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Generally, ports in the North American Great Lakes are not supported with navigational guidance (water level, water temperature, currents, ice) by NOAA's Great Lakes Operational Forecast System (GLOFS). To examine the benefit of extending model coverage to this critical infrastructure, a linked hydrologic-hydrodynamic framework was developed for the Twin Ports of Duluth-Superior in western Lake Superior, and tested over three case studies of flooding due to storm surge and/or river flooding. Streamflow from 22 National Water Model (NWM) simulated and 3 gauged inflows within the domain was injected into a hydrodynamic model built on the Finite Volume Community Ocean Model (FVCOM), with a wet-dry grid that covered the harbor and surrounding floodplain. Model results from flood simulations compared well against time-series of water level and streamflow at gauges within the harbor, with root mean square error (RMSE) and bias values small relative to the typical fluctuations. Inclusion of NWM-simulated tributaries improved the accuracy of modeled water levels in the harbor, and increased simulated current speeds in navigational channels by up to 0.5 ms−1. Modeled inundation extent in the floodplain closely matched flood extent surveys conducted in response to a record flood event in 2012, demonstrating the capability of the modeling framework to provide flood guidance in the complex coastal setting. [ABSTRACT FROM AUTHOR]

In response to record-breaking flooding on Lake Champlain in 2011, the International Joint Commission launched a 5-year study to explore solutions to flooding in the binational Lake Champlain-Richelieu River (LCRR) basin. As a component of the study, a real-time flood forecasting modeling system was developed to provide short-term (5 days) water level and wave forecast guidance, intended to enhance flood preparedness by providing advanced warning of flooding to residents and other stakeholders within the basin. The system consists of a hydrodynamic model built on the Finite Volume Community Ocean Model (FVCOM) with one-way coupling to a WAVEWATCH III wave model. The National Water Model stream network was expanded to include the entire LCRR domain and is used to inform river inflows into the system. Water level output from the hydrodynamic model shows strong agreement with gauge observations at annual and short-term time scales, with an increasing negative bias at longer forecast horizons. Modeled significant wave heights compared well with observations from a wave buoy deployed as part of the study and also have a negative bias in the latter portions of the forecast. The scale of the errors in modeled water levels and wave heights is consistent with an underestimation of river inflow and wind speed inputs, respectively, based on validation of this model forcing against available observations. The development and validation of the LCRR modeling system serve as a precursor for the first operational real-time 3D hydrodynamic-wave forecast system for Lake Champlain. [ABSTRACT FROM AUTHOR]

The interannual and decadal variability of summer Arctic sea ice is analyzed, using the longest reconstruction (1850–2017) of Arctic sea ice extent available, and its relationship with the dominant internal variabilities of the climate system is further investigated quantitatively. The leading empirical orthogonal function (EOF) mode of summer Arctic sea ice variability captures an in-phase fluctuation over the Arctic Basin. The second mode characterizes a sea ice dipolar pattern with out-of-phase variability between the Pacific Arctic and the Atlantic Arctic. Summer sea ice variability is impacted by the major internal climate patterns: the Atlantic multidecadal oscillation (AMO), North Atlantic Oscillation (NAO), Arctic Oscillation (AO), Pacific decadal oscillation (PDO), and the Dipole Anomaly (DA), with descending order of importance based on the multiple regression analyses. The internal climate variability of the five teleconnection patterns accounts for up to 46% of the total variance in sea ice mode 1 (thermodynamical effect), and up to 30% of the total variance in mode 2 (dynamical effect). Furthermore, the variability of sea ice mode 1 decreased from 46% during 1953–2017 to 28% during 1979–2017, while the variability of mode 2 increased from 11% during 1953–2017 to 30% during 1979–2017. The increasingly greater reduction of Arctic summer sea ice during the recent four decades was enhanced with the positive ice/ocean albedo feedback loop being accelerated by the Arctic amplification, contributed in part by the atmospheric thermodynamical forcing from −AO, +NAO, +DA, +AMO, and −PDO and by the dynamical transpolar sea ice advection and outflow driven by +DA- and +AMO-derived strong anomalous meridional winds. Further analysis, using multiple large ensembles of climate simulations and single-forcing ensembles, indicates that mode 1 of summer sea ice, dominated by the multidecadal oscillation, is partially a forced response to anthropogenic warming. [ABSTRACT FROM AUTHOR]

