateral shear‐induced spreading along a thermal front. Comparisons with other systems show a wide range of spreading rates for large lakes, and larger rates in both the ocean and the Gulf of Mexico, which may be caused by the relative absence of submesoscale processes in offshore Lake Michigan.
Dillon, R.A., J.D. Conroy, L. Rudstam, P.F. Craigmile, D.M. MASON, and S.A. Ludsin. Towards more robust hydroacoustic estimates of fish abundance in the presence of pelagic macroinvertebrates. FIsheries Research 230(2020). (IN PRESS)
The inclusion of unwanted targets in hydroacoustic surveys biases estimates of fish abundance. Thus, knowledge of frequency-dependent responses of unwanted targets (e.g., pelagic macroinvertebrates) can help ensure that transducer frequencies are used that minimize this bias. We determined how fish density estimates varied across multiple frequencies when the larval stage of a midge, Chaoborus, was present in the water column. We hypothesized that fish density estimates would increase with increasing transducer frequency, owing to greater backscattering by Chaoborus at higher frequencies than lower ones, which allows it to be included with the backscattering caused by fish. We found that fish density estimates were always greater at higher frequencies (e.g., 120 and 200 kHz) compared to a lower one (70 kHz) in several productive north-temperate reservoirs. Furthermore, pairwise comparisons of total (i.e., fish plus Chaoborus) backscattering showed that significantly more backscattering occurred at higher rather than lower frequencies. We also found that fish density estimates varied between spring and summer, partially owing to inter-seasonal size variation in Chaoborus that influenced its backscattering. Beyond demonstrating why the presence of pelagic macroinvertebrates needs to be considered when estimating fish abundance with hydroacoustics, we provide methods to identify and reduce this bias.
FRY, L.M., D. Apps, and A.D. GRONEWOLD. Operational Seasonal Water Supply and Water Level Forecasting for the Laurentian Great Lakes. Journal of Water Resources Planning and Management 143(9)(DOI:10.1061/(ASCE)WR.1943-5452.0001214) (2020). https://www.glerl.noaa.gov/pubs/fulltext/2020/20200019.pdf
Seasonal water supply forecasts are a critical component of regional water resources management planning. Across the United States, multiple modeling tools and operational protocols have evolved over time to address this need. Here, the authors document, assess, and recommend improvements to the current operational water supply forecasting protocols employed in managing flows through, and water levels across, Earth’s largest lake system: the Laurentian Great Lakes. The water resources management actions for this massive system are critically linked to North America’s economy, and to human health and safety, through planning and policy decisions related to hydropower management, commercial navigation, and water level fluctuations. The authors’ assessment indicates that existing operational seasonal water supply forecasting systems for the Great Lakes have moderate skill and that there are several high-priority areas for improvement, including improved representation of initial hydrological conditions, aligning variability in future meteorological forcings with climate-scale projections, and robust representations of forecast uncertainty.
GRONEWOLD, A.D., J.P. SMITH, L.K. Read, and J.L. Crooks. Reconciling the water balance of large lake systems. Advances in Water Resources 137(DOI:10.1016/j.advwatres.2020.103505) (2020).
Water balance models are commonly employed to improve understanding of drivers behind changes in the hydrologic cycle across multiple space and time scales. Generally, these models are physically-based, a feature that can lead to unreconciled biases and uncertainties when a model is not encoded to be faithful to changes in water storage over time. Statistical methods represent one approach to addressing this problem. We find, however, that there are very few historical hydrological modeling studies in which bias correction and uncertainty quantification methods are routinely applied to ensure fidelity to the water balance. Importantly, we know of none (aside from preliminary applications of the model we advance in this study) applied specifically to large lake systems. We fill this gap by developing and applying a Bayesian statistical analysis framework for inferring water balance components specifically in large lake systems. The model behind this framework, which we refer to as the L2SWBM (large lake statistical water balance model), includes a conventional water balance model encoded to iteratively close the water balance over multiple consecutive time periods. Throughout these iterations, the L2SWBM can assimilate multiple preliminary estimates of each water balance component (from either historical model simulations or interpolated in situ monitoring data, for example), and it can accommodate those estimates even if they span different time periods. The L2SWBM can also be executed if data for a particular water balance component are unavailable, a feature that underscores its potential utility in data scarce regions. Here, we demonstrate the utility of our new framework through a customized application to the Laurentian Great Lakes, the largest system of lakes on Earth. Through this application, we find that the L2SWBM is able to infer new water balance component estimates that, to our are knowledge, are the first ever to close the water balance over a multi-decadal historical period for this massive lake system. More specifically, we find that posterior predictive intervals for changes in lake storage are consistent with observed changes in lake storage across this period over simulation time intervals of both 6 and 12 months. In additional to introducing a framework for developing definitive long-term hydrologic records for large lake systems, our study provides important insights into the origins of biases in both legacy and state-of-the-art hydrological models, as well as regional and global hydrological data sets.
