Predicting thermal vulnerability of stream and river ecosystems to climate change

Abstract

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.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

References

  1. Bogan T, Mohseni O, Stefan HG (2003) Stream temperature-equilibrium temperature relationship. Water Resour Res 39:1245

    Google Scholar 

  2. Breiman L (2001) Random forests. Mach Learn 45:5–32

    Article  Google Scholar 

  3. Brown GW, Krygier JT (1970) Effects of clear-cutting on stream temperature. Water Resour Res 6:1133–1139

    Article  Google Scholar 

  4. Caissie D, El-Jabi N, Satish M (2001) Modeling of maximum daily water temperatures in a small stream using air temperatures. J Hydrol 251:14–28

    Article  Google Scholar 

  5. Caldwell P, Chin H-NS, Bader DC, Bala G (2009) Evaluation of a WRF dynamical downscaling simulation over California. Clim Chang 95:499–521

    Article  Google Scholar 

  6. Carlisle DM, Falcone J, Wolock D, Meador M, Norris R (2010) Predicting the natural flow regime: models for assessing hydrological alteration in streams. River Res Appl 26:118–136

    Google Scholar 

  7. Chang H, Psaris M (2013) Local landscape predictors of maximum stream temperature and thermal sensitivity n the Columbia River Basin, USA. Sci Total Environ 461–462:587–600

    Article  Google Scholar 

  8. Chapra SC (1997) Surface water-quality modeling. Waveland Press, Long Grove

    Google Scholar 

  9. Chessman BC (2009) Climatic changes and 13-year trends in stream macroinvertebrate assemblages in New South Wales, Australia. Glob Chang Biol 15:2791–2802

    Article  Google Scholar 

  10. Collins WD, Bitz CM, Blackmon ML, Bonan GB, Bretherton CS, Carton JA, Chang P, Doney SC, Hack JJ, Henderson TB (2006) The Community Climate System Model Version 3 (CCSM3). J Clim 19:2122–2143

    Article  Google Scholar 

  11. Cutler DR, Edwards TC, Beard KH, Cutler A, Hess KT (2007) Random forests for classification in ecology. Ecology 88:2783–2792

    Article  Google Scholar 

  12. Daly C, Halbleib M, Smith JI, Gibson WP, Doggett MK, Taylor GH, Curtis J, Pasteris PP (2008) Physiographically sensitive mapping of climatological temperature and precipitation across the conterminous United States. Int J Climatol 28:2031–2064

    Article  Google Scholar 

  13. Flint L, Flint A (2012) Downscaling future climate scenarios to fine scales for hydrologic and ecological modeling and analysis. Ecol Process 1:1–15

    Article  Google Scholar 

  14. Hari RE, Livingstone DM, Siber R, Burkhardt-Holm P, Guttinger H (2006) Consequences of climatic change for water temperature and brown trout populations in Alpine rivers and streams. Glob Chang Biol 12:10–26

    Article  Google Scholar 

  15. Hastie TJ, Tibshirani RJ, Friedman JH (2001) The elements of statistical learning: data mining, inference, and prediction. Springer, New York

    Book  Google Scholar 

  16. Hawkins CP, Hogue JN, Decker LM, Feminella JW (1997) Channel morphology, water temperature, and assemblage structure of stream insects. J N Am Benthol Soc 16:728–749

    Article  Google Scholar 

  17. Hawkins CP, Olson JR, Hill RA (2010) The reference condition: predicting benchmarks for ecological and water-quality assessments. J N Am Benthol Soc 29:312–358

    Article  Google Scholar 

  18. Hill RA, Hawkins CP, Carlisle DM (2013) Predicting thermal reference conditions for USA streams. Freshwat Sci 32:39–55

    Article  Google Scholar 

  19. Isaak DJ, Luce CH, Rieman BE, Nagel DE, Peterson EE, Horan DL, Parkes S, Chandler GL (2010) Effects of climate change and wildfire on stream temperatures and salmonid thermal habitat in a mountain river network. Ecol Appl 20:1350–1371

    Article  Google Scholar 

  20. Jin J, Wang SY, Gillies RR (2011) Improved dynamical downscaling of climate projections for the western United States. In: Climate Change / Book 2, Kheradmand H (ed). InTech Open Access Publisher (Available from: http://www.intechopen.com/books/climate-change-research-and-technology-for-adaptation-and-mitigation/an-improved-dynamical-downscaling-for-the-western-united-states)

  21. Kalnay E, Kanamitsu M, Kistler R, Collins W, Deaven D, Gandin L, Iredell M, Saha S, White G, Woollen J, Zhu Y, Chelliah M, Ebisuzaki W, Higgins W, Janowiak J, Mo KC, Ropelewski C, Wang J, Leetmaa A, Reynolds R, Jenne R, Joseph D (1996) The NCEP/NCAR 40-year reanalysis project. Bull Am Meteorol Soc 77:437–471

    Article  Google Scholar 

  22. Kaushal SS, Likens GE, Jaworski NA, Pace ML, Sides AM, Seekell D, Belt KT, Secor DH, Wingate RL (2010) Rising stream and river temperatures in the United States. Front Ecol Environ 8:461–466

    Article  Google Scholar 

  23. Kelleher C, Wagener T, Gooseff M, McGlynn B, McGuire K, Marshall L (2012) Investigating controls on the thermal sensitivity of Pennsylvania streams. Hydrol Process 26:771–785

