<p> Stream temperature (ST) is a primary determinant of individual stream species distributions and community composition. Moreover, thermal modifications associated with urbanization, agriculture, reservoirs, and climate change can significantly alter stream ecosystem structure and function. Despite its importance, we lack ST measurements for the vast majority of USA streams. To effectively manage these important systems, we need to understand how STs vary geographically, what the natural (reference) thermal condition of altered streams was, and how STs will respond to climate change. Empirical ST models, if calibrated with physically meaningful predictors, could provide this information. My dissertation objectives were to: (1) develop empirical models that predict reference- and nonreference-condition STs for the conterminous USA, (2) assess how well modeled STs represent measured STs for predicting stream biotic communities, and (3) predict potential climate-related alterations to STs. For objective 1, I used random forest modeling with environmental data from several thousand US Geological Survey sites to model geographic variation in nonreference mean summer, mean winter, and mean annual STs. I used these models to identify thresholds of watershed alteration below which there were negligible effects on ST. With these reference-condition sites, I then built ST models to predict summer, winter, and annual STs that should occur in the absence of human-related alteration (r<sup>2</sup> = 0.87, 0.89, 0.95, respectively). To meet objective 2, I compared how well modeled and measured ST predicted stream benthic invertebrate composition across 92 streams. I also compared predicted and measured STs for estimating taxon-specific thermal optima. Modeled and measured STs performed equally well in both predicting invertebrate composition and estimating taxon-specific thermal optima (r<sup>2</sup> between observation and model-derived optima = 0.97). For objective 3, I first showed that predicted and measured ST responded similarly to historical variation in air temperatures. I then used downscaled climate projections to predict that summer, winter, and annual STs will warm by 1.6 °C - 1.7 °C on average by 2099. Finally, I used additional modeling to identify initial stream and watershed conditions (i.e., low heat loss rates and small base-flow index) most strongly associated with ST vulnerability to climate change.</p>
| Identifer | oai:union.ndltd.org:PROQUEST/oai:pqdtoai.proquest.com:3587567 |
| Date | 28 August 2013 |
| Creators | Hill, Ryan A. |
| Publisher | Utah State University |
| Source Sets | ProQuest.com |
| Language | English |
| Detected Language | English |
| Type | thesis |
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