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Regional Environmental Change

, Volume 13, Supplement 1, pp 131–139 | Cite as

Hydrologic impacts of future climate change on Southeast US watersheds

  • Satish Bastola
Original Article

Abstract

The hydrological impact of climate change is assessed for 28 watersheds located within the Southeast United States using output from global climate models (GCMs) from the Climate Model Intercomparison Project phase 3 (CMIP3) and phase 5 (CMIP5) run. Subsequently, the impact of projected change in seasonal streamflow is derived by propagating projected scenarios, generated using changes derived from GCMs and weather generators, through a suite of conceptual hydrological models. Analysis shows that the spread in the magnitude of change in temperature and rainfall for CMIP3 is wider than that for CMIP5. The reduction in the spread among many factors may be attributed to improved physics, model number and resolution, and emission scenarios. The spread in projected change in temperature (precipitation) increases (decreases) from southernmost to northernmost latitude. Hydrological projection with CMIP3 output for the 2070s shows that streamflow decreases for most of the watersheds in spring and summer and increased in fall. In contrast, CMIP5 results show an increase in flow for all seasons except with the high-end scenarios in spring. However, the uncertainty in projections in streamflow is high with model uncertainty dominating emission scenario. The variability in prediction uncertainty among watersheds is partly explained by the variability in wetness index. The probability distribution function for projected seasonal flow for each scenario is markedly wide and therefore reflects that the uncertainty associated with using multiple GCMs from both CMIP3 and CMIP5 experiment is high which makes design and implementation of adaption measure a difficult task.

Keywords

CMIP3 CMIP5 Hydrological models GLUE 

Notes

Acknowledgments

I acknowledge the editorial assistance of Kathy Fearon of the Center for Ocean-Atmospheric Prediction Studies, Florida State University in preparing this manuscript. This work was supported by NOAA Grant NA07OAR4310221 and USGS Grant 06HQGR0125. The paper has been greatly improved by the comments and suggestions from three anonymous reviewers and Dr V Misra. Its contents are solely the responsibility of the author and do not necessarily represent the official views of the acknowledged funding agencies.

Supplementary material

10113_2013_454_MOESM1_ESM.docx (110 kb)
Supplementary material 1 (DOCX 109 kb)