Ivan, L.N., D.M. MASON, H. ZHANG, E.S. RUTHERFORD, T.S. HUNTER, S.E. Sable, A.T. Adamack, and K. Rose. Potential establishment and ecological effects of bighead and silver carp in a productive embayment of the Laurentian Great Lakes. Biological Invasions 22(8):2473-2495 (DOI:10.1007/s10530-020-02263-z) (2020). https://www.glerl.noaa.gov/pubs/fulltext/2020/20200024.pdf
Bighead carp H. nobilis and silver carp Hypothalmichthys molitrix (collectively bigheaded carps, BHC) are invasive planktivorous fishes that threaten to enter the Laurentian Great Lakes and disrupt food webs. To assess the likelihood of BHC establishment and their likely effects on the food web of Saginaw Bay, Lake Huron, we developed a multi-species individual-based bioenergetics model that tracks individual bighead and silver carp, four key fish species, and seven prey biomass groups over 50 years. The model tracks the daily consumption, mortality and growth of all individuals and the biomass dynamics of interacting prey pools. We ran simulation scenarios to determine the likelihood of BHC establishment under initial introductions from 5 to 1 million yearling and older individuals, and assuming variable age-0 carp survival rates (high, intermediate, and low). We bounded the survival of age-0 BHC as recruitment continues to be one of the biggest unknowns. We also simulated the potential effects of an established population of 1 million bighead carp or silver carp assuming variation in age-0 survival. Results indicated that as few as 10 BHC could establish a population assuming high or intermediate age-0 survival, but at least 100,000 individuals were needed to establish a population assuming low age-0 survival. BHC had negative effects on plankton and planktivorous fish biomass, which increased with BHC density. However, piscivorous walleye Sander vitreus appeared to benefit from BHC establishment. The potential for BHC to establish and affect ecologically and economically important fish species in Saginaw Bay is a cause for concern.
Kelley, J.G.W., Y. Chen, E.J. ANDERSON, G.A. LANG, and P. Machuan. Upgrade of NOS Lake Michigan and Lake Huron operational forecast systems to FVCOM : model development and hindcast skill assessment. NOAA technical memorandum NOS CS 42. NOAA National Ocean Service, Silver Spring, MD, 116 pp. (DOI:10.25923/mmyb-qh56) (2020). https://repository.library.noaa.gov/view/noaa/23891
NOS Lake Michigan-Huron Operational Forecast System (LMHOFS) is a three-dimensional lake forecast modeling system which uses near real-time atmospheric analyses, river observations and numerical weather prediction model forecast guidance to generate hourly nowcasts and forecast guidance out to 120 hours of three-dimensional water temperatures and currents and two-dimensional water levels for Lakes Michigan and Huron. The present operational NOS forecast systems for the two lakes, LMOFS and LHOFS, use the Great Lakes version of the Princeton Ocean Model (POMGL) as its core three-dimensional numerical oceanographic forecast model and have a horizontal resolution of 5 km (3.1 mi) and 20 vertical sigma (terrain-following) levels and provide forecast guidance out to 60 hours. LMHOFS has been developed using the Finite Volume Community Ocean Model (FVCOM) with a horizontal resolution ranging from 100 m (328 ft) near the shore to 2.5 km (1.6 mi) offshore and with 21 vertical sigma levels. In addition, unlike current LHOFS and LMOFS, this new OFS combined Lake Michigan and Lake Huron together as one model grid domain in order to simulate the flow between the two lakes via the Straits of Mackinac. LMHOFS is another collaborative project among NOAA’s Great Lakes Environmental Research Laboratory (GLERL), the National Ocean Service’s (NOS) Coast Survey Development Laboratory (CSDL), the Center for Operational Oceanographic Products and Services (CO-OPS), and the FVCOM Development Team at the University of Massachusetts-Dartmouth. NOS’ Lake Erie Operational Forecast System (LEOFS) was the first of the Great Lakes Operational Forecast System to be upgraded to FVCOM. LEOFS was implemented operational in 2016. The accuracy of predictions from LMHOFS was evaluated by thorough NOS comparisons to observations for three NOS skill assessment scenarios: 1) hindcast, 2) the semi-operational nowcast, and 3) the semi-operational forecast guidance. This report describes the results of the hindcast skill assessment. A similar skill assessment report for the semi-operational nowcasts and forecast guidance is being prepared by NOS/CO-OPS.
Labuhn, K., A.D. GRONEWOLD, T. Calappi, A. MacNeil, C.W. Brown, and E.J. ANDERSON. Towards an Operational Flow Forecasting System for the Upper Niagara River. Journal of Hydraulic Engineering 146(9)(DOI:10.1061/(ASCE)HY.1943-7900.0001781) (2020).
The authors developed a Hydrologic Engineering Center–River Analysis System (HEC–RAS) model to serve as the key component of a new, first-of-its-kind, short-term operational flow forecasting system for the Niagara River. The Niagara River transports a continental-scale flow (with an annual mean of roughly 6,300 m3/s) that supports the economy of both the United States and Canada through hydropower generation, tourism, and other activities. The river also serves as a link connecting the two most downstream lakes (Lakes Erie and Ontario) in the largest system of lakes on Earth. Despite its significance, the authors know of no federally operated, short-term forecasting system for the Niagara River. Hydropower facilities management and other water resources management activities on the river have historically relied on an array of experimental, in-house, or proprietary models to simulate and forecast Niagara River flows. The study presented here fills this gap in large-scale hydraulic modeling and engineering science by calibrating a HEC–RAS model for the Upper Niagara River and customizing it to meet the operational requirements of the National Oceanic and Atmospheric Administration (NOAA) National Weather Service (NWS) Northeast River Forecasting Center (NERFC). The skill of the new forecasting system, which was recently deployed in its operational environment at the NERFC, will depend in large part on the accuracy of meteorological boundary conditions. The authors envision a more comprehensive assessment of the system’s forecasting skill and other potential future model improvements as an area for future research.