    Article  Google Scholar 

  24. Kundzewicz ZW, Stakhiv EZ (2010) Are climate models “ready for prime time” in water resources management applications, or is more research needed? Hydrol Sci J 55:1085–1089

    Article  Google Scholar 

  25. Kurylyk BL, Bourque CP-A, MacQuarrie KTB (2013) Potential surface temperature and shallow groundwater response to climate change: an example from a small forested catchment in east-central New Brunswick (Canada). Hydrol Earth Syst Sci 10:3283–3326

    Article  Google Scholar 

  26. Leopold LB, Wolman MG, Miller JP (1964) Fluvial processes in geomorphology. Dover Publications, New York

    Google Scholar 

  27. Mearns LO, Sain S, Leung LR, Bukovsky MS, McGinnis S, Biner S, Caya D, Arritt RW, Gutowski W, Takle E, Snyder M, Jones RG, Nunes AMB, Tucker S, Herzmann D, McDaniel L, Sloan L (2013) Climate change projections of the north american regional climate change assessment program. Clim Chang. doi:10.1007/s10584-013-0831-3

    Google Scholar 

  28. Mohseni O, Stefan HG (1999) Stream temperature air temperature relationship: a physical interpretation. J Hydrol 218:128–141

    Article  Google Scholar 

  29. Mohseni O, Erickson TR, Stefan HG (2002) Upper bounds for stream temperatures in the contiguous United States. J Environ Eng-ASCE 128:4–11

    Article  Google Scholar 

  30. Mohseni O, Stefan HG, Eaton JG (2003) Global warming and potential changes in fish habitat in US streams. Clim Chang 59:389–409

    Article  Google Scholar 

  31. O’Driscoll M, DeWalle DR (2006) Stream-air temperature relations to classify stream-ground water interactions in a karst setting, central Pennsylvania, USA. J Hydrol 329:140–153

    Article  Google Scholar 

  32. Ordoyne C, Friedl MA (2008) Using MODIS data to characterize seasonal inundation patterns in the Florida Everglades. Remote Sens Environ 112:4107–4119

    Article  Google Scholar 

  33. Raupach MR, Marland G, Ciais P, Le Quere C, Canadell JG, Klepper G, Field CB (2007) Global and regional drivers of accelerating CO2 emissions. Proc Natl Acad Sci U S A 104:10288–10293

    Article  Google Scholar 

  34. Solomon S, Qin D, Manning M, Chen Z, Marquis M, Averyt KB, Tignor M, Miller HL (2007) Contributions of working group I to the fourth assessment report of the intergovernmental panel on climate change. Chapter 10.5.4 – the multi-model ensemble approach. Cambridge University Press, United Kingdom

    Google Scholar 

  35. Stefan HG, Sinokrot BA (1993) Projected global climate-change impact on water temperatures in 5 north central United-States streams. Clim Chang 24:353–381

    Article  Google Scholar 

  36. Sweeney BW (1993) Effects of streamside vegetation on macroinvertebrate communities of white clay creek in eastern North America. Proc Acad Natl Sci Phila 144:291–340

    Google Scholar 

  37. van Vliet MTH, Franssen WHP, Yearsley JR, Ludwig F, Haddeland I, Lettenmaier DP, Kabat P (2013) Global river discharge and water temperature under climate change. Glob Environ Chang 23:450–464

    Article  Google Scholar 

  38. Vannote RL, Sweeney BW (1980) Geographic analysis of thermal equilibria: a conceptual model for evaluating the effects of natural and modified thermal regimes on aquatic insect communities. Am Nat 115:667–695

    Article  Google Scholar 

  39. Webb BW (1996) Trends in stream and river temperature. Hydrol Process 10:205–226

    Article  Google Scholar 

  40. Wilby RL (2010) Evaluating climate model outputs for hydrological applications. Hydrol Sci J 55:1090–1093

    Article  Google Scholar 

  41. Wilby RL, Gutowski W, Arritt R, Takle E, Pan Z, Leavesley G, Clark M (2000) Hydrological responses to dynamically and statistically downscaled climate model output. Geophys Res Lett 27:1199–1202

    Article  Google Scholar 

  42. Wolock DM (1997) STATSGO soil characteristics for the conterminous United States. U.S. Geological Survey Open-File Report 97–656. US Geological Survey, Lawrence

    Google Scholar 

  43. Wolock DM (2003) Base-flow index grid for the conterminous United States. U.S. Geological Survey Open-File Report 03–263. US Geological Survey, Reston

    Google Scholar 

  44. Woodward G, Perkins DM, Brown LE (2010) Climate change and freshwater ecosystems: impacts across multiple levels of organization. Philos Trans Roy Soc B-Biol Sci 365:2093–2106

    Article  Google Scholar 

Download references

Acknowledgments

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.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Ryan A. Hill.

Electronic supplementary material

Below is the link to the electronic supplementary material.

ESM 1

(PDF 657 kb)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Hill, R.A., Hawkins, C.P. & Jin, J. Predicting thermal vulnerability of stream and river ecosystems to climate change. Climatic Change 125, 399–412 (2014). https://doi.org/10.1007/s10584-014-1174-4

Download citation

Keywords

  • Random Forest
  • Vapor Pressure Deficit
  • Longwave Radiation
  • Stream Temperature
  • Individual Stream