References

  1. Bastola S, Misra V (2013) Evaluation of dynamically downscaled reanalysis precipitation data for hydrological application. Hydrol Process. doi: 10.1002/hyp.9734
  2. Bastola S, Murphy C, Sweeney J (2011) The sensitivity of fluvial flood risk in irish catchments to the range of IPCC AR4 climate change scenarios. Sci Total Environ 409:5403–5415CrossRefGoogle Scholar
  3. Beven KJ, Binley AM (1992) The future of distributed models: model calibration and uncertainty prediction. Hydrol Process 6:279–298CrossRefGoogle Scholar
  4. Booth BBB, Bernie D, McNeall D, Hawkins E, Caesar J, Boulton C, Friedlingstein P, Sexton D (2012) Scenario and modelling uncertainty in global mean temperature change derived from emission driven global climate models, earth syst. Dyn Discuss 3:1055–1084. doi: 10.5194/esdd-3-1055-2012 CrossRefGoogle Scholar
  5. Boyle D (2001) Multicriteria calibration of hydrological models. PhD dissertation. Tucson, AZ: Department of Hydrology and Water Resources, University of ArizonaGoogle Scholar
  6. Christensen NS, Wood AW, Voisin N, Lettenmaier DP, Palmer RN (2004) Effects of climate change on the hydrology and water resources of the colorado river basin. Clim Change 62(1–3):337–363CrossRefGoogle Scholar
  7. Fowler HJ, Blenkinsopa S, Tebaldi C (2007) Review: linking climate change modelling to impacts studies: recent advances in downscaling techniques for hydrological modelling. Int J Climatol 27:547–1578Google Scholar
  8. Guilyardi E, Bellenger H et al (2012) A first look at ENSO in CMIP5. Clivar Exch 58 17(1):29–32Google Scholar
  9. Harding BL, Wood AW, Prairie JR (2012) The implications of climate change scenario selection for future streamflow projection in the upper colorado river basin. Hydrol Earth Syst Sci 16:3989–4007CrossRefGoogle Scholar
  10. Hargreaves GL, Hargreaves GH, Riley JP (1985) Irrigation water requirement for senegal river basin. J Irrig Drain Eng ASCE 111(3):265–275CrossRefGoogle Scholar
  11. Hawkins ED, Sutton R (2009) The potential to narrow uncertainty in regional climate predictions. Bull Am Meteorol Soc 90:1095–1107CrossRefGoogle Scholar
  12. Karl TR, Melillo JM, Peterson TC (eds) (2009) Global Climate Change Impacts in the United States. Cambridge University Press, New YorkGoogle Scholar
  13. Kim HM, Webster PJ, Curry JA (2012) Evaluation of short-term climate change prediction in multi-model CMIP5 decadal hindcasts. Geophys Res Lett 39:L10701. doi: 10.1029/2012GL051644 Google Scholar
  14. Kuntti R and Sadlacek J (2012) Robustness and uncertainties in the new CMIP5 climate model projections doi: 10.1038/NCLIMATE1716
  15. Ledbetter R, Prudhomme C, Arnell N (2011) A method for incorporating climate variability in climate change impact assessments: sensitivity of river flows in the eden catchment to precipitation scenarios. Clim Change 113:803–823CrossRefGoogle Scholar
  16. Madsen H (2000) Automatic calibration of a conceptual rainfall–runoff model using multiple objectives. J Hydrol 235:276–288CrossRefGoogle Scholar
  17. Maslin M, Austin P (2012) Climate models at their limit? Nature 486:183–184CrossRefGoogle Scholar
  18. Maurer EP (2007) Uncertainty in hydrologic impacts of climate change in the Sierra Nevada mountains, California under two emissions scenarios. Clim Change 82:309–332CrossRefGoogle Scholar
  19. Meehl GA, Bony S (2011) Introduction to CMIP5. Clivar Exch 56 16(2):4–5Google Scholar
  20. Meehl G, Covey C, Delworth T, Latif M, McAvaney B, Mitchell J, Stouffer R, Taylor K (2007) The WCRP CMIP3 multimodel dataset. Bull Am Meteorol Soc 88:1383–1394CrossRefGoogle Scholar
  21. Misra V, DiNapoli S (2012) Understanding wet season variations over Florida Clim. Dyn. doi: 10.1007/s00382-012-1382-4
  22. Mo KC, Schemm JKE, Yoo SH (2009) Influence of ENSO and the Atlantic multidecadal oscillation on drought over the United States. J. Clim 22:5962–5982CrossRefGoogle Scholar
  23. Moss et al (2010) The next generation of scenarios for climate change research and assessment. Nature 463:747–756. doi: 10.1038/nature08823
  24. Richarsdon CW, Wright DA (1984) WGEN: a model for generating daily weather variables U.S. Department of Agricultural, Agricultural Research Service ARS-8, p 83Google Scholar
  25. Schaake J, Cong S, Duan Q (2006) US mopex datasets, IAHS publication series (https://ereports-ext.llnl.gov/pdf/333681.pdf)
  26. Sugawara M (1995) Tank model. In: Singh VP (ed) Computer models of watershed hydrology. Water Resources Publications, Littleton, Co, pp 165–214Google Scholar
  27. Taylor KE, Stouffer RJ, Meehl GA (2012) An overview of CMIP5 and the experiment design. Bull Am Meteorol Soc 93:485–498. doi: 10.1175/BAMS-D-11-00094.1 CrossRefGoogle Scholar
  28. Wilks DS (1992) Adapting stochastic weather generation algorithms for climate changes studies. Clim Change 22:67–84CrossRefGoogle Scholar
  29. Wood AW, Leung LR, Sridhar V, Lettenmaier DP (2004) Hydrologic implications of dynamical and statistical approaches to downscaling climate model outputs. Clim Change 62:189–216. doi: 10.1023/B:CLIM.0000013685.99609.9e CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Center for Ocean-Atmospheric Prediction StudiesFlorida State UniversityTallahasseeUSA

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