Climatic Change

, Volume 62, Issue 1–3, pp 189–216 | Cite as

Hydrologic Implications of Dynamical and Statistical Approaches to Downscaling Climate Model Outputs

  • A. W. Wood
  • L. R. Leung
  • V. Sridhar
  • D. P. Lettenmaier
Article

Abstract

Six approaches for downscaling climate model outputs for use in hydrologic simulation were evaluated, with particular emphasis on each method's ability to produce precipitation and other variables used to drive a macroscale hydrology model applied at much higher spatial resolution than the climate model. Comparisons were made on the basis of a twenty-year retrospective (1975–1995) climate simulation produced by the NCAR-DOE Parallel ClimateModel (PCM), and the implications of the comparison for a future(2040–2060) PCM climate scenario were also explored. The six approaches were made up of three relatively simple statistical downscaling methods – linear interpolation (LI), spatial disaggregation (SD), and bias-correction and spatial disaggregation (BCSD) – each applied to both PCM output directly(at T42 spatial resolution), and after dynamical downscaling via a Regional Climate Model (RCM – at 1/2-degree spatial resolution), for downscaling the climate model outputs to the 1/8-degree spatial resolution of the hydrological model. For the retrospective climate simulation, results were compared to an observed gridded climatology of temperature and precipitation, and gridded hydrologic variables resulting from forcing the hydrologic model with observations. The most significant findings are that the BCSD method was successful in reproducing the main features of the observed hydrometeorology from the retrospective climate simulation, when applied to both PCM and RCM outputs. Linear interpolation produced better results using RCM output than PCM output, but both methods (PCM-LI and RCM-LI) lead to unacceptably biased hydrologic simulations. Spatial disaggregation of the PCM output produced results similar to those achieved with the RCM interpolated output; nonetheless, neither PCM nor RCM output was useful for hydrologic simulation purposes without a bias-correction step. For the future climate scenario, only the BCSD-method (using PCM or RCM) was able to produce hydrologically plausible results. With the BCSD method, the RCM-derived hydrology was more sensitive to climate change than the PCM-derived hydrology.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Beniston, M., Diaz, H. F., and Bradley, R. S.: 1997, ‘Climatic Change at High Elevation Sites: An Overview’, Clim. Change 36, 233–251.Google Scholar
  2. Betts, A. K., Chen, F., Mitchell, K., and Janjic, Z. I.: 1997, ‘Assessment of the Land Surface and Boundary LayerModels in Two Operational Versions of the NCEP Eta Model Using FIFE Data’, Mon. Wea. Rev. 125, 2896–2916.Google Scholar
  3. Charles, S. P., Bates, B. C., Whetton, P. H., and Hughes, J. P.: 1999, ‘Validation of a Downscaling Model for Changed Climate Conditions in Southwestern Australia’, Clim. Res. 12, 1–14.Google Scholar
  4. Christensen, N. S., Wood, A. W., Lettenmaier, D. P., and Palmer, R. N.: 2004, ‘Effects of Climate Change on the Hydrology and Water Resources of the Colorado River Basin’, Clim. Change 62, 337–363.Google Scholar
  5. Cocke, S. and LaRow, T. E.: 2000, ‘Seasonal Predictions Using a Regional Spectral Model Embedded within a Coupled Ocean-Atmosphere Model, Mon. Wea. Rev. 128, 689–708.Google Scholar
  6. Crane, R. G., Yarnal, B., Barron, E. J., and Hewitson, B. C.: 2002, ‘Scale Interactions and Regional Climate: Examples from the Susquehanna River Basin, Human and Ecological Risk Assessment 8, 147–158.Google Scholar
  7. Dai, A., Washington, W. M., Meehl, G. A., Bettge, T. W., and Strand, W. G.: 2004, ‘The ACPI Climate Change Simulations’, Clim. Change 62, 29–43.Google Scholar
