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|Resource type:||Composite Resource|
|Storage:||The size of this resource is 1019.4 MB|
|Created:||Feb 21, 2019 at 8:24 p.m.|
|Last updated:|| Feb 22, 2019 at 6:16 p.m.
|Citation:||See how to cite this resource|
|Content types:||Geographic Feature Content|
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Globally changing temperature and precipitation patterns are causing rapid changes stream temperatures, which in turn drive changes in the life histories and distributions of aquatic biota. However, large-scale stream temperature datasets have not been developed, and observational data remains limited. In order to better understand how ongoing thermal regime changes impact aquatic species, managers and researchers need better methods of quantifying stream temperatures at large spatial scales. Here, a linear regression model is used to develop a relationship between air and stream temperature, then is used to predict stream temperatures across the state of Utah in the month of August. Model validity was assessed by examining goodness of fit to observation data using R², Nash-Sutcliffe Efficiency index, and root mean square error-observations standard deviation ratio (RSR). Impact of outliers were assessed by examining mean absolute error (MAE), root mean square error (RMSE), and residuals. The approach presented here contributes to the well-described linear air/stream temperature model by providing a study of its performance at large spatial scales.
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|The content of this resource is derived from||http://prism.oregonstate.edu/|
|The content of this resource is derived from||https://www.fs.fed.us/rm/boise/AWAE/projects/NorWeST.html|
|The content of this resource is derived from||https://www.waterqualitydata.us/|
People or Organizations that contributed technically, materially, financially, or provided general support for the creation of the resource's content but are not considered authors.
|Sarah Null||Utah State University;iUTAH||Utah, US|
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This resource is shared under the Creative Commons Attribution CC BY.http://creativecommons.org/licenses/by/4.0/