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Filtering by: Creator Fry, Lauren M. Remove constraint Creator: Fry, Lauren M. Discipline Engineering Remove constraint Discipline: Engineering

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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
    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

  • A Daily Hydro-Meteorological Dataset for The World’s Largest System of Lakes

    work
    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