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- Creator:
- Howard, Cecilia M., Velazquez, Diana, Rico, Kathryn I., and Sheldon, Nathan D.
- Description:
- This study combines a field survey time series with analysis of remotely and locally sensed environmental and climate data. Field survey data consists of sediment chemistry from hand-push cores, and includes %Corg, %N, δ13Corg, Corg:N, collection month and year, and depth in sediment. Climate and environmental data for the region around Middle Island Sinkhole was pulled from publicly available NOAA databases (ERDDAP, National Data Buoy Center, NWS) for as much of the same time period as the sediment data as was available. These data included general weather information from the NDBC and NWS (air temperature, wind speed, wind direction, gust speed, monthly precipitation totals), as well as satellite-derived environmental data from a 0.25° area centered on MIS (ice cover, lake surface temperature, CDOM, DOC, Chlorophyll, suspended minerals). Data were processed to monthly and annual averages as described below in order to compare to sinkhole sediment chemistry. Abstract: Records of recent past climate provide an essential window into understanding how changing climate influences environments and ecosystems such as lakes. Sediment carbon and nitrogen chemistry can offer insight into productivity and biochemistry, and anoxic sediments can often preserve short-term changes in these signals. We found that seasonal and annual changes in local ice season, chlorophyll, and precipitation influenced the amount and isotopic composition of carbon reaching the sediments of Middle Island Sinkhole, an anoxic sinkhole in Lake Huron. Carbon and nitrogen signals reflected the year or season of sample collection in sediments as deep as 12 cm. Our findings demonstrate that declining ice cover in this part of the Great Lakes is leading to increased export of organic carbon into sediments, but that in situ sediment processes may make teasing out short-term changes from sediment cores difficult even in an anoxic setting.
- Keyword:
- Carbon burial, Great Lakes, Ice cover, Sediment carbon, Sediment nitrogen, Anoxia, and Geosciences
- Citation to related publication:
- Howard, C.M., et al. (in prep). Climate-driven changes in sediment carbon and nitrogen of an anoxic Lake Huron sinkhole.
- Discipline:
- Science
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- Creator:
- King, Katelyn, Fujisaki-Manome, Ayumi, Brant, Cory, and Alofs, Karen
- Description:
- Ice cover on the Great Lakes plays an important role in regional climate, supports tourism and recreation, and provides ecological habitat. As the climate warms, ice cover in the Great Lakes is expected to decline, which in turn will create more lake effect precipitation, reduce ice cover for recreation, and alter habitat for fishes. Therefore, it is important to understand historical ice patterns to better understand and predict future ice cover on the lakes. However, Great Lakes ice cover data prior to 1973 is scarce, due to the limited routine satellite observations. Our dataset aims to fill this gap by providing historical spatial ice duration layers to be used for modeling species distributions. and ArcGIS Pro ( https://www.esri.com/en-us/arcgis/products/arcgis-pro/overview), QGIS ( https://qgis.org/) or other spatial data software will be required to view this dataset.
- Keyword:
- ice, Great Lakes, Superior, Michigan, Ontario, Erie, Huron, and historical
- Citation to related publication:
- King, K., Fujisaki-Manome, A., Brant, C., Cohn, D., Peng, I., Alofs, K., Reconstructing Great Lakes air temperature and ice dynamics data back to 1897. Under Review
- Discipline:
- Science
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Great Lakes Historical Ice Dynamics
User Collection
- Creator:
- King, Katelyn B., Fujisaki-Manome, Ayumi, Brant, Cory, and Alofs, Karen
- Description:
- This collection includes raster layers (as a geodatabase) with ice metrics for each of the Great Lakes representing the historical time period of 1898-1960. One metric is spatial mean ice duration (the number of days per year when the lake was frozen) and the other is coefficient of variation of ice duration (variability across years).
- Keyword:
- Great Lakes, Ice , Ice duration, Ice variability, Historical , Huron, Ontario, Erie, Michigan, and Superior
- Discipline:
- Science
1Works -
- Creator:
- Jones, Kaylin, Fernández Correa, Mariana I., Malherbe, Julien, and Cotel, Aline J.
- Description:
- Sea lampreys (Petromyzon marinus) are an invasive species of concern in the Laurentian Great Lakes. Their predation on native species has degraded the ecological and economic health of the region. They are thus targeted for removal through various means, including trapping. Currently, sea lamprey traps are somewhat inefficient, believed to be partially due to their “entrance flows”, or the flow patterns induced by these traps that are felt by approaching sea lampreys. This study experimentally quantifies these flows. Models of two common sea lamprey trap designs were built and installed in a water tunnel in the University of Michigan Hydraulics Lab, and attraction flows were measured using Particle Image Velocimetry (PIV) with minimal background turbulence. Velocity, velocity gradient, and vorticity distributions in the flow are evaluated from the PIV data. These same models were installed in larger-scale raceways at USGS’ Hammond Bay Biological Station, and attraction flows were again measured using PIV to explore how these patterns change in a more turbulent environment that better mimics natural conditions. and Hammond Bay Biological Station (HBBS) is a research center that aims to develop control measures for sea lampreys and conduct research to aid native fish restoration. HBBS is a field station of the USGS Great Lakes Science Center (GLSC) managed by the Great Lakes Fishery Commission (GLFC). More information on HBBS can be found here: https://www.usgs.gov/centers/great-lakes-science-center/science/hammond-bay-biological-station.
