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Investigating Uncertainty Associated with the Great Lakes Water Balance

Sector: Water Supply and Storage • Location: United States of America

Source: Grants.gov

Project
Archived

The proposed project would focus on conducting research related to exploring alternative approaches for incorporating various models of Great Lakes connecting channel flows, evaporation and run-off into the large lake statistical water balance model (link below). In some instances, the government will be responsible for generating associated models for uncertainty analysis in the large lake statis

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The project “Investigating Uncertainty Associated with the Great Lakes Water Balance” is an infrastructure initiative in the Water Supply and Storage sector, located in United States of America. Taiyo aggregates data on it from Grants.gov.

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Description

Description

The proposed project would focus on conducting research related to exploring alternative approaches for incorporating various models of Great Lakes connecting channel flows, evaporation and run-off into the large lake statistical water balance model (link below). In some instances, the government will be responsible for generating associated models for uncertainty analysis in the large lake statistical water balance model. The government will provide a team member and fulfill required, parallel tasks in order to advance this research. The water balance model is open source and has been used to investigate evaporation and channel discharge as case studies in the Great Lakes. The USACE would like to expand some of these case studies and initiate new studies. https://deepblue.lib.umich.edu/data/concern/data_sets/2514nk609?locale=en The initial phase for this project will focus on quantifying variability and uncertainty over time in Detroit River and St. Clair River discharge estimates. Extend the case study of the Detroit River by Quinn, Clites and Gronewold (2020) through 2019. Expand above case study to the St. Clair River through 2019. Encode conventional SFD models into L2SWBM using prior parameter distributions from external independent regression analysis (developed and supplied by the government)

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High

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100%

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