EARLY HEARING DETECTION AND INTERVENTION VIRTUAL CONFERENCE
MARCH 2-5, 2021
(Virtually the same conference, without elevators, airplane tickets, or hotel room keys)
5/24/2018 | 3:00 PM - 3:15 PM | A STATISTICAL MIXING MODEL PREDICTS NETWORK-SCALE TEMPERATURE PATTERNS IN STREAMS WITH ABUNDANT ALPINE LAKES | 310 B
A STATISTICAL MIXING MODEL PREDICTS NETWORK-SCALE TEMPERATURE PATTERNS IN STREAMS WITH ABUNDANT ALPINE LAKES
Streams often display a longitudinal trend in stream temperature along elevational gradients. However, atmospheric heat exchange in alpine lakes differs substantially from that of alpine streams. Thus, lake-outlet streams contribute spatial variation in stream temperature across montane stream networks, yielding complex longitudinal temperature patterns that deviate from the expected monotonic trends. We developed a GIS-based metric of lake influence and incorporated the metric into a statistical mixing model to disentangle and characterize alpine-lake and elevation effects on stream temperature in the North St. Vrain stream network in Rocky Mountain National Park, Colorado, USA. Relative to more conventional multiple linear regression approaches, our method yielded a marked increase in explanatory power for daily mean stream temperature across a stream network with abundant alpine lakes. As a mixing model, our method may be applicable to any system where network-scale patterns in stream temperature arises from spatially variable influences of multiple water sources that have distinct thermal regimes.
- Temperature
- Watershed
- Modeling
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Presenters/Authors
Geoffrey Poole
(), Montana State University, Montana Institute on Ecosystems, gpoole@montana.edu ;
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Sam Carlson
(), Montana State University, sam.p.carlson@gmail.com;
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