A Linear Approach to Modeling Stream Temperature in Utah

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.