LIU, Q., M.D. ROWE, E.J. ANDERSON, C.A. STOW, and T.H. JOHENGEN. Probabilistic forecast of microcystin toxin using satellite remote sensing, in situ observations and numerical modeling. Environmental Modelling & Software 128(DOI:10.1016/j.envsoft.2020.104705) (2020). https://www.glerl.noaa.gov/pubs/fulltext/2020/20200012.pdf
Lake Erie has experienced a resurgence of cyanobacterial harmful algal blooms (CHABs) since the early 2000's dominated by Microcystis aeruginosa, which produce toxins known as microcystins. We develop an approach to predict the spatially- and temporally-resolved probability of exceeding a public health advisory (PHA) level (6 μg/L) of microcystins in the western basin of Lake Erie that would be suitable for use in a forecast system, consisting of 1) an existing HAB chlorophyll forecast system, 2) a toxin-chlorophyll-a relationship that is updated weekly from observations, and 3) a statistical model relating observed relative frequency of exceeding the PHA to model predictions over a hindcast period. We evaluate the system's performance and the system's useful level of skill. This novel approach to a CHAB toxin forecast system could provide a decision support tool to Lake Erie stakeholders, and the approach may be adapted to other systems.
LOWER, E.K., N. BOUCHER, R.A. STURTEVANT, and A.K. ELGIN. 2019 Update To “An Impact Assessment Of Great Lakes Aquatic Nonindigenous Species”. NOAA Technical Memorandum GLERL-161c. NOAA, Great Lakes Environmental Research Laboratory, Ann Arbor, Mich. (DOI:10.25923/zpeq-c616) (2020). https://www.glerl.noaa.gov/pubs/tech_reports/glerl-161c/tm-161c.pdf
This report includes all major updates to the earlier Risk Assessments on nonindigenous species conducted by the GLANSIS project during the 2019 calendar year. All new assessments were conducted following the same methods outlined in the original technical memorandum, NOAA Technical Memorandum GLERL-161 “An impact assessment of Great Lakes aquatic nonindigenous species” (Sturtevant et al, 2014). All re-assessments are based on new literature surveys using the original as a baseline and conducted to the same methods. All assessments were reviewed by members of the GLANSIS Team (according to expertise) and by select external reviewers. Results of each risk assessment are incorporated into the species profiles found on the GLANSIS website (www.glerl.noaa.gov/glansis/).
LOWER, E.K., N. BOUCHER, A. Davidson, A.K. ELGIN, and R.A. STURTEVANT. 2019 Update To “A Risk Assessment Of Potential Great Lakes Aquatic Invaders”. NOAA Technical Memorandum GLERL-169c. NOAA, Great Lakes Environmental Research Laboratory, Ann Arbor, Mich., 179 pp. (DOI:10.25923/w9vp-bk86) (2020). https://www.glerl.noaa.gov/pubs/tech_reports/glerl-169c/tm-169c.pdf
This report includes all major changes to Risk Assessments of watchlist species conducted by the GLANSIS project during calendar year 2019. All new assessments were conducted following the same methods outlined in the original NOAA Technical Memorandum GLERL-169 (Fusaro et al., 2016). All re-assessments are based on new literature surveys using the original as a baseline and conducted using the same methods. All assessments were reviewed by co-authors on the GLANSIS team, and each new or substantively updated assessment was checked by select external reviewers. Results of each risk assessment are incorporated into the species profiles on the main GLANSIS site (www.glerl.noaa.gov/glansis) as well as incorporated into the new GLANSIS Risk Assessment Clearinghouse. The websites are updated more frequently and should be considered the most recent information.
MANOME, A.F., D. GILL, T. GUO, E.J. ANDERSON, and M.C. Lemos. Knowledge Co-production in a Research-to-Operation (R2O) Process for Development of a Great Lakes Ice Forecast: Reflection from a Stakeholder Engagement Workshop. Earth and Space Science Open Archive (DOI:10.1002/essoar.10501135.1) (2020). https://www.glerl.noaa.gov/pubs/fulltext/2020/20200010.pdf
In weather forecast products, stakeholder engagement in the research-to-operations (R2O) transition process has been increasingly valued yet it is far from being standardized. Engagement at multiple R2O stages and methods rigorously supported by social science are critical in implementing a practice of knowledge coproduction in such forecast products. With an example of short-term ice forecasts in the North American Great Lakes, this commentary provides a reflection of the stakeholder engagement workshop where two targeted stakeholder groups (shipping industry and U.S. Coast Guard 9 District), operational forecast providers, and scientists worked together to maximize the usability of ice forecast guidance from the National Oceanic and Atmospheric Administration (NOAA)’s Great Lakes Operational Forecast System (GLOFS). The workshop was designed carefully by social scientists to address predominant questions; what decisions do stakeholders make with ice information; what ice information do stakeholders use to support that decision-making; and what are stakeholder usability requirements for a short-term Great Lakes ice forecast? The findings from the workshop provided in-depth information to formulate recommendations to GLOFS on its user interface of the upcoming ice forecast guidance, as well as the future model development. The effort placed a steppingstone toward a new standard of R2O, where participation of stakeholders and social scientists is a formalized part of the process.
