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

The summary for the Cooperative Agreement for CESU-affiliated Partner with Great Lakes Cooperative Ecosystem Studies Unit 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 Agreement for CESU-affiliated Partner with Great Lakes Cooperative Ecosystem Studies Unit: 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.