An extraordinary strong wind‐driven upwelling event occurred in Lake Superior in summer of 2010 when the lake was strongly stratified. In this paper, a detailed three‐dimensional (3‐D) investigation of the current and thermal structures during the upwelling event was conducted using in situ observations, remote sensing products, and the results of a long‐term numerical simulation. A 3‐D finite‐volume coupled ice–ocean model tailored for the Laurentian Great Lakes was employed for this purpose. The model was validated with temperature observations at National Oceanic and Atmospheric Administration buoys and mooring data from 2010. The upwelling event observed in satellite imagery and at a mooring station was reproduced by the model, showing a cooling of as much as 10°C in August 2010 along the northwestern coast. The relationship between the alongshore wind and the offshore thermocline displacement (upwelling front) derived in theoretical work (Csanady, 1977, https://doi.org/10.1029/JC082i003p00397) was used to calculate upwelling front movement offshore and found to be in in close agreement with model prediction. A significant correlation between alongshore wind stress and lake temperature change in the upwelling zone was found with a correlation coefficient of −0.87. A simple linear heat balance model explained most of variability in temperature. Plain Language Summary: An extraordinary strong upwelling event observed during summer 2010 in Lake Superior was reproduced by a numerical ocean model. A detailed three‐dimensional (3‐D) investigation of the current and thermal structures during the upwelling event was conducted using in situ observations, remote sensing products, and the results of a long‐term numerical simulation. A 4‐day long strong, persistent alongshore wind was the major driving force of this event. A significant correlation between alongshore wind stress and lake temperature change in the upwelling zone was found. A simple linear heat balance model explained most of variability in temperature measurements. The relationship between the alongshore wind and the offshore displacement of the upwelling front derived in theoretical work was used to calculate upwelling front movement offshore and found to be in in close agreement with model prediction. Key Points: An extraordinarily strong upwelling event observed during summer 2010 in Lake Superior was reproduced by a coupled ice–ocean modelStrong correlation between lake surface temperature change in the upwelling zone and alongshore wind stress was found (with a correlation coefficient of −0.87); a simple linear heat balance model explained most of time rate of change in temperatureOffshore thermocline displacement (upwelling front) in the model was in good agreement with predictions of an analytical model that employed alongshore wind and initial thermal structure [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Efficient management and prevention of species invasions requires accurate prediction of where species of concern can arrive and persist. Species distribution models provide one way to identify potentially suitable habitat by developing the relationship between climate variables and species occurrence data. However, these models when applied to freshwater invasions are complicated by two factors. The first is that the range expansions that typically occur as part of the invasion process violate standard species distribution model assumptions of data stationarity. Second, predicting potential range of freshwater aquatic species is complicated by the reliance on terrestrial climate measurements to develop occurrence relationships for species that occur in aquatic environments. To overcome these obstacles, we combined a recently developed algorithm for species distribution modeling—range bagging—with newly available aquatic habitat-specific information from the North American Great Lakes region to predict suitable habitat for three potential invasive species: golden mussel, killer shrimp, and northern snakehead. Range bagging may more accurately predict relative suitability than other methods because it focuses on the limits of the species environmental tolerances rather than central tendency or “typical” cases. Overlaying the species distribution model output with aquatic habitat-specific data then allowed for more specific predictions of areas with high suitability. Our results indicate there is suitable habitat for northern snakehead in the Great Lakes, particularly shallow coastal habitats in the lower four Great Lakes where literature suggests they will favor areas of wetland and submerged aquatic vegetation. These coastal areas also offer the highest suitability for golden mussel, but our models suggest they are marginal habitats. Globally, the Great Lakes provide the closest match to the currently invaded range of killer shrimp, but they appear to pose an intermediate risk to the region. Range bagging provided reliable predictions when assessed either by a standard test set or by tests for spatial transferability, with golden mussel being the most difficult to accurately predict. Our approach illustrates the strength of combining multiple sources of data, while reiterating the need for increased measurement of freshwater habitat at high spatial resolutions to improve the ability to predict potential invasive species. [ABSTRACT FROM AUTHOR]

Efficient management and prevention of species invasions requires accurate prediction of where species of concern can arrive and persist. Species distribution models provide one way to identify potentially suitable habitat by developing the relationship between climate variables and species occurrence data. However, these models when applied to freshwater invasions are complicated by two factors. The first is that the range expansions that typically occur as part of the invasion process violate standard species distribution model assumptions of data stationarity. Second, predicting potential range of freshwater aquatic species is complicated by the reliance on terrestrial climate measurements to develop occurrence relationships for species that occur in aquatic environments. To overcome these obstacles, we combined a recently developed algorithm for species distribution modeling—range bagging—with newly available aquatic habitat‐specific information from the North American Great Lakes region to predict suitable habitat for three potential invasive species: golden mussel, killer shrimp, and northern snakehead. Range bagging may more accurately predict relative suitability than other methods because it focuses on the limits of the species environmental tolerances rather than central tendency or “typical” cases. Overlaying the species distribution model output with aquatic habitat‐specific data then allowed for more specific predictions of areas with high suitability. Our results indicate there is suitable habitat for northern snakehead in the Great Lakes, particularly shallow coastal habitats in the lower four Great Lakes where literature suggests they will favor areas of wetland and submerged aquatic vegetation. These coastal areas also offer the highest suitability for golden mussel, but our models suggest they are marginal habitats. Globally, the Great Lakes provide the closest match to the currently invaded range of killer shrimp, but they appear to pose an intermediate risk to the region. Range bagging provided reliable predictions when assessed either by a standard test set or by tests for spatial transferability, with golden mussel being the most difficult to accurately predict. Our approach illustrates the strength of combining multiple sources of data, while reiterating the need for increased measurement of freshwater habitat at high spatial resolutions to improve the ability to predict potential invasive species. [ABSTRACT FROM AUTHOR]