- Keyword:
- Entrance Flows, sea lamprey, Great Lakes, hydrodynamics, invasive species, particle image velocimetry, sea lamprey, sea lamprey traps, and turbulence
- Citation to related publication:
- Jones, Kaylin, et. al. 2024. Investigating entrance hydrodynamics of sea lamprey traps. Canadian Journal of Fisheries and Aquatic Sciences. XX(X): XXX-XXX. https://doi.org/XX.XXXX/cjfas-XXXX.
- Discipline:
- Engineering
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- Creator:
- Lehman, John T
- Description:
- The information reported in this dataset was obtained through research grants from the Michigan Sea Grant Program. The research was undertaken to study trace metal limitation of algal growth in Saginaw Bay, Lake Huron from 1999 to 2003.
- Keyword:
- algae, nutrients, trace metals, and Great Lakes
- Citation to related publication:
- Bazzi, A., Lehman, J.T., Nriagu, J.O., Hollandsworth, D., Irish, N. and Nosher. T. 2002. Chemical speciation of dissolved copper in Saginaw Bay, Lake Huron with Square Wave Anodic Stripping Voltammetry. Journal of Great Lakes Research 28: 466-478. and Lehman, J.T., Bazzi, A., Nosher, T. and Nriagu J.O. 2004. Copper inhibition of algal growth in Saginaw Bay – Lake Huron. Canadian Journal of Fisheries and Aquatic Sciences 61: 1871-1880.
- Discipline:
- Science
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Estimates of the water balance of the Laurentian Great Lakes using the Large Lakes Statistical Water Balance Model (L2SWBM)
User Collection
- Creator:
- Smith, Joeseph P., Fry, Lauren M., Do, Hong X., and Gronewold, Andrew D.
- Description:
- This collection contains estimates of the water balance of the Laurentian Great Lakes that were produced by the Large Lakes Statistical Water Balance Model (L2SWBM). Each data set has a different configuration and was used as the supplementary for a published peer-reviewed article (see "Citations to related material" section in the metadata of individual data sets). The key variables that were estimated by the L2SWBM are (1) over-lake precipitation, (2) over-lake evaporation, (3) lateral runoff, (4) connecting-channel outflows, (5) diversions, and (6) predictive changes in lake storage. and Contact: Andrew Gronewold Office: 4040 Dana Phone: (734) 764-6286 Email: [email protected]
- Keyword:
- Great Lakes water levels, statistical inference, water balance, data assimilation, Great Lakes, Laurentian, Machine learning, Bayesian, and Network
- Citation to related publication:
- Smith, J. P., & Gronewold, A. D. (2017). Development and analysis of a Bayesian water balance model for large lake systems. arXiv preprint arXiv:1710.10161., Gronewold, A. D., Smith, J. P., Read, L., & Crooks, J. L. (2020). Reconciling the water balance of large lake systems. Advances in Water Resources, 103505., and Do, H.X., Smith, J., Fry, L.M., and Gronewold, A.D., Seventy-year long record of monthly water balance estimates for Earth’s largest lake system (under revision)
- Discipline:
- Science and Engineering
5Works -
- Creator:
- Hong, Yi, Fry, Lauren M., Orendorf, Sophie, Ward, Jamie L., Mroczka, Bryan, Wright, David, and Gronewold, Andrew
- Description:
- Accurate estimation of hydro-meteorological variables is essential for adaptive water management in the North American Laurentian Great Lakes. However, only a limited number of monthly datasets are available nowadays that encompass all components of net basin supply (NBS), such as over-lake precipitation (P), evaporation (E), and total runoff (R). To address this gap, we developed a daily hydro-meteorological dataset covering an extended period from 1979 to 2022 for each of the Great Lakes. The daily P and E were derived from six global gridded reanalysis climate datasets (GGRCD) that include both P and E estimates, and R was calculated from National Water Model (NWM) simulations. Ensemble mean values of the difference between P and E (P – E) and NBS were obtained by analyzing daily P, E, and R. Monthly averaged values derived from our new daily dataset were validated against existing monthly datasets. This daily hydro-meteorological dataset has the potential to serve as a validation resource for current data and analysis of individual NBS components. Additionally, it could offer a comprehensive depiction of weather and hydrological processes in the Great Lakes region, including the ability to record extreme events, facilitate enhanced seasonal analysis, and support hydrologic model development and calibration. The source code and data representation/analysis figures are also made available in the data repository.
