Stream Temperature Monitoring and the Development of Models to Predict Stream Temperatures in Priority Drainages in the East Slopes 2004/2005
Author(s)
Kevin Fitzsimmons
Summary
In the south central East Slopes of Alberta, threats to brown trout (Salmo trutta), bull trout (Salvelinus confluentus), brook trout (S. fontinalis), rainbow trout (Oncorhynchus mykiss) cutthroat trout (O. clarki) and mountain whitefish (Prosopium williamsoni) from declining stream flows and increased stream temperatures has been recognized as an important issue to the management of these sportfish. Alberta Sustainable Resource Development (ASRD), Fisheries Management has expressed a desire to predict water temperatures in study streams prone to high temperature events, through the use of
real‐time stream temperature data readily available from the Alberta Environment (AENV) internet web site. Having the ability to predict stream temperatures would allow for proactive management of the fisheries in these waters by resource mangers.
The objectives of this project were to:
• Collect baseline stream temperature data from streams prone to high temperatures.
• Investigate the relationship between stream temperature data collected throughout a drainage in 2004 by the Alberta Conservation Association (ACA) and stream temperature data collected in 2004 at AENV real‐time stream temperature‐monitoring sites.
• Describe this relationship as a model for the prediction of stream temperatures throughout a drainage based on the real‐time stream temperature data collected by AENV.
• Assess the predictive ability of the model using data obtained in 2003 by the ACA and ASRD and 2003 AENV real‐time stream temperature data.
In the summer of 2004, the ACA collected data from 44 stream temperature monitoring sites. Stream temperature monitoring sites were located in the Red Deer River (n=25), Bow River (n=3) and Highwood River (n=16) drainages. Stream temperatures were collected using temperature data loggers placed in study streams at 30 km intervals and programmed to record stream temperatures every 15 minutes. Study streams were chosen based on the opinion of ASRD, or prior stream temperature monitoring indicating that high stream temperatures occur in a particular stream.
The relationship between ACA and AENV data was found to be strongly linear, therefore liner regression was selected as the most appropriate method to describe this relationship. Overall, the linear regression models developed here from ACA and AENV data explain the relationship between ACA and AENV data sites very well. However, the goodness of fit of the models, and therefore their predictive ability, tends to decrease with increasing distance upstream on study streams.
Graphical analysis of the values predicted by the ACA models for 2003 and observed data from 2003 indicates that the model can successfully predict water temperature at a location in a drainage based on the water temperatures at the AENV real‐time stream temperature‐monitoring station. Model assessments have, however, only been conducted for five of the models developed in the project where 2003 stream temperature data was available. The remaining models have not been assessed, as no data is available for these assessments.