Application of LANDSAT to the Surveillance and Control of Lake Eutrophication in the Great Lakes Basin
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Rogers, R. H.↗
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Application of LANDSAT to the surveillance and control of lake eutrophication in the Great Lakes basin
The author has identified the following significant results. Computer techniques were developed for mapping water quality parameters from LANDSAT data, using surface samples collected in an ongoing survey of water quality in Saginaw Bay. Chemical and biological parameters were measured on 31 July 1975 at 16 bay stations in concert with the LANDSAT overflight. Application of stepwise linear regression bands to nine of these parameters and corresponding LANDSAT measurements for bands 4 and 5 only resulted in regression correlation coefficients that varied from 0.94 for temperature to 0.73 for Secchi depth. Regression equations expressed with the pair of bands 4 and 5, rather than the ratio band 4/band 5, provided higher correlation coefficients for all the water quality parameters studied (temperature, Secchi depth, chloride, conductivity, total kjeldahl nitrogen, total phosphorus, chlorophyll a, total solids, and suspended solids).
Rogers, R. H.↗
Application of LANDSAT to the surveillance of lake eutrophication in the Great Lakes basin
The author has identified the following significant results. A step-by-step procedure for establishing and monitoring the trophic status of inland lakes with the use of LANDSAT data, surface sampling, laboratory analysis, and aerial observations were demonstrated. The biomass was related to chlorophyll-a concentrations, water clarity, and trophic state. A procedure was developed for using surface sampling, LANDSAT data, and linear regression equations to produce a color-coded image of large lakes showing the distribution and concentrations of water quality parameters, causing eutrophication as well as parameters which indicate its effects. Cover categories readily derived from LANDSAT were those for which loading rates were available and were known to have major effects on the quality and quantity of runoff and lake eutrophication. Urban, barren land, cropland, grassland, forest, wetlands, and water were included.
Rogers, R. H.↗
Cold Season Performance of the NU-WRF Regional Climate Model in the Great Lakes Region
As Earth’s largest collection of freshwater, the Laurentian Great Lakes have enormous ecological and socio-economic value. Their basin has become a regional hotspot of climatic and limnological change, potentially threatening its vital natural resources. Consequentially, there is a need to assess the current state of climate models regarding their performance across the Great Lakes region and develop the next generation of high-resolution regional climate models to address complex limnological processes and lake-atmosphere interactions. In response to this need, the current paper focuses on the generation and analysis of a 20-member ensemble of 3-km National Aeronautics and Space Administration (NASA)-Unified Weather Research and Forecasting (NU-WRF) simulations for the 2014-2015 cold season. The study aims to identify the model’s strengths and weaknesses; optimal configuration for the region; and the impacts of different physics parameterizations, coupling to a 1D lake model, time-variant lake-surface temperatures, and spectral nudging. Several key biases are identified in the cold-season simulations for the Great Lakes region, including an atmospheric cold bias that is amplified by coupling to a 1D lake model but diminished by applying the Community Atmosphere Model radiation scheme and Morrison microphysics scheme; an excess precipitation bias; anomalously early initiation of fall lake turnover and subsequent cold lake bias; excessive and overly persistent lake ice cover; and insufficient evaporation over Lakes Superior and Huron. The research team is currently addressing these key limitations by coupling NU-WRF to a 3D lake model in support of the next generation of regional climate models for the critical Great Lakes Basin.
Michael Notaro↗
The NASA Short-Term Prediction Research and Transition (SPoRT) Center: Opportunities for Collaboration in the Great Lakes Region
The presentation slides include: The SPoRT Center, History and Future of SPoRT, Great Lakes Applications, Great Lakes Forecasting Issues, Applications to the WRF-EMS, Precipitation Science, Lake Effect Precipitation, Sensitivity to Microphysics, Exploring New Schemes, Opportunities for Collaboration, and SPoRT Research and Development.