  8. Dettinger, M. D., Cayan, D. R., Meyer, M. K., and Jeton, A. E.: 2004, ‘Simulated Hydrologic Responses to Climate Variations and Change in the Merced, Carson, and American River basins, Sierra Nevada, California, 1900-2099’, Clim. Change 62, 283–317.Google Scholar
  9. Giorgi, F., Hurrell, J. W., Marinucci, M. R., and Beniston, M.: 1997, ‘Elevation Dependency of the Surface Climate Signal: A Model Study’, J. Climate 10, 288–296.Google Scholar
  10. Giorgi, F. and Mearns, L. O.: 1991, ‘Approaches to the Simulation of Regional Climate Change, A Review’, Reviews in Geophysics 29, 191–216.Google Scholar
  11. Hewitson, B. C. and Crane, R. G.: 1996, ‘Climate Downscaling: Techniques and Application’, Clim. Res. 7, 85–95.Google Scholar
  12. Hutchinson, M. F.: 1995, ‘Interpolating Mean Rainfall Using Thin Plate Smoothing Splines’, Int. J. Geogr. Inf. Syst. 9, 385–403.Google Scholar
  13. IPCC (Intergovernmental Panel on Climate Change): 1996, ‘Climate Change 1995: The Science of Climate Change, Contribution of Working Group I to the Second Assessment Report of the Intergovernmental Panel on Climate Change’, in Houghton, J. T., Meira Filho, L. G., Callander, B. A., Harris, N., Kattenberg A., and Maskell, K. (eds.), WMO/UNEP, Cambridge University Press, 572 pp.Google Scholar
  14. IPCC: 2001, ‘Climate Change 2001: The Scientific Basis’, Houghton, J. T. and Ding, Y. (eds.), Cambridge, Cambridge UP.Google Scholar
  15. Kidson, J. W. and Thompson, C. S.: 1998, ‘A Comparison of Statistical and Model-Based Downscaling Techniques for Estimating Local Climate Variations’, J. Climate 11, 735–753.Google Scholar
  16. Kim, J.: 2001, ‘A Nested Modeling Study of Elevation-Dependent Climate Change Signals in California Induced by Increased Atmospheric CO2’, Geophys. Res. Lett. 28, 2951.Google Scholar
  17. Kim, J., Miller, N. L., Farrara, J. D., and Hong, S.-Y.: 2000, ‘A Seasonal Precipitation and Stream Flow Hindcast and Prediction Study in the Western United States during the 1997/98 Winter Season Using a Dynamic Downscaling System’, J. Hydrometeorology 1, 311–329.Google Scholar
  18. Koster, R. D., Suarez, M. J., and Heiser, M.: 1999, ‘Variance and Predictability of Precipitation at Seasonal-to-Interannual Timescales’, J. Hydrometeorology 1, 26–46.Google Scholar
  19. Lau, W. K.-M., Kim J. H., and Sud, Y.: 1996, ‘Intercomparison of Hydrologic Processes in AMIP GCMs’, Bull. Amer. Meteorol. Soc. 77, 2209–2226.Google Scholar
  20. Lettenmaier, D. P., Wood, A. W., Palmer, R. N., Wood, E. F., and Stakhiv, E. Z.: 1999, ‘Water Resources Implications of Global Warming: A U.S. Regional Perspective’, Clim. Change 43, 537–579.Google Scholar
  21. Leung, L. R. and Ghan, S. J.: 1999, ‘Pacific Northwest Climate Sensitivity Simulated by a Regional Climate Model Driven by a GCM. Part I: Control Simulations’, J. Climate 12, 2010–2030.Google Scholar
  22. Leung, L. R., Hamlet, A. F., Lettenmaier, D. P., and Kumar, A.: 1999, ‘Simulations of the ENSO Hydroclimate Signals in the Pacific Northwest Columbia River Basin’, Bull. Amer. Meteorol. Soc. 80, 2313–2329.Google Scholar
  23. Leung, L. R., Mearns, L. O., Giorgi, F., and Wilby, R. L.: 2003, ‘Regional Climate Research’, Bull. Amer. Meteorol. Soc. 84, 89–95.Google Scholar
  24. Leung, L. R., Qian, Y., Bian, X., Washington, W. M., Han, J., and Roads, J. O.: 2004, ‘Mid-Century Ensemble Regional Climate Change Scenarios for the Western United States’, Clim. Change 62, 75–113.Google Scholar
  25. Liang, X., Lettenmaier, D. P., Wood, E. F., and Burges, S. J.: 1994, ‘A Simple Hydrologically Based Model of Land Surface Water and Energy Fluxes for General Circulation Models’, J. Geophys. Res. 99, 14415–14428.Google Scholar
  26. Liang, X., Wood, E. F., and Lettenmaier, D. P.: 1996, ‘Surface Soil Moisture Parameterization of the VIC-2L Model: Evaluation and Modifications’, Global Plan. Change 13, 195–206.Google Scholar
  27. Liang, X., Wood, E. F., and Lettenmaier, D. P.: 1999, ‘Modeling Ground Heat Flux in Land Surface Parameterization Schemes, J. Geophys. Res. 104, 9581–9600.Google Scholar