Detection of invasive species before or soon after they establish in novel environments is critical to prevent widespread ecological and economic impacts. Environmental DNA (eDNA) surveillance and monitoring is an approach to improve early detection efforts. Here we describe a large-scale conservation application of a quantitative polymerase chain reaction assay with a case study for surveillance of a federally listed nuisance species (Ruffe, Gymnocephalus cernua) in the Laurentian Great Lakes. Using current Ruffe distribution data and predictions of future Ruffe spread derived from a recently developed model of ballast-mediated dispersal in US waters of the Great Lakes, we designed an eDNA surveillance study to target Ruffe at the putative leading edge of the invasion. We report a much more advanced invasion front for Ruffe than has been indicated by conventional surveillance methods and we quantify rates of false negative detections (i.e. failure to detect DNA when it is present in a sample). Our results highlight the important role of eDNA surveillance as a sensitive tool to improve early detection efforts for aquatic invasive species and draw attention to the need for an improved understanding of detection errors. Based on axes that reflect the weight of eDNA evidence of species presence and the likelihood of secondary spread, we suggest a two-dimensional conceptual model that management agencies might find useful in considering responses to eDNA detections. [ABSTRACT FROM AUTHOR]

Risk analysis of species invasions links biology and economics, is increasingly mandated by international and national policies, and enables improved management of invasive species. Biological invasions proceed through a series of transition probabilities (i.e., introduction, establishment, spread, and impact), and each of these presents opportunities for management. Recent research advances have improved estimates of probability and associated uncertainty. Improvements have come from species-specific trait-based risk assessments (of estimates of introduction, establishment, spread, and impact probabilities, especially from pathways of commerce in living organisms), spatially explicit dispersal models (introduction and spread, especially from transportation pathways), and species distribution models (establishment, spread, and impact). Results of these forecasting models combined with improved and cheaper surveillance technologies and practices [e.g., environmental DNA (eDNA), drones, citizen science] enable more efficient management by focusing surveillance, prevention, eradication, and control efforts on the highest-risk species and locations. Bioeconomic models account for the interacting dynamics within and between ecological and economic systems, and allow decision makers to better understand the financial consequences of alternative management strategies. In general, recent research advances demonstrate that prevention is the policy with the greatest long-term net benefit. [ABSTRACT FROM AUTHOR]

In 2011, Lake Erie experienced the largest harmful algal bloom in its recorded history, with a peak intensity over three times greater than any previously observed bloom. Here we show that long-term trends in agricultural practices are consistent with increasing phosphorus loading to the western basin of the lake, and that these trends, coupled with meteorological conditions in spring 2011, produced record-breaking nutrient loads. An extended period of weak lake circulation then led to abnormally long residence times that incubated the bloom, and warm and quiescent conditions after bloom onset allowed algae to remain near the top of the water column and prevented flushing of nutrients from the system. We further find that all of these factors are consistent with expected future conditions. If a scientifically guided management plan to mitigate these impacts is not implemented, we can therefore expect this bloom to be a harbinger of future blooms in Lake Erie. [ABSTRACT FROM AUTHOR]