- Keyword:
- Great Lakes, Hydrometeorological, National Water Model, Daily, Overlake precipitation, Overlake evaporation, Total runoff, Net Basin Supply, and Water Balance
- Discipline:
- Science and Engineering
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- Creator:
- Fuller, Jennifer, Rowan, Erin, Landgraf, Ava, Alofs, Karen, Foufopoulos, Johannes, and Gronewold, Andrew
- Description:
- This study investigates the rapid decline of black tern (Chlidonias niger) over eight years in one of Michigan’s largest colonies, Lake St. Clair. 1. Nesting Success Model: A multiple logistic regression with a binomial (logit-link) fit using the glm() function from the ‘stats’ package in R (55) to determine the influence of habitat and biological predictors on nesting survival. 2. ArcMap visualization of Nesting Success: To visualize the geographic extent of the habitat’s potential to predispose nests’ vulnerability, the coefficients and intercept from our selected GLM were applied to raster layers in ArcMap using the Raster Calculator Tool. 3. Population Change & Habitat Extent: To quantify sub-colony breeding pair population size and their response to changes in sub-colony habitat in the geospatial model, we applied a general linear mixed model (GLMM) using the lmer() function from the ‘lme4’ package in R (55). Predictor variables were chosen a priori, and included the area of open water, uninhabitable vegetation (NDVI>0.72), any habitable area, and area with >50% hatch success.
- Keyword:
- black terns, wetlands, ecology, climate change, lake levels, Great Lakes, waterbirds, ornithology, general linear model, geospatial, GIS, nesting success, avian, and breeding
- Citation to related publication:
- Fuller, J., Rowan, E., Landgraf, A., Alofs, K., Foufopoulos, J., Gronewold, A., (2021). Collapse of a Black Tern Colony (Chlidonias niger) as a Result of Climate Change Driven Lake-Level Extremes and Anthropogenic Habitat Alteration [Data set]. University of Michigan - Deep Blue. and Fuller, J., et al. (2021). Shorebird colony collapses under climate driven lake-level rise and anthropogenic stressors. Forthcoming.
- Discipline:
- Science
-
- Creator:
- Minallah, Samar and Steiner, Allison L.
- Description:
- Data format: netcdf4 , Time series duration: 2016-06-01 to 2020-10-31, Temporal resolution: Daily, and Spatial resolution: The model output was regridded to a 0.05 degree rectilinear (lat/lon) grid using the conservative remapping method ("cdo remapcon" tool).
- Keyword:
- Land surface hydrology, Great Lakes, Land surface model, NOAH-MP, WRF-Hydro, and Hydrologic modeling
- Citation to related publication:
- Minallah, S. (2022). A Study on the Atmospheric, Cryospheric, and Hydrologic Processes Governing the Evolution of Regional Hydroclimates (Doctoral dissertation, University of Michigan Ann Arbor). https://dx.doi.org/10.7302/6223
- Discipline:
- Science and Engineering
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- Creator:
- Smith, Joeseph P., Gronewold, Andrew D., Read, Laura, Crooks, James L., School for Environment and Sustainability, University of Michigan, Department of Civil and Environmental Engineering, University of Michigan, and Cooperative Institute for Great Lakes Research
- Description:
- Using the statistical programming package R ( https://cran.r-project.org/), and JAGS (Just Another Gibbs Sampler, http://mcmc-jags.sourceforge.net/), we processed multiple estimates of the Laurentian Great Lakes water balance components -- over-lake precipitation, evaporation, lateral tributary runoff, connecting channel flows, and diversions -- feeding them into prior distributions (using data from 1950 through 1979), and likelihood functions. The Bayesian Network is coded in the BUGS language. Water balance computations assume that monthly change in storage for a given lake is the difference between beginning of month water levels surrounding each month. For example, the change in storage for June 2015 is the difference between the beginning of month water level for July 2015 and that for June 2015., More details on the model can be found in the following summary report for the International Watersheds Initiative of the International Joint Commission, where the model was used to generate a new water balance historical record from 1950 through 2015: https://www.glerl.noaa.gov/pubs/fulltext/2018/20180021.pdf. Large Lake Statistical Water Balance Model (L2SWBM): https://www.glerl.noaa.gov/data/WaterBalanceModel/, and This data set has a shorter timespan to accommodate a prior which uses data not used in the likelihood functions.
- Keyword:
- Water, Balance, Great Lakes, Laurentian, Machine, Learning, Lakes, Bayesian, and Network
- Citation to related publication:
- Smith, J., Gronewald, A. et al. Summary Report: Development of the Large Lake Statistical Water Balance Model for Constructing a New Historical Record of the Great Lakes Water Balance. Submitted to: The International Watersheds Initiative of the International Joint Commission. Accessible at https://www.glerl.noaa.gov/pubs/fulltext/2018/20180021.pdf, Large Lake Statistical Water Balance Model (L2SWBM). https://www.glerl.noaa.gov/data/WaterBalanceModel/, and Gronewold, A.D., Smith, J.P., Read, L. and Crooks, J.L., 2020. Reconciling the water balance of large lake systems. Advances in Water Resources, p.103505.
- Discipline:
- Science and Engineering