Investigating Uncertainty Associated with the Great Lakes Water Balance

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Funding Opportunity ID: 327515
Opportunity Number: W81EWF-20-SOI-0028
Opportunity Title: Investigating Uncertainty Associated with the Great Lakes Water Balance
Opportunity Category: Discretionary
Opportunity Category Explanation:
Funding Instrument Type: Cooperative Agreement
Category of Funding Activity: Science and Technology and other Research and Development
Category Explanation:
CFDA Number(s): 12.630
Eligible Applicants: Others (see text field entitled “Additional Information on Eligibility” for clarification)
Additional Information on Eligibility: This opportunity is restricted to non-federal partners of the Great Lakes-Northern Forests Cooperative Ecosystems Studies Unit (CESU).
Agency Code: DOD-COE
Agency Name: Department of Defense
Dept. of the Army — Corps of Engineers
Posted Date: Jun 04, 2020
Close Date: Jul 31, 2020
Last Updated Date: Jun 04, 2020
Award Ceiling: $85,000
Award Floor: $0
Estimated Total Program Funding: $325,000
Expected Number of Awards: 1
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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