Predicting thermal vulnerability of stream and river ecosystems to climate change
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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.
KeywordsRandom Forest Vapor Pressure Deficit Longwave Radiation Stream Temperature Individual Stream
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.
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