MANOME, A.F., E.J. ANDERSON, J. KESSLER, P. CHU, J. WANG, and A.D. GRONEWOLD. Simulating Impacts of Precipitation on Ice Cover and Surface Water Temperature Across Large Lakes. Journal of Geophysical Research: Oceans 125(5)(DOI:10.1029/2019JC015950) (2020). https://www.glerl.noaa.gov/pubs/fulltext/2020/20200022.pdf
Precipitation impacts on ice cover and water temperature in the Laurentian Great Lakes were examined using state‐of‐the‐art coupled ice‐hydrodynamic models. Numerical experiments were conducted for the recent anomalously cold (2014–2015) and warm (2015–2016) winters that were accompanied by high and low ice coverage over the lakes, respectively. The results of numerical experiments showed that snow cover on the ice, which is the manifestation of winter precipitation, reduced the total ice volume (or mean ice thickness) in all of the Great Lakes, shortened the ice duration, and allowed earlier warming of water surface. The reduced ice volume was due to the thermal insulation of snow cover. The surface albedo was also increased by snow cover, but its impact on the delay the melting of ice was overcome by the thermal insulation effect. During major snowstorms, snowfall over the open lake caused notable cooling of the water surface due to latent heat absorption. Overall, the sensible heat flux from rain in spring and summer was found to have negligible impacts on the water surface temperature. Although uncertainties remain in overlake precipitation estimates and model's representation of snow on the ice, this study demonstrated that winter precipitation, particularly snowfall on the ice and water surfaces, is an important contributing factor in Great Lakes ice production and thermal conditions from late fall to spring.
Marino, J.A., H.A. VANDERPLOEG, S.A. POTHOVEN, A.K. ELGIN, and S.D. Peacor. Long‐term survey data reveal large predator and temperature effects on population growth of multiple zooplankton species. Limnology and Oceanography 65(4):694+706 (DOI:10.1002/lno.11340) (2020).
Predators can strongly affect prey communities, but their influence may be difficult to distinguish from bottom-up and other environmental effects. The problem of assessing predator impact is especially difficult in large systemsthat do not allow for comparisons across multiple units (e.g., small lakes) that have varying predator density. Forinstance, the invasion of the predatory zooplankter,Bythotrephes longimanus, into the Laurentian Great Lakes contrib-uted to the nearly complete disappearance of several zooplankton species, but current effects on extant zooplanktonare not well understood. We used generalized additive models (GAMs) applied to long-term data time series(1994–2012) to examineB. longimanuseffects on zooplankton species in Lake Michigan. BecauseB. longimanusabun-dance varied over time, our approach allowed assessment of predator effects fromfield data while accounting forother factors, including food resources, temperature, and seasonality. Results suggest thatB. longimanussubstantiallyreduces some zooplankton population growth rates, with the largest effects on species thatB. longimanusaffectedmore strongly in experiments. For example, at maximumB. longimanusabundance,Daphnia mendotae,Bosminalongirostris,andDiacyclops thomasipopulation growth rates were estimated to be reduced by 17%, 30%, and 21%,respectively, compared to no effect on calanoid copepods. Results further indicated positive temperature effects onpopulation growth that differed by species. Our study thus providesfield-based evidence for ongoing impacts of inva-sive species and temperature on zooplankton production and composition, with potential consequences forplanktivorousfish, and exemplifies how GAMs can be used to determine predator effects from time series data.
Mehler, K., L.E. Burlakova, A.Y. Karatayev, A.K. ELGIN, T.F. NALEPA, C.P. Madenjian, and E. Hinchey. Long-term trends of Lake Michigan benthos with emphasis on the southern basin. Journal of Great Lakes Research 46(3):528-537 (DOI:10.1016/j.jglr.2020.03.011) (2020).
Lake Michigan benthic macrofauna have been studied for almost a century, allowing for a unique analysis of long-term changes in community structure. We examined changes in abundances of three major taxonomic groups of benthic macroinvertebrates (Diporeia, Oligochaeta, and Sphaeriidae) in southern Lake Michigan from 1931 to 2015 and identified the most likely causes for these changes. Abundances of all three groups increased during 1931–1980 with the bulk of these increases occurring in nearshore (≤50 m) waters and coincident with increased loading of phosphorus (P) and subsequent increased primary production. Abundances of all three taxa declined during 1980–2000 again mostly in nearshore waters and coincident with decreased P loading. The quagga mussel (Dreissena rostriformis bugensis) invasion was associated with a further decline in phytoplankton primary production during 2000–2015. Both Diporeia and Sphaeriidae declined in abundance during that time, with Diporeia exhibiting the more pronounced decrease of the two groups. In contrast, Oligochaeta increased in abundance during 2000–2015. The quagga mussel has become, by far, the most abundant benthic macroinvertebrate species in terms of density and biomass. Overall, the primary driver of changes in the abundances of the three major taxa during this 85-year period appeared to be changes in phytoplankton primary production due to changing P loadings and, later in the time series, Dreissena filtering. The dreissenid mussels invasions coincided with a rapid decline of Diporeia abundance, but the mechanism of this negative effect remains unidentified. In contrast, Oligochaeta likely benefited from the quagga mussel invasion, perhaps via quagga-generated food supply.
