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Filtering by: Creator Smith, Joeseph P. Keyword Machine learning Discipline Science

Estimates of the water balance of the Laurentian Great Lakes using the Large Lakes Statistical Water Balance Model (L2SWBM)
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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

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Estimates of the water balance of the Laurentian Great Lakes using the Large Lakes Statistical Water Balance Model (L2SWBM)

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Estimates of the water balance of the Laurentian Great Lakes using the Large Lakes Statistical Water Balance Model (L2SWBM)

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