  28. Livezey, R. E., Masutani, M., Leetmaa, A., Rui, H.-L., Ji, M., and Kumar, A.: 1997, ‘Teleconnective Response of the Pacific/North American Region Atmosphere to Large Central Equatorial Pacific SST Anomalies’, J. Climate 10, 1787–1820.Google Scholar
  29. Maurer, E. P., Wood, A. W., Adam, J. C., Lettenmaier, D. P., and Nijssen, B.: 2002, ‘A Long-Term Hydrologically-Based Data Set of Land Surface Fluxes and States for the Conterminous United States’, J. Climate 15, 3237–3251.Google Scholar
  30. Murphy, J.: 1999, ‘An Evaluation of Statistical and Dynamical Techniques for Downscaling Local Climate, J. Climate 12, 2256–2284.Google Scholar
  31. Nijssen, B., Lettenmaier, D. P., Liang, X., Wetzel, S. W., and Wood, E. F.: 1997, ‘Streamflow Simulation for Continental-Scale River Basins’, Water Resour. Res. 33, 711–724.Google Scholar
  32. Panofsky, H. A. and Brier, G. W.: 1968, ‘Some Applications of Statistics to Meteorology’, The Pennsylvania State University, University Park, 224 pp.Google Scholar
  33. Payne, J. T., Wood, A.W., Hamlet, A. F., Palmer, R. N., and Lettenmaier, D. P.: 2004, ‘Mitigating the Effects of Climate Change on the Water Resources of the Columbia River Basin, Clim. Change 62, 233–256.Google Scholar
  34. Shukla, J.: 1998, ‘Predictability in the Midst of Chaos: A Scientific Basis for Climate Forecasting’, Science 282, 728–731.Google Scholar
  35. Stern, P. C. and Easterling, W. E. (eds.): 1999, Making Climate Forecasts Matter, National Research Council Report, National Academy Press, Washington, D.C.Google Scholar
  36. VanRheenen, N. T., Wood, A. W., Palmer, R. N., and Lettenmaier, D. P.: 2004, ‘Potential Implications of PCM Climate Change Scenarios for California Hydrology and Water Resources’, Clim. Change 62, 257–281.Google Scholar
  37. Washington, W. M., Weatherly, J. W., Meehl, G. A., Semtner, A. J., Bettge, T.W., Craig, A. P., Strand, W. G., Arblaster, J., Wayland, V. B., James, R., and Zhang, Y.: 2000, ‘Parallel Climate Model (PCM) Control and Transient Simulations’, Clim. Dyn. 16, 755–774.Google Scholar
  38. Wilby, R. L., Hay, L. E., Gutowski, W. J. Jr., Arritt, R. W., Takle, E. S., Pan, Z., Leavesley, G. H., and Clark, M. P.: 2000, ‘Hydrological Responses to Dynamically and Statistically Downscaled Climate Model Output’, Geophys. Res. Lett. 27, 1199–1202.Google Scholar
  39. Wilby, R. L. and Wigley, T. M. L.: 1997, ‘Downscaling General Circulation Model Output: A Review of Methods and Limitations’, Prog. Phys. Geog. 21, 530–548.Google Scholar
  40. Wilby, R. L., Wigley, T. M. L., Conway, D., Jones, P. D., Hewistson, B. C., Main, J., and Wilks, D. S.: 1998, ‘Statistical Downscaling of General Circulation Model Output: A Comparison of Methods’, Water Resour. Res. 34, 2995–3008.Google Scholar
  41. Wood, A. W., Maurer, E. P., Kumar, A., and Lettenmaier, D. P.: 2002, ‘Long Range Experimental Hydrologic Forecasting for the Eastern U.S.’, J. Geophys. Res. 107 (D20), 4429.Google Scholar
  42. Yarnal, B., Lakhtakia, M. N., Yu, Z., White, R. A., Pollard, D., Miller, D. A., and Lapenta, W. M.: 2000, ‘A Linked Meteorological and Hydrological Model System: The Susquehanna River Basin Experiment (SRBEX)’, Glob. Plan. Change 25, 149–161.Google Scholar
  43. Zhu, C., Pierce, D. W., Barnett, T. P., Wood, A. W., and Lettenmaier, D. P.: 2004, ‘Evaluation of Hydrologically Relevant PCM Climate Variables and Large-Scale Variability over the Continental U.S.’, Clim. Change 62, 45–74.Google Scholar

Copyright information

© Kluwer Academic Publishers 2004

Authors and Affiliations

  • A. W. Wood
    • 1
  • L. R. Leung
    • 2
  • V. Sridhar
    • 1
  • D. P. Lettenmaier
    • 1
  1. 1.Department of Civil and Environmental EngineeringUniversity of WashingtonSeattleU.S.A
  2. 2.U.S. Department of Energy Pacific Northwest National LaboratoryRichlandU.S.A

Personalised recommendations