Merzel, R.L., L. Purser, T.L. Soucy, M. Olszewski, I. Colon-Bernal, M.B. Duhaime, A.K. ELGIN, and M.M. Banaszak Holl. Uptake and Retention of Nanoplastics in Quagga Mussels. Global Challenges 4(6)(DOI:10.1029/2019JC015950) (2020). https://www.glerl.noaa.gov/pubs/fulltext/2020/20200020.pdf
Here, a set of experiments to assess the feasibility of using an invasive and widespread freshwater mussel (Dreissena rostrformis bugensis ) as a sentinel species for nanoplastic detection is reported. Under laboratory experimental conditions, mussels ingest and retain fluorescent polystyrene (PS) beads with carboxylic acid (COOH) termination over a size range of 200–2000 nm. The number of beads the mussels ingested is quantified using fluorescence spectroscopy and the location of the beads in the mussels is imaged using fluorescence microscopy. PS beads of similar size (1000–2000 nm) to mussels' preferred food are trafficked in the ciliated food grooves of the gills. Beads of all sizes are observed in the mussels' digestive tracts, indicating that the mussels do not efficiently reject the beads as unwanted foreign material, regardless of size. Fluorescence microscopy shows all sizes of beads are concentrated in the siphons and are retained there for longer than one month postexposure. Combined atomic force microscopy–infrared spectroscopy and photothermal infrared spectroscopy are used to locate, image, and chemically identify the beads in the mussel siphons. In sum, these experiments demonstrate the potential for using mussels, specifically their siphons, to monitor environmental accumulation of aquatic nanoplastics.
Nalepa, T.F., L.E. Burlakova, A.K. ELGIN, A.Y. Karatayev, G.A. LANG, and K. Mehler. Abundance and Biomass of Benthic Macroinvertebrates in Lake Michigan in 2015, with a Summary of Temporal Trends. NOAA Technical Memorandum GLERL-175. NOAA, Great Lakes Environmental Research Laboratory, Ann Arbor, Mich., 42 pp. (DOI:10.25923/g0d3-3v41) (2020). https://www.glerl.noaa.gov/pubs/tech_reports/glerl-175/tm-175.pdf
POTHOVEN, S.A. The influence of ontogeny and prey abundance on feeding ecology of age‐0 Lake Whitefish (Coregonus clupeaformis) in southeastern Lake Michigan. Ecology of Freshwater Fish 29(1):103-111 (DOI:doi.org/10.1111/eff.12498) (2020).
A shift towards oligotrophic conditions in Lake Michigan has led to concern that altered trophic pathways are leading to lower early life survival and recruitment for Lake Whitefish (Coregonus clupeaformis). This study evaluated ontogenetic shifts in age‐0 Lake Whitefish diets and evaluated how feeding ecology and the amount of food eaten varied with prey abundance and composition at a site in southeastern Lake Michigan during 2014–2017. Although prey densities varied among years, cyclopoid copepods were overall the most abundant prey available. In turn, cyclopoids were the predominant prey item in diets each year, particularly for the smallest larval Lake Whitefish. However, there was a tendency for the importance of cyclopoids to decline somewhat in each diet index as fish grew and other prey such as calanoid copepods, Bosminidae, Daphniidae and/or chironomids increased in importance. High zooplankton abundance, especially high cyclopoid abundance, available to the small size groups of Lake Whitefish (<21 mm) in 2014 was associated with high food mass/fish, high number of zooplankton eaten/fish, and low incidence of empty stomachs compared with 2015–2017. As fish grew, the impact of food abundance on prey consumption diminished somewhat, indicating that the relationship between fish feeding ecology and the prey environment can change quickly with fish size during the early life period.
POTHOVEN, S.A., and H.A. VANDERPLOEG. Seasonal patterns for Secchi depth, chlorophyll a, total phosphorus, and nutrient limitation differ between nearshore and offshore in Lake Michigan. Journal of Great Lakes Research 46(3):519-527 (DOI:10.1016/j.jglr.2020.03.013) (2020).
