Climatic Change

, Volume 125, Issue 3–4, pp 399–412 | Cite as

Predicting thermal vulnerability of stream and river ecosystems to climate change

  • Ryan A. HillEmail author
  • Charles P. Hawkins
  • Jiming Jin


We use a predictive model of mean summer stream temperature to assess the vulnerability of USA streams to thermal alteration associated with climate change. The model uses air temperature and watershed features (e.g., watershed area and slope) from 569 US Geological Survey sites in the conterminous USA to predict stream temperatures. We assess the model for predicting climate-related variation in stream temperature by comparing observed and predicted historical stream temperature changes. Analysis of covariance confirms that observed and predicted changes in stream temperatures respond similarly to historical changes in air temperature. When applied to spatially-downscaled future air temperature projections (A2 emission scenario), the model predicts mean warming of 2.2 °C for the conterminous USA by 2100. Stream temperatures are most responsive to climate changes in the Cascade and Appalachian Mountains and least responsive in the southeastern USA. We then use random forests to conduct an empirical sensitivity analysis to identify those stream features most strongly associated with both observed historical and predicted future changes in summer stream temperatures. Larger changes in stream temperature are associated with warmer future air temperatures, greater air temperature changes, and larger watershed areas. Smaller changes in stream temperature are predicted for streams with high initial rates of heat loss associated with longwave radiation and evaporation, and greater base-flow index values. These models provide important insight into the potential extent of stream temperature warming at a near-continental scale and why some streams will likely be more vulnerable to climate change than others.


Random Forest Vapor Pressure Deficit Longwave Radiation Stream Temperature Individual Stream 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This research was supported by grant (RD834186) from the US Environmental Protection Agency’s National Center for Environmental Research (NCER) Science to Achieve Results (STAR) program. RAH and CPH were equally responsible for research design and data interpretation. JJ was responsible for developing, applying, and interpreting the climate model. We thank David Tarboton, Sarah Null, and three anonymous reviewers for comments that improved the manuscript, and Adele and Richard Cutler for advice regarding random forest models.

Supplementary material

10584_2014_1174_MOESM1_ESM.pdf (657 kb)
ESM 1 (PDF 657 kb)


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Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  • Ryan A. Hill
    • 1
    • 2
    Email author
  • Charles P. Hawkins
    • 1
    • 2
    • 3
  • Jiming Jin
    • 1
    • 4
  1. 1.Department of Watershed SciencesUtah State University LoganLoganUSA
  2. 2.Western Center for Monitoring and Assessment of Freshwater EcosystemsUtah State UniversityLoganUSA
  3. 3.The Ecology CenterUtah State UniversityLoganUSA
  4. 4.Department of Plants, Soils & ClimateUtah State UniversityLoganUSA

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