Molthan, Andrew L.↗
Great Lakes Wind Energy Challenges and Opportunities Assessment
Many issues associated with wind development in the Great Lakes will require solutions different from those developed for offshore wind in ocean states and may not fully benefit from the industry learnings of nearby states. As a result, technology readiness and cost reduction for Great Lakes Wind (GLW) energy generation is likely to be delayed relative to other regions without a substantial, targeted GLW research campaign, and proactive stakeholder engagement in the region at all levels. Failure to conduct the necessary research to lower GLW costs in the near term could limit its contribution to the Nation's decarbonization goals by 2035, and could potentially raise long term energy prices in Great Lakes states if demand for renewable energy continues to accelerate. The overall objective of a research program such as that described in this report would be to enable the realization of commercial GLW before 2035. With the aim of ensuring that prospective development of GLW is conducted efficiently, safely, and coordinated in the best interests of the local residents and stakeholders, the U.S. Department of Energy (DOE) Wind Energy Technologies Office (WETO) tasked the National Renewable Energy Laboratory (NREL) to assist in (a) developing an improved understanding of offshore wind power's development potential in the Great Lakes, (b) identifying the key issues that need to be resolved for this potential to be achieved, and (c) defining a comprehensive research program to address and resolve these issues. This report presents the results of NREL's effort to address these needs.
cost modeling↗
Monthly Mean In Situ Surface Flux Observations Paired with Satellite-Derived and Reanalysis-Based Flux Data for the Great Lakes Region, 2001–2020
Surface radiative and turbulent heat fluxes over the Great Lakes strongly influence regional hydrological and meteorological processes, and their accurate representation is critical for numerical weather prediction and coupled atmosphere–lake modeling. However, direct flux observations are spatially sparse across the region, so gridded reanalysis and satellite-derived products are often used for climatological analyses and model evaluation despite differences in their flux representations. This dataset provides processed, quality-controlled, monthly mean surface flux observations from the Great Lakes Evaporation Network (GLEN), AmeriFlux, and the National Data Buoy Center, paired with spatiotemporally matched flux estimates from two reanalysis products, the fifth generation European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis dataset (ERA5) and the Modern Era Reanalysis for Research and Applications, version 2 (MERRA-2), and two satellite-derived products, the Clouds and Earth's Radiant Energy Systems Energy Balanced and Filled (CERES-EBAF) and the Cloud, Albedo and Surface Radiation dataset from AVHRR data - Edition 3 (CLARA-A3). The dataset includes sixteen observational stations with variable temporal coverage within 2001–2020. For each station, a CSV file contains monthly time series of available flux variables, including surface downwelling shortwave radiation (SW), surface downwelling longwave radiation (LW), sensible heat (SH) flux, and latent heat flux (LH), alongside matched gridded product values where available. Columns in the CSV file correspond to different variables sourced from each dataset, with column titles structured as "{dataset}_{variable}". Columns with relevant metadata are also provided in each CSV file, including station latitude and longitude, monthly timestamps, and the name of the sourced observational data. These files are structured for direct use in common analysis tools, including Microsoft Excel, Python pandas, and Python matplotlib. This dataset supports climatological analysis of the Great Lakes regional surface energy budget, evaluation of satellite-derived and reanalysis-based flux products, and development or validation of flux representations in numerical weather prediction and coupled atmosphere–lake models.
Great Lakes↗
Phase 2 development of Great Lakes algorithms for Nimbus-7 coastal zone color scanner
A series of experiments have been conducted in the Great Lakes designed to evaluate the application of the NIMBUS-7 Coastal Zone Color Scanner (CZCS). Atmospheric and water optical models were used to relate surface and subsurface measurements to satellite measured radiances. Absorption and scattering measurements were reduced to obtain a preliminary optical model for the Great Lakes. Algorithms were developed for geometric correction, correction for Rayleigh and aerosol path radiance, and prediction of chlorophyll-a pigment and suspended mineral concentrations. The atmospheric algorithm developed compared favorably with existing algorithms and was the only algorithm found to adequately predict the radiance variations in the 670 nm band. The atmospheric correction algorithm developed was designed to extract needed algorithm parameters from the CZCS radiance values. The Gordon/NOAA ocean algorithms could not be demonstrated to work for Great Lakes waters. Predicted values of chlorophyll-a concentration compared favorably with expected and measured data for several areas of the Great Lakes.