Data on Secchi depth, chlorophyll a, total phosphorus (TP), and nutrient status of phytoplankton were collected at five nearshore sites (11–17 m deep) and two offshore sites (>100 m) between the Grand River and Muskegon River outflows during March-December 2014–2018 to describe seasonal patterns and to compare the two depth regions in southeastern Lake Michigan. In contrast to the offshore, where spring chlorophyll a and TP concentrations declined dramatically following the dreissenid mussel expansion, the nearshore region of southeastern Lake Michigan was still characterized by low Secchi depth and elevated chlorophyll a and TP in the spring. During May, median Secchi depth was 5 times higher in the offshore than the nearshore, whereas chlorophyll a and TP were over 9 and 3 times higher in the nearshore, respectively. Even though spring chlorophyll a and TP have declined substantially at some of the nearshore sites compared to 1996, particularly the sites closest to tributary outflows, the overall yield of chlorophyll a per unit TP did not change over time in the nearshore. There were indications of P-deficiency in the nearshore in 2014–2018, but P-deficiency was even more severe in the offshore during the spring where yield of chlorophyll a per unit TP was also lower than in the nearshore. Although dreissenid mussels can be abundant in the nearshore, their populations are patchy and inputs from tributaries provide conditions that apparently dampen any potential filtering impacts of mussels in the nearshore compared to the offshore, especially during the spring.
QUINN, F.H., A.H. CLITES, and A.D. GRONEWOLD. Evaluating Estimates of Channel Flow in a Continental-Scale Lake-Dominated Basin. Journal of Hydraulic Engineering 146(3)(DOI:10.1061/(ASCE)HY.1943-7900.0001685) (2020).
Accurate estimates of continental-scale channel flows are needed to understand spatiotemporal variability in water supplies and the water balance. At regional scales, models of connecting channel flows are commonly used to understand how variability in the water cycle propagates into engineering-oriented decisions related to water quantity and water quality management. Since 1958, deterministic monthly flows have been calculated for all of the connecting channels of the Great Lakes–St. Lawrence River system through a binational, multiagency coordination process. This article provides a review of these historical estimates, most of which have never appeared (or appeared decades ago) in the peer-reviewed literature, and compares them to new estimates from a novel statistical water balance model. This new model was developed using a variety of water balance component estimates across the entire Great Lakes system and includes an explicit expression of uncertainty. The findings of this research indicate that the historical range of deterministic channel flow estimates is similar to the range of uncertainty represented by the authors’ statistical water balance model. Findings also indicate that historical internationally coordinated flows for this massive lake and river system from the late 1990s through 2009 appear to be negatively biased and may need to be revised. The proposed statistical water balance model provides an ideal platform for implementing this revision and other future updates to regional water balance information.
ROWLAND, F., C.A. STOW, T.H. JOHENGEN, A.M. BURTNER, D.A. PALLADINO, D.C. GOSSIAUX, T.W. Davis, L.T. Johnson, and S.A. RUBERG. Recent patterns in Lake Erie phosphorus and chlorophyll a concentrations in response to changing loads. Environmental Science and Technology 54(2):835-841 (DOI:10.1021/acs.est.9b05326) (2020).
Despite the initial success of extensive efforts to reduce phosphorus (P) loading to Lake Erie as part of the Great Lakes Water Quality Agreement, Lake Erie appears to be undergoing a re-eutrophication and is plagued by harmful algal blooms. To offer insights into potential lake responses under differing Maumee River loads and reveal recent changes with time, we explored patterns in phosphorus and chlorophyll a data from 2008–2018 collected in western Lake Erie near the mouth of the Maumee River. We found high, but relatively stable Maumee River and lake concentrations of total P (TP) and soluble reactive P (SRP) with no discernable annual or seasonal patterns. Maumee spring TP load was not strongly related to lake TP, and lake SRP concentrations were positively but weakly related to SRP loads. Lake TP was a strong predictor of chlorophyll a, but the relationship was weaker at sites closer to the Maumee. These results highlight spatial differences both in P concentration and the relationship between TP and chlorophyll a, and indicate that spring phosphorus loads are a weak algal biomass predictor in the portion of the western basin of Lake Erie represented by these sampling stations
Scavia, D., E.J. ANDERSON, A. Dove, B. Hill, C.M. Long, and Y.-C. Wang. Lake Huron’s Phosphorus Contributions to the St. Clair–Detroit River Great Lakes Connecting Channel. Environmental Science & Technology 54(9):5550-5559 (DOI:10.1021/acs.est.0c00383) (2020). https://www.glerl.noaa.gov/pubs/fulltext/2020/20200021.pdf
The United States and Canada called for a 40% load reduction of total phosphorus from 2008 levels entering the western and central basins of Lake Erie to achieve a 6000 MTA target and help reduce its central basin hypoxia. The Detroit River is a significant source of total phosphorus to Lake Erie; it in turn has been reported to receive up to 58% of its load from Lake Huron when accounting for resuspended sediment loads previously unmonitored at the lake outlet. Key open questions are where does this additional load originate, what drives its variability, and how often does it occur. We used a hydrodynamic model, satellite images of resuspension events and ice cover, wave hindcasts, and continuous turbidity measurements at the outlet of Lake Huron to determine where in Lake Huron the undetected load originates and what drives its variability. We show that the additional sediment load, and likely phosphorus, is from wave-induced Lake Huron sediment resuspension, primarily within 30 km of the southeastern shore. When the flow is from southwest or down the center of the lake, the resuspended sediment is not detected at Canada’s sampling station at the head of the St. Clair River.
