Grants / Department of the Interior / Geological Survey

G21AS00564: Cooperative Agreement for CESU-affiliated Partner with Great Lakes Cooperative Ecosystem Studies Unit

Closed Closed July 23, 2021 Opportunity G21AS00564 ☆ Save

This grant is closed. A newer notice from the same program is open now: Cooperative Agreement for affiliated Partner with Alaska Cooperative Ecosystem Studies Unit (G27AS00066). Status: Closes December 7, 2026 (30 days left). It is in our archive of Geological Survey notices from 2021.

• Award ceiling$180,000
• Cost sharingNot required
• Funding typeCooperative Agreement

Key dates

Who can apply

This opportunity was open to the applicants described in the notice.

• Others (see text field entitled "Additional Information on Eligibility" for clarification)

This financial assistance opportunity is being issued under a Cooperative Ecosystem Studies Unit (CESU) Program. CESU’s are partnerships that provide research, technical assistance, and education. Eligible recipients must be a participating partner of the Great Lakes Cooperative Ecosystem Studies Unit (CESU) Program.

Is this a realistic fit?

• Past U.S. Geological Survey Research and Data Collection awards averaged $557,251; this notice allows up to $180,000 per award.
• Most of the program's money (84% over five years) went to colleges and universities.
• No cost sharing or match is required.

What the grant funds

The USGS is offering a funding opportunity to a CESU partner for research in stream and reservoir water quality modeling, with a focus on temperature. Water temperature is a “master variable” for many important aquatic outcomes, including the suitability of habitat, evaporation rates, greenhouse gas exchange, and efficiency of thermoelectric energy production. Stream temperature is one of the most widely measured water characteristics by the USGS, though monitoring gaps in time and space requires modeling efforts to understand broad-scale temperature dynamics and supply decision-ready data to our stakeholders. Currently, stream and lake temperature are modeled separately, despite our knowledge that water flowing into a reservoir affects its temperature, and that reservoirs greatly impact the temperature of downstream river reaches. Further, in some places, water managers can affect downstream temperatures via reservoir releases, and understanding when to release, how much to release, and the expected water temperature changes from the release can support better decision making. The USGS and collaborators are developing process-guided machine learning models for streams and lakes that leverage the benefits of both process and machine learning models; the models are grounded in physical realism and perform well in data sparse and data rich conditions (e.g., Read et al., 2019). But key processes related to stream temperature remain unexplored or not accurately predicted or represented in the process-guided deep learning framework. These include but are not limited to: the impact of reservoir releases on downstream temperature, sub-daily prediction to accurately predict extremes, inclusion of different data types that may have lower accuracy (e.g., satellite estimated surface temperature), translation to finer resolution stream segments, prediction beneath reservoirs with varying amounts of data, and representation of certain processes that might be critical to evaluate long term change like groundwater contribution to stream temperature dynamics.

Category: Science and Technology and other Research and Development.

Applying now

This notice no longer takes applications. Apply under the newer notice, G27AS00066, which is open now.

The notice on Grants.gov keeps its full text and attachments, which is useful for planning a resubmission.