Multimodel evaluation of longitudinal stream temperature gradient and dominant influencing factors in Michigan streams

• Published in: River research and applications Vol. 38; no. 10; pp. 1829 - 1842
• Main Authors: Andrews, Ryan M., Hayes, Daniel B., Zorn, Troy G.
• Format: Journal Article
• Language: English
• Published: Chichester, UK John Wiley & Sons, Ltd 01.12.2022; Wiley Subscription Services, Inc
• Subjects: Air temperature; Annual variations; Atmospheric models; Criteria; Discharge; Downstream; Fish; Groundwater; Hydrology; Hydrostatic pressure; longitudinal temperature gradient; Michigan; Migrations; multimodel selection; overland discharge; partial regression; Performance evaluation; Regression analysis; Rivers; Solar radiation; Stabilizing; Stream discharge; Stream flow; stream temperature; Streams; Structure-function relationships; Survival; Temperature; Temperature gradients; Water pressure; United States > US; Michigan
• ISSN: 1535-1459; 1535-1467
• DOI: 10.1002/rra.4054

Stream temperature is an important determinant of fish growth, migration, and survival and can thus impact the structure and function of stream ecosystems. Many streams in Michigan and elsewhere in North America receive groundwater inputs that help regulate instream conditions by stabilizing discharge as well as stream temperature. However, groundwater withdrawal can cause reductions in streamflow which typically results in increased summer stream temperatures. Other atmospheric and hydrologic variables (i.e., overland discharge) also impact the rate at which stream temperature changes as it flows downstream. We deployed paired up‐ and downstream water pressure and temperature loggers within 21 stream reaches throughout the state of Michigan to quantify and model relationships between stream discharge, air temperature, and longitudinal change in stream temperature (i.e., temperature gradient). Using multimodel selection criteria, we evaluated the performance of a hierarchical suite of models that predict temperature gradient as a function of potential driving variables. The multimodel selection criteria identified a best‐fitting model that was able to model the diurnal, seasonal, and annual variations in rates of longitudinal temperature fluctuations across most sample streams. Partial regression analysis indicated that proxy variables representing solar radiation at the stream surface were generally the most influential predictors of longitudinal changes in stream temperature, but air temperature and components of streamflow including groundwater input were significant predictors and important in many streams.