Tanis, Fred J.↗
Leveraging Google Earth Engine User Interface for Semiautomated Wetland Classification in the Great Lakes Basin at 10 m With Optical and Radar Geospatial Datasets
As one of the world’s largest freshwater ecosystems,the Great Lakes Basin houses hundreds of thousands of acres of wetlands that support a variety of crucial ecological and environmental functions at the local, regional, and global levels.Monitoring these wetlands is critical to conservation and restoration efforts, however current methods that rely on field monitoring are labor-intensive, costly, and often outdated. In this study, we present a graphical user interface constructed in Google Earth Engine called the Wetland Extent Tool (WET),which allows semi-automatic wetland classification according to a user-input area of interest and date range. WET composites datasets and conducts multi source, moderate resolution processing utilizing Landsat 8 OLI, Sentinel-2 MSI, Sentinel-1 C-SAR, and Shuttle Radar Topography Mission (SRTM) datasets to classify wetlands in the entire Great Lakes Basin. We evaluated classification results of wetlands, uplands, and open water from May-September 2019, and tested whether SRTM elevation, slope,or the Dynamic Surface Water Extent produced the most accurate results in each Great Lake Basin in conjunction with optical indices and radar composites. We found that elevation produced the most accurate classification in Lake Erie, Michigan,and Ontario, while slope performed best in Lake Huron and Superior. Lake Erie, Michigan, Ontario, and Huron achieved high overall accuracy and identification of wetlands. WET leverages cloud-computing for multi source processing of moderate resolution remote sensing data, and employs a user interface in Google Earth Engine that wetland managers and conservationists can use to monitor wetland extent in the Great Lakes Basin in near real-time.
Vanessa L Valenti↗
Shipborne Scatterometer Measurements of Great Lakes Ice
The Jet Propulsion Laboratory C-band polarimetric scatterometer was used to measure radar backscatter of Great Lakes ice during the 1997 Great Lakes winter experiments.
Great Lakes C-band ENVISAT RADARSAT & RADARSAT-II ↗
Development of Great Lakes algorithms for the Nimbus-G coastal zone color scanner
A series of experiments in the Great Lakes designed to evaluate the application of the Nimbus G satellite Coastal Zone Color Scanner (CZCS) were conducted. Absorption and scattering measurement data were reduced to obtain a preliminary optical model for the Great Lakes. Available optical models were used in turn to calculate subsurface reflectances for expected concentrations of chlorophyll-a pigment and suspended minerals. Multiple nonlinear regression techniques were used to derive CZCS water quality prediction equations from Great Lakes simulation data. An existing atmospheric model was combined with a water model to provide the necessary simulation data for evaluation of the preliminary CZCS algorithms. A CZCS scanner model was developed which accounts for image distorting scanner and satellite motions. This model was used in turn to generate mapping polynomials that define the transformation from the original image to one configured in a polyconic projection. Four computer programs (FORTRAN IV) for image transformation are presented.
Tanis, F. J.↗
An operational all-weather Great Lakes ice information system
A description is given of the NASA developed all-weather ice information system for the Great Lakes winter navigation program. The system utilizes an X-band side looking airborne radar (SLAR) for determining type, location, and areal distribution of the ice cover in the Great Lakes and an airborne, S band, down looking short pulse radar for obtaining ice thickness. Digitized SLAR data are relayed in real time via the NOAA-GOES satellite in geosynchronous orbit. The SLAR images along with hand drawn interpretative ice charts for various Great Lakes winter shipping areas are broadcast to facsimile recorders aboard vessles is the area via the MARAD marine VHF-FM radio network. These data assist such vessels in navigating both through and around the ice.
Gedney, R. T.↗
Quantitative interpretation of Great Lakes remote sensing data
The paper discusses the quantitative interpretation of Great Lakes remote sensing water quality data. Remote sensing using color information must take into account (1) the existence of many different organic and inorganic species throughout the Great Lakes, (2) the occurrence of a mixture of species in most locations, and (3) spatial variations in types and concentration of species. The radiative transfer model provides a potential method for an orderly analysis of remote sensing data and a physical basis for developing quantitative algorithms. Predictions and field measurements of volume reflectances are presented which show the advantage of using a radiative transfer model. Spectral absorptance and backscattering coefficients for two inorganic sediments are reported.
