This grant is for advancing stream and reservoir water quality modeling, specifically focusing on temperature dynamics within the Great Lakes region. The USGS aims to fund a CESU-affiliated partner to research how water temperature, a crucial environmental variable, impacts aquatic habitats, energy production, and gas exchange. Currently, separate models for streams and lakes limit comprehensive understanding, especially regarding the impact of reservoir releases on downstream temperatures. This initiative seeks to integrate and enhance process-guided machine learning models. Key research areas include understanding reservoir release effects, enabling sub-daily predictions, incorporating diverse data, achieving finer spatial resolution, and accurately representing groundwater contributions to temperature variations, ultimately supporting better water management decisions.
Opportunity ID: 334291
General Information
| Document Type: | Grants Notice |
| Funding Opportunity Number: | G21AS00564 |
| Funding Opportunity Title: | Cooperative Agreement for CESU-affiliated Partner with Great Lakes Cooperative Ecosystem Studies Unit |
| 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: | – |
| Expected Number of Awards: | – |
| Assistance Listings: | 15.808 — U.S. Geological Survey Research and Data Collection |
| Cost Sharing or Matching Requirement: | No |
| Version: | Synopsis 1 |
| Posted Date: | Jun 23, 2021 |
| Last Updated Date: | Jun 23, 2021 |
| Original Closing Date for Applications: | Jul 23, 2021 Electronically submitted applications must be submitted no later than 5:00 p.m., ET, on the listed application due date. |
| Current Closing Date for Applications: | Jul 23, 2021 Electronically submitted applications must be submitted no later than 5:00 p.m., ET, on the listed application due date. |
| Archive Date: | – |
| Estimated Total Program Funding: | – |
| Award Ceiling: | $180,000 |
| Award Floor: | $0 |
Eligibility
| Eligible Applicants: | Others (see text field entitled “Additional Information on Eligibility” for clarification) |
| Additional Information on Eligibility: | 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. |
Additional Information
| Agency Name: | Geological Survey |
| Description: | 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. |
| Link to Additional Information: | – |
| Grantor Contact Information: | If you have difficulty accessing the full announcement electronically, please contact:
FAITH GRAVES
fgraves@usgs.gov Email:fgraves@usgs.gov |
Version History
| Version | Modification Description | Updated Date |
|---|---|---|
Related Documents
Folder 334291 Full Announcement-Full Announcement -> FUNDING OPPORTUNITY.pdf
Packages
| Agency Contact Information: | FAITH GRAVES fgraves@usgs.gov Email: fgraves@usgs.gov |
| Who Can Apply: | Organization Applicants |
| Assistance Listing Number | Competition ID | Competition Title | Opportunity Package ID | Opening Date | Closing Date | Actions |
|---|---|---|---|---|---|---|
| 15.808 | G21AS00564 | Cooperative Agreement for CESU-affiliated Partner with Great Lakes Cooperative Ecosystem Studies Unit | PKG00267880 | Jun 23, 2021 | Jul 23, 2021 | View |
Package 1
Mandatory forms
334291 SF424_4_0-4.0.pdf
334291 ProjectNarrativeAttachments_1_2-1.2.pdf
334291 SF424A-1.0.pdf
334291 SF424B-1.1.pdf
Share This Post, Choose Your Platform!
About the Author: osnqt
