Cooperative Ecosystem Studies Unit, Great Lakes Northern Forests CESU

Funding Opportunity ID: 328715
Opportunity Number: G20AS00139
Opportunity Title: Cooperative Ecosystem Studies Unit, Great Lakes Northern Forests CESU
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): 15.808
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.
Agency Code: DOI-USGS1
Agency Name: Department of the Interior
U. S. Geological Survey
Posted Date: Aug 17, 2020
Close Date: Sep 07, 2020
Last Updated Date: Aug 17, 2020
Award Ceiling: $110,000
Award Floor: $0
Estimated Total Program Funding: $110,000
Expected Number of Awards: 1
Description: The USGS is offering a funding opportunity to a CESU partner for research in stream and reservoir temperature modeling. 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). However, we have yet to model a stream network that reflects both lake and stream temperature dynamics.
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