Shook, D. F.↗
Expanding Collaborative Capacity to Address Climate Resiliency in the Great Lakes Region
U.S. Department of Energy (DOE), Office of Science, Award DE-SC0023215, Expanding Collaborative Capacity to Address Climate Resiliency in the Great Lakes Region, supported capacity and partnership building between researchers at Central Michigan University (CMU) and scientists at DOE laboratories. The work involved identifying potential collaborations both internal to CMU and between CMU researchers and DOE staff, developing collaborative research proposals, and partnering on pilot-scale research projects focused on enhancing resilience of Great Lakes communities, ecosystems, and infrastructure to global change and environmental stressors. DOE funds supported travel, time to develop collaborations, investigate new research pathways, and draft grant proposals, and a loan of DOE equipment to collect pilot data in support of future work. The project achieved its goal of strengthening research collaborations between interdisciplinary researchers at CMU and establishing new collaborations and partnerships between CMU and DOE scientists. These collaborations resulted in 5 pre-applications and 4 full applications for funding to DOE BER Funding Opportunities, a successfully funded DOE BER award (DE-SC0025260), a new pilot project that leveraged DOE resources (AquaBOT) to improve understanding of water quality conditions in Michigan rivers, and two presentations of preliminary results at national scientific conferences. This project’s outcomes will benefit the Great Lakes Region via new lines of collaborative research focused on improving the resilience of Michigan communities, ecosystems, and infrastructure.
58 GEOSCIENCES↗
Great Lakes all-weather ice information system
A system is described which utilizes an X-band Side-Looking-Airborne-Radar (SLAR) for determining type, location, and aerial distribution of the ice cover in the Great Lakes and an airborne, S-band, short pulse radar for obtaining ice thickness. The SLAR system is currently mounted aboard a U.S. Coast Guard C-130B aircraft. Digitized SLAR data are relayed in real-time via the NOAA-GOES-1 satellite in geosynchronous orbit to the U.S. Coast Guard Ice Center in Cleveland, Ohio. SLAR images along with hand-drawn interpretative ice charts for various winter shipping areas in the Great Lakes are broadcast to facsimile recorders aboard Great Lakes vessels. The operational aspects of this ice information system are being demonstrated by NASA, U.S. Coast Guard, and NOAA/National Weather Service. Results from the 1974-75 winter season demonstrated the ability of this system to provide all-weather ice information to shippers in a timely manner.
Schertler, R. J.↗
Marine Fuel Innovation in the Great Lakes and St Lawrence Seaway
NLR collaborated with five Great Lakes ports to conduct analysis on potential risks and opportunities for making additional fuels available at their ports. The project included: Identifying existing and anticipated fuel production within the region; examining current port infrastructure and activities; exploring potential siting feasibility of additional energy infrastructure based on available area; introducing the Alternative Energy for Ports Explorer (APEX) tool to help ports understand existing energy resources and infrastructure. This presentation summarizes the work we did and our key findings.
29 ENERGY PLANNING, POLICY, AND ECONOMY↗
Evaluation of the TOPEX/Poseidon Altimeter System Over the Great Lakes
The TOPEX/POSEIDON altimeter measurement system is evaluated over the Great Lakes. Using in situ lake level measurements, the temporal variations in lake level are removed from the altimeter measurements, thus permitting the performance of the altimeter system to be assessed.
TOPEX/POSEIDON↗
The use of ERTS-1 imagery in air pollution and mesometeorological studies around the Great Lakes
ERTS-1 images continue to be highly useful in studies of: (1) long range transport of air pollutants over the Great Lakes; (2) the mesoscale atmospheric dynamics associated with episodic levels of photochemical smog along the western shore of Lake Michigan; and (3) inadvertant weather modification by large industrial complexes. Also unusual wave patterns in fogs and low stratus over the Great Lakes are being detected for the first time due to the satellites high resolution.
Lyons, W. A.↗