Investigating Uncertainty Associated with the Great Lakes Water Balance | W81EWF 20 SOI 0028

Opportunity Information: Apply for W81EWF 20 SOI 0028

This grant opportunity, titled "Investigating Uncertainty Associated with the Great Lakes Water Balance" (Funding Opportunity Number W81EWF 20 SOI 0028), is a Department of Defense, U.S. Army Corps of Engineers (USACE) research effort focused on improving how uncertainty is represented in Great Lakes water balance accounting. The project is set up as a discretionary cooperative agreement, meaning the recipient is expected to work collaboratively with the government rather than operate fully independently. The total anticipated funding is up to $85,000, with one award expected. The opportunity was posted June 4, 2020, with an original closing date of July 31, 2020, and falls under CFDA 12.630 within the Science and Technology / Research and Development activity category.

At its core, the work centers on the Large Lake Statistical Water Balance Model (often abbreviated as L2SWBM), an open-source statistical modeling framework used to estimate and reconcile the major components of the Great Lakes water budget. A Great Lakes water balance typically accounts for inputs and outputs such as precipitation over the lake, tributary runoff, evaporation from the lake surface, and inflows/outflows through connecting channels between lakes. Because many of these terms are measured indirectly or modeled, and because different datasets and methods can disagree, a major theme of this opportunity is to better quantify variability and uncertainty in those components and to explore alternative ways of incorporating multiple model estimates into the water balance framework. The model referenced by the opportunity is publicly accessible through the University of Michigan Deep Blue repository, and USACE is explicitly interested in building on prior case studies already conducted with this tool.

The initial phase of the proposed work is narrowly targeted: it focuses on connecting-channel discharge estimates for two key Great Lakes channels, the Detroit River and the St. Clair River, with an emphasis on how the estimated flows vary over time and how uncertain those estimates are. Specifically, the recipient would extend an existing Detroit River case study by Quinn, Clites, and Gronewold (2020) through the year 2019, essentially updating and lengthening the historical analysis to incorporate more recent conditions and data. In parallel, the recipient would expand that same type of analysis to the St. Clair River through 2019, creating a comparable uncertainty-focused discharge case study for that channel as well. These rivers are critical because they control flows between major lake basins (e.g., outflow from Lake St. Clair through the Detroit River to Lake Erie, and flow from Lake Huron through the St. Clair River system), so uncertainty in their discharge estimates propagates directly into uncertainty in lake-level and net-basin-supply assessments.

A key technical deliverable described in the announcement is the integration of conventional SFD models into L2SWBM. In this context, "SFD" refers to stage-fall-discharge relationships, which are commonly used hydraulic rating-type models that estimate discharge based on relationships among water level (stage), water surface slope or head difference (fall), and resulting flow (discharge). The grant calls for encoding these conventional SFD formulations directly into the L2SWBM framework so the water balance model can use them as part of its statistical estimation and uncertainty propagation. Importantly, the opportunity notes that prior parameter distributions for these SFD models will come from an external independent regression analysis that is developed and supplied by the government. In other words, USACE intends to provide foundational parameter information (priors) derived from separate analyses, and the recipient would incorporate those priors into the Bayesian/statistical machinery of L2SWBM to support uncertainty-aware discharge estimation.

Because this is a cooperative agreement, the government is not only funding the work but also participating in it. The announcement states that, in some instances, the government will generate associated models needed for uncertainty analysis within the large lake statistical water balance model, and that USACE will provide a team member who will complete required parallel tasks to help advance the research. This setup signals that the awardee should expect regular technical coordination, shared development responsibilities, and alignment with USACE priorities for Great Lakes hydrologic assessment.

Overall, the opportunity is aimed at strengthening the scientific basis for Great Lakes water budget estimates by making channel-flow components more transparent, reproducible, and statistically rigorous, particularly with respect to time-varying uncertainty. By extending an established Detroit River analysis through 2019, replicating and adapting the approach for the St. Clair River through 2019, and embedding conventional stage-fall-discharge models into an open-source water balance framework using government-supplied prior distributions, the project is designed to produce practical improvements that can be reused in future evaporation, runoff, and connecting-channel studies across the Great Lakes system.

• The Department of Defense, Dept. of the Army -- Corps of Engineers in the science and technology and other research and development sector is offering a public funding opportunity titled "Investigating Uncertainty Associated with the Great Lakes Water Balance" and is now available to receive applicants.
• Interested and eligible applicants and submit their applications by referencing the CFDA number(s): 12.630.
• This funding opportunity was created on Jun 04, 2020.
• Applicants must submit their applications by Jul 31, 2020. (Agency may still review applications by suitable applicants for the remaining/unused allocated funding in 2026.)
• Each selected applicant is eligible to receive up to $85,000.00 in funding.
• The number of recipients for this funding is limited to 1 candidate(s).
• Eligible applicants include: Others (see text field entitled Additional Information on Eligibility for clarification).

Apply for W81EWF 20 SOI 0028

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