1. Hypoxia occurs seasonally in many stratified coastal marine and freshwater ecosystems when bottom dissolved oxygen (DO) concentrations are depleted below 2-3 mg O L. 2. We evaluated the effects of hypoxia on fish habitat quality in the central basin of Lake Erie from 1987 to 2005, using bioenergetic growth rate potential (GRP) as a proxy for habitat quality. We compared the effect of hypoxia on habitat quality of (i) rainbow smelt, Osmerus mordax mordax Mitchill (young-of-year, YOY, and adult), a cold-water planktivore, (ii) emerald shiner, Notropis atherinoides Rafinesque (adult), a warm-water planktivore, (iii) yellow perch, Perca flavescens Mitchill (YOY and adult), a cool-water benthopelagic omnivore and (iv) round goby Neogobius melanostomus Pallas (adult) a eurythermal benthivore. Annual thermal and DO profiles were generated from 1D thermal and DO hydrodynamics models developed for Lake Erie's central basin. 3. Hypoxia occurred annually, typically from mid-July to mid-October, which spatially and temporally overlaps with otherwise high benthic habitat quality. Hypoxia reduced the habitat quality across fish species and life stages, but the magnitude of the reduction varied both among and within species because of the differences in tolerance to low DO levels and warm-water temperatures. 4. Across years, trends in habitat quality mirrored trends in phosphorus concentration and water column oxygen demand in central Lake Erie. The per cent reduction in habitat quality owing to hypoxia was greatest for adult rainbow smelt and round goby (mean: −35%), followed by adult emerald shiner (mean: −12%), YOY rainbow smelt (mean: −10%) and YOY and adult yellow perch (mean: −8.5%). 5. Our results highlight the importance of differential spatiotemporally interactive effects of DO and temperature on relative fish habitat quality and quantity. These effects have the potential to influence the performance of individual fish species as well as population dynamics, trophic interactions and fish community structure. [ABSTRACT FROM AUTHOR]

This paper proposes a methodology for combining satellite images with advection-diffusion models for interpolation and prediction of environmental processes. We propose a dynamic state-space model and an ensemble Kalman filter and smoothing algorithm for on-line and retrospective state estimation. Our approach addresses the high dimensionality, measurement bias, and nonlinearities inherent in satellite data. We apply the method to a sequence of SeaWiFS satellite images in Lake Michigan from March 1998, when a large sediment plume was observed in the images following a major storm event. Using our approach, we combine the images with a sediment transport model to produce maps of sediment concentrations and uncertainties over space and time. We show that our approach improves out-of-sample RMSE by 20%-30% relative to standard approaches. This article has supplementary material online. [ABSTRACT FROM AUTHOR]

In this paper we develop and examine several schemes for combining daily images obtained from the Sea-viewing Wide Field Spectrometer (SeaWiFS) with a two-dimensional sediment transport model of Lake Michigan. We consider two data assimilation methods, direct insertion and a kriging-based approach, and perform a forecasting study focused on a 2-month period in spring 1998 when a large storm caused substantial amounts of sediment resuspension and horizontal sediment transport in the lake. By beginning with the simplest possible forecast method and sequentially adding complexity we are able to assess the improvements offered by combining the satellite data with the numerical model. In our application, we find that data assimilation schemes that include both the data and the lake dynamics improve forecast root mean square error by 40% over purely model-based approaches and by 20% over purely data-based approaches. [ABSTRACT FROM AUTHOR]

We present a method for assimilating current observations into a two-dimensional circulation model of Lake Michigan, based on the Princeton Ocean Model (POM) and driven by observed winds. Because measurements of surface level are not available, we require that the point-wise update to the forecast horizontal current does not change the forecast surface level. This requirement makes it possible to represent the current updates by a stream function. Given an appropriate covariance model of this stream function, the current updates are calculated by kriging interpolation using the observations and the corresponding model forecast. It is further required that the current updates do not create cross-shore flows; this is represented by the stream function being constant along the coastline and is enforced by incorporating pseudo coastal data into the interpolation. This eliminates the need to construct complex spatial covariance models for the stream function. The method also accommodates observational errors. Results show that the method successfully melds observations into the model, and the influence of data assimilation propagates in space and time. [ABSTRACT FROM AUTHOR]

A two-dimensional circulation model has been used to test the hypothesis of whether the observed low-frequency current variations in the central Gulf of Riga, Baltic Sea, can be explained by basin-scale topographic wave response. A comparison of two-dimensional model results with measurements from a single current meter in the gulf showed good correlation. More sophisticated three-dimensional barotropic and baroclinic models provided only marginal improvement over the two-dimensional model. The model results indicate that wind-driven flow over variable bottom topography is the dominant process during moderate and strong winds. The double-gyre circulation pattern resembles the gravest basin-scale topographic wave. The free topographic wave propagates cyclonically around the basin, but does not complete a full cycle because of the shallowness of the Gulf of Riga. The evolution of the topographic wave under realistic wind conditions is analyzed using vorticity dynamics in a basin-scale sense. The topographic wave is reinforced by cyclonically rotating wind and can be destroyed most effectively by anticyclonically rotating wind. The topographic wave signature is more apparent in deep water and almost absent in shallow areas of the basin. During calm periods or under the influence of weak winds, the double-gyre circulation will evolve into predominantly cyclonic circulation. [ABSTRACT FROM AUTHOR]