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Great Lakes
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- 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:
- 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:
- 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
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- Creator:
- Yuan, Ye, Currie, William S., Sharp, Sean J. , Martina, Jason P. , and Elgersma, Kenneth J.
- Description:
- This archived dataset includes all of the input files that were used to run the model for all the runs in this set, including files containing model parameters and drivers. This dataset also includes all of the model output files from model runs in this set.
- Keyword:
- wetland, Great Lakes, greenhouse gas, climate change, and ecosystem model
- Citation to related publication:
- Yuan, Y., S. J. Sharp, J. P. Martina, K. J. Elgersma, and W. S. Currie. Sustained-flux global warming potential driven by nitrogen inflow and hydroperiod in a model of Great Lakes coastal wetlands. JGR Biogeosciences in review.
- 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: drewgron@umich.edu
- 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:
- Ayumi Fujisaki-Manome
- Description:
- Precipitation impacts on ice cover and water temperature in the Laurentian Great Lakes were examined using state-of-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 over-lake 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.
- Keyword:
- Great Lakes, lake ice, numerical modeling, and precipitation
- Citation to related publication:
- Fujisaki-Manome, A., Anderson, E. J., Kessler, J. A., Chu, P. Y., Wang, J., & Gronewold, A. D. (2020). Simulating Impacts of Precipitation on Ice Cover and Surface Water Temperature Across Large Lakes. Journal of Geophysical Research: Oceans, 125(5), e2019JC015950. https://doi.org/10.1029/2019JC015950
- Discipline:
- Science and Engineering
-
- 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
-
- 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
-
- 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, University of Michigan
- 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, 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
-
- Creator:
- Mason, Lacey A., Riseng, Catherine M., Layman, Andrew J., and Jensen, Robert
- Description:
- Wind exposure is a key physical driver of coastal systems in aquatic environments influencing circulation and wave dynamics. A measure of wind exposure is fetch, the distance over which wind can travel across open water. In large lake systems, such as the Laurentian Great Lakes, estimating fetch has proved to be difficult due to their vast size and complex topobathymetry. Here we describe the development of two spatially discrete indicators of exposure to provide a more accurate indicator of influence of wind exposure in the nearshore of the Laurentian Great Lakes. We summarized wind data from offshore buoys and leveraged existing tools to calculate effective fetch and relative exposure index (effective fetch scaled by mean wind speed) at a 30 m grid cell resolution. We validated these models by comparing our exposure maps to the U.S. Army Corps of Engineers Wave Information Studies models and found general agreement. These exposure maps are available for public download for the years 2004-2014.
- Keyword:
- GLAHF, Great Lakes, waves, fetch, REI, wind exposure, and geospatial
- Discipline:
- Science