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

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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.