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Greg Goodrum

Utah State University | Graduate Research Assistant

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ABSTRACT:

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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ABSTRACT:

This resource presents an example method for taking raw GAMUT sensor data and calculating daily averages across a given time period using the Python programming language. The example code here uses turbidity as an example, but other variables and timescales can use a similar method. This resource was created as part of the coursework associated with CEE6110 at Utah State University.

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ABSTRACT:

The purpose of this project is to assess whether the distribution of dissolved oxygen (DO) data collection locations is representative of the stream networks found within the current ranges of Bonneville cutthroat trout (BCT) in the state of Utah, and to recommend additional monitoring locations in order to improve shortfalls in spatial representation.

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Composite Resource Composite Resource

ABSTRACT:

The purpose of this project is to assess whether the distribution of dissolved oxygen (DO) data collection locations is representative of the stream networks found within the current ranges of Bonneville cutthroat trout (BCT) in the state of Utah, and to recommend additional monitoring locations in order to improve shortfalls in spatial representation.

Show More
Composite Resource Composite Resource

ABSTRACT:

This resource presents an example method for taking raw GAMUT sensor data and calculating daily averages across a given time period using the Python programming language. The example code here uses turbidity as an example, but other variables and timescales can use a similar method. This resource was created as part of the coursework associated with CEE6110 at Utah State University.

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A Linear Approach to Modeling Stream Temperature in Utah
Created: Feb. 21, 2019, 8:24 p.m.
Authors: Greg Goodrum

ABSTRACT:

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.

Show More