Cooperative Ecosystem Studies Unit, Great Lakes Northern Forests CESU

The summary for the Cooperative Ecosystem Studies Unit, Great Lakes Northern Forests CESU grant is detailed below. This summary states who is eligible for the grant, how much grant money will be awarded, current and past deadlines, Catalog of Federal Domestic Assistance (CFDA) numbers, and a sampling of similar government grants. Verify the accuracy of the data FederalGrants.com provides by visiting the webpage noted in the Link to Full Announcement section or by contacting the appropriate person listed as the Grant Announcement Contact. If any section is incomplete, please visit the website for the Geological Survey, which is the U.S. government agency offering this grant.

Cooperative Ecosystem Studies Unit, Great Lakes Northern Forests CESU: 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.