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A model to predict stream water temperature across the conterminous USA

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https://ir.library.oregonstate.edu/concern/articles/qb98mh28b

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  • Stream water temperature (tₛ) is a critical water quality parameter for aquatic ecosystems. However, tₛ records are sparse or nonexistent in many river systems. In this work, we present an empirical model to predict tₛ at the site scale across the USA. The model, derived using data from 171 reference sites selected from the Geospatial Attributes of Gages for Evaluating Streamflow database, describes the linear relationship between monthly mean air temperature (tₐ) and tₛ. Multiple linear regression models are used to predict the slope (m) and intercept (b) of the tₐ–tₛ linear relation as a function of climatic, hydrologic and land cover characteristics. Model performance to predict ts resulted in a mean Nash–Sutcliffe efficiency coefficient of 0.78 across all sites. Application of the model to predict tₛ at additional 89 nonreference sites with a higher human alteration yielded a mean Nash– Sutcliffe value of 0.45. We also analysed seasonal thermal sensitivity (m) and found strong hysteresis in the tₐ–tₛ relation. Drainage area exerts a strong control on m in all seasons, whereas the cooling effect of groundwater was only evident for the spring and fall seasons. However, groundwater contributions are negatively related to mean tₛ in all seasons. Finally, we found that elevation and mean basin slope are negatively related to mean tₛ in all seasons, indicating that steep basins tend to stay cooler because of shorter residence times to gain heat from their surroundings. This model can potentially be used to predict climate change impacts on ts across the USA.
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  • Segura, C., Caldwell, P., Sun, G., McNulty, S., & Zhang, Y. (2015). A model to predict stream water temperature across the conterminous USA. Hydrological Processes, 29(9), 2178-2195. doi: 10.1002/hyp.10357
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  • 29
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  • 9
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  • This is the publisher’s final pdf. The published article is copyrighted by John Wiley & Sons Ltd.
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  • This study was funded by the National Science Foundation EaSM programme (AGS-1049200) at NCSU and the Eastern Forest Environmental Threat Assessment Center (EFETAC), USDA Forest Service.
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