Stauffer, B.A., e. al., and i.V. WOUDE. Considerations in Harmful Algal Bloom Research and Monitoring: Perspectives From a Consensus-Building Workshop and Technology Testing. Frontiers in Marine Science 6(399)(DOI:10.3389/fmars.2019.00399) (2020). https://www.glerl.noaa.gov/pubs/fulltext/2020/20200030.pdf
Recurrent blooms of harmful algae and cyanobacteria (HABs) plague many coastal and inland waters throughout the United States and have significant socioeconomic impacts to the adjacent communities. Notable HAB events in recent years continue to underscore the many remaining gaps in knowledge and increased needs for technological advances leading to early detection. This review summarizes the main research and management priorities that can be addressed through ocean observation-based approaches and technological solutions for harmful algal blooms, provides an update to the state of the technology to detect HAB events based on recent activities of the Alliance for Coastal Technologies (ACT), offers considerations for ensuring data quality, and highlights both ongoing challenges and opportunities for solutions in integrating HAB-focused technologies in research and management. Specifically, technological advances are discussed for remote sensing (both multispectral satellite and hyperspectral); deployable in situ detection of HAB species on fixed or mobile platforms (based on bulk or taxa-specific biomass, images, or molecular approaches); and field-based and/or rapid quantitative detection of HAB toxins (via molecular and analytical chemistry methods). Suggestions for addressing challenges to continued development and adoption of new technologies are summarized, based on a consensus-building workshop hosted by ACT, including dealing with the uncertainties in investment for HAB research, monitoring, and management. Challenges associated with choosing appropriate technologies for a given ecosystem and/or management concern are also addressed, and examples of programs that are leveraging and combining complementary approaches are highlighted.
Stone, J.P., K.L. Pangle, S.A. POTHOVEN, H.A. VANDERPLOEG, S.B. Brandt, T.O. Hook, T.H. JOHENGEN, and S.A. Ludsin. Hypoxia’s impact on pelagic fish populations in Lake Erie: A tale of two planktivores. Canadian Journal of Fisheries and Aquatic Sciences 77(7):1131-1148 (DOI:10.1139/cjfas-2019-0265) (2020).
Whether bottom hypoxia has long-lasting consequences for pelagic fish populations remains speculative for most ecosystems. We explored hypoxia’s influence on two pelagic zooplanktivores in Lake Erie that have different thermal preferences: cold-water rainbow smelt (Osmerus mordax) and warm-water emerald shiners (Notropis atherinoides). To assess acute effects, we combined predictive bioenergetics-based modeling with field collections made across the hypoxic season in central Lake Erie during 2005 and 2007. To assess chronic effects, we related fishery-independent and fishery-dependent catches with hypoxia severity and top predator (walleye, Sander vitreus) abundance during 1986-2014. As our modeling predicted, hypoxia altered rainbow smelt movement and distributions, leading to avoidance of cold, hypoxic bottom waters. In response, diets shifted from benthic to pelagic organisms, and consumption and energetic condition declined. These changes were lacking in emerald shiners. Our long-term analyses showed rainbow smelt abundance and hypoxia to be negatively related and suggested that hypoxia-avoidance increases susceptibility to commercial fishing and walleye predation. Collectively, our findings show that hypoxia can negatively affect pelagic fish populations over the long-term, especially those requiring cold water.
STOW, C.A., Q. LIU, and E.J. ANDERSON. Nutrient loading and nonstationarity: The importance of differentiating the independent effects of tributary flow and nutrient concentration. WIREs Water 7(1)(DOI:10.1002/wat2.1396) (2020).
The “phosphorus loading concept,” or more generally the “nutrient loading concept,” arose from Richard Vollenweider's work in the 1960–1970s that showed correlations between phosphorus loads and various eutrophication symptoms. The initial success of target loads developed for the Great Lakes solidified the concept that nutrient loading causes eutrophication, and load targets have become common tools to reduce eutrophication. Using concepts from the field of causality, we offer additional context to the nutrient loading concept to show that the correlation between nutrient load and eutrophication is spurious; load and eutrophication have common drivers, tributary flow and tributary nutrient concentration, but load itself is not causal. Consequently, in‐lake conditions are not invariant to the same load delivered at differing flow‐concentration combinations. We then use a simulation model to evaluate the consequences of delivering the same load at various flow‐concentration combinations from the Maumee River into Lake Erie. We show that load reductions under increased tributary flows may cause in‐lake phosphorus concentration increases, potentially offsetting the anticipated effect of the load reduction. Thus, particularly under a scenario where climate change may cause systematic flow changes, it will be important to expand the nutrient loading concept to consider the independent effects of tributary flow and nutrient concentrations, to assess the effectiveness of nutrient reduction strategies.
STOW, C.A., K. GLASSNER-SHWAYDER, D.H. LEE, L. Wang, G. Arhonditsis, J.V. DePinto, and M.R. Twiss. Lake Erie phosphorus targets: An imperative for active adaptive management. Journal of Great Lakes Research 46(3)(DOI:10.1016/j.jglr.2020.02.005) (2020).
