A statistical model for predicting water temperature in temperate rivers and streams
Patricia Y. Koh
A * , Jian D. L. Yen B , Jarod P. Lyon B , Matt West A and John R. Morrongiello A
A
B
Abstract
Water temperature affects the biology and ecology of many freshwater species. However, in situ water temperature measurements are not always available because of spatial or temporal gaps in observations.
We evaluated the importance of different environmental variables in predicting water temperature in temperate Australian rivers and streams and developed a water temperature model for use in these environments.
We used linear mixed models that incorporated combinations of air temperature, stream flow and catchment variables to predict daily water temperatures.
Air temperature integrated over the preceding 7 days, in conjunction with elevation, were very good predictors of water temperature. However, stream flow did not significantly improve model predictions.
Air temperature explained the most variation in water temperature, and elevation also improved model predictions.
Our approach demonstrated that water temperature in temperate rivers and streams can be readily modelled using elevation and air temperature across large spatial and temporal scales. Our work has provided an easily implementable method to fill gaps in monitoring networks in temperate climate zones exhibiting warm summers. The predictions created by our model will have most use in studies where researchers want to explore the impacts of relative changes in water temperature.
Keywords: air temperature, Australia, elevation, freshwater, lotic, river temperature, statistical models, stream temperature, water temperature.
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