Management actions taken to meet the phosphorus load targets in the 1978 Great Lakes Water Quality Agreement proved highly successful, initially. Eutrophication symptoms abated, and attention was redirected toward other important water quality problems. However, in the early 2000s Lake Erie, in particular, began to re-experience severe algal blooms and other problems associated with excessive nutrient inputs. The 2012 GLWQA prompted the development of updated phosphorus targets, and endorsed the concept of adaptive management. We propose that an active adaptive management program that maximizes learning opportunities will be imperative to sustain any future improvements realized in response to the new targets. Every year offers natural, albeit uncontrolled experiments to exploit the adaptive management concept of “learning by doing." A carefully thought out plan of complementary monitoring and modeling, supported by stakeholder engagement, will promote an improved understanding the processes that influence lake behavior and guide essential refinements to management goals and appropriate actions to attain them. In 2019 the International Joint Commission released a set of recommendations regarding the use of modeling approaches to support adaptive management in Lake Erie. We have incorporated those recommendations herein to further inspire the Great Lakes community to invest in an active adaptive management strategy that will serve us into the future.
WANG, J., T.-Y. Yang, J. KESSLER, H. HU, and P. CHU. Great Lakes ice duration, winter severity index, cumulative freezing degree days, and atmospheric teleconnection patterns, 1973 – 2018. NOAA Technical Memorandum GLERL-174. NOAA, Great Lakes Environmental Research Laboratory, Ann Arbor, Michigan, 67 pp. (DOI:10.25923/88c9-hm22) (2020). https://www.glerl.noaa.gov/pubs/tech_reports/glerl-174/tm-174.pdf
This report investigates interannual variability in ice coverage (Bai et al., 2012; Assel et al., 1998; Assel et al., 2013). We conduct analyses of the ice coverage records—freeze-up date, break-up date, duration and annual maximum ice coverage (AMIC)—with air temperature —cumulative freezing degree days (FDD), winter severity index (WSI)—and atmospheric teleconnections—El Niño–Southern Oscillation (ENSO), Atlantic Multidecadal Oscillation (AMO), North Atlantic Oscillation (NAO), and Pacific Decadal Oscillation (PDO). In addition, we use scatter plots and linear / non-linear regression, to determinate whether they have linear or quadratic relationships. The purpose of this report is to provide users with the Great Lakes environmental parameters and in depth analyses that are easily digested and applied to resources management, projection, and planning.
Yang, T.-y., J. KESSLER, L.A. Mason, P.Y. CHU, and J. WANG. A consistent Great Lakes ice cover digital data set for winters 1973–2019. Scientific Data 7(DOI:10.1038/s41597-020-00603-1) (2020). https://www.glerl.noaa.gov/pubs/fulltext/2020/20200028.pdf
Ice formation and loss in the Laurentian Great Lakes has a strong impact on regional climate, weather, economy and ecology in North America. To record the ice changes during the winter season, Great Lakes ice cover data has been collected and maintained since 1973 by Canadian Ice Service, U.S. National Ice Center, and National Oceanic and Atmospheric Administration’s Great Lakes Environmental Research Laboratory. Throughout this long history, technology has improved and the needs of users have evolved, so Great Lakes ice cover datasets have been upgraded several times in both spatial and temporal resolutions. In order to make those long-term data consistent and accessible, we reprocessed the Great Lakes ice cover database to generate daily gridded data (1.8 km resolution) using a re-project method with Nearest Neighbor Search for spatial interpolation, and linear interpolation with categorization for temporal interpolation. This report elucidates data history, generation procedures, and file structure in order to improve access and usability of Great Lakes ice cover data.
Ye, X., P. CHU, E.J. ANDERSON, C. Huang, G.A. LANG, and P. Xue. Improved thermal structure simulation and optimized sampling strategy for Lake Erie using a data assimilative model. Journal of Great Lakes Research 46(1):144-158 (DOI:10.1016/j.jglr.2019.10.018) (2020). https://www.glerl.noaa.gov/pubs/fulltext/2020/20200006.pdf
Lake Erie has experienced substantial environmental issues (e.g., hypoxia, harmful algal blooms) for decades, which are closely related to the lake’s thermal characteristics. While three-dimensional (3D) hydrodynamic models have been widely applied to Lake Erie, challenges remain due to model representation of physical processes, errors and uncertainty in boundary conditions and forcing terms. The Great Lakes region has a relatively dense and long-term observational record, and these observational data have been used for model initialization and verification, but have not been incorporated into 3D model simulations through data assimilation (DA) to create reanalysis products or improve short-term forecasts. In this work, we developed and evaluated DA to improve thermal structure simulation of Lake Erie. Moored instrument data and satellite data are incorporated into a data-assimilative hydrodynamic model for analysis and evaluation. Results show that DA can effectively improve the model performance to create reanalysis fields when the DA formulation is appropriately developed in recognition of the dynamic complexities and anisotropic error covariances of Lake Erie. The data assimilative model also improves forecasting accuracy and restrains forecasting uncertainty to an acceptable level on a timescale of 1–7 days after being unleashed from DA. Lastly, data sampling strategies based on an error correlation map are examined. Results show the method can effectively reduce the sampling effort while still achieving similar model skills with potential for optimal design of an observation network or field sampling strategy.
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