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Advances in Meteorology
Volume 2016, Article ID 7463963, 12 pages
http://dx.doi.org/10.1155/2016/7463963
Research Article

Comparison of Three Statistical Downscaling Methods and Ensemble Downscaling Method Based on Bayesian Model Averaging in Upper Hanjiang River Basin, China

1State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan 430072, China
2College of Tourism Culture and Geographical Science, Huanggang Normal University, Huanggang 438000, China
3Shanghai Branch of Yangtze River Water Resources Protection Bureau, Shanghai 200120, China

Received 25 June 2015; Revised 29 September 2015; Accepted 11 October 2015

Academic Editor: Alexander Gelfan

Copyright © 2016 Jiaming Liu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. C.-Y. Xu, “Climate change and hydrologic models: a review of existing gaps and recent research developments,” Water Resources Management, vol. 13, no. 5, pp. 369–382, 1999. View at Publisher · View at Google Scholar · View at Scopus
  2. J. Sun and H. Chen, “A statistical downscaling scheme to improve global precipitation forecasting,” Meteorology and Atmospheric Physics, vol. 117, no. 3, pp. 87–102, 2012. View at Publisher · View at Google Scholar
  3. S. L. Grotch and M. C. MacCracken, “The use of general circulation models to predict regional climatic change,” Journal of Climate, vol. 4, no. 3, pp. 286–303, 1991. View at Publisher · View at Google Scholar
  4. R. L. Wilby and T. M. L. Wigley, “Downscaling general circulation model output: a review of methods and limitations,” Progress in Physical Geography, vol. 21, no. 4, pp. 530–548, 1997. View at Google Scholar · View at Scopus
  5. R. L. Wilby, L. E. Hay, and G. H. Leavesley, “A comparison of downscaled and raw GCM output: implications for climate change scenarios in the San Juan River Basin, Colorado,” Journal of Hydrology, vol. 225, no. 1-2, pp. 67–91, 1999. View at Publisher · View at Google Scholar · View at Scopus
  6. I. Hanssen-Bauer, C. Achberger, R. E. Benestad, D. Chen, and E. J. Førland, “Statistical downscaling of climate scenarios over Scandinavia,” Climate Research, vol. 29, no. 3, pp. 255–268, 2005. View at Publisher · View at Google Scholar · View at Scopus
  7. H. J. Fowler, S. Blenkinsop, and C. Tebaldi, “Linking climate change modelling to impacts studies: recent advances in downscaling techniques for hydrological modelling,” International Journal of Climatology, vol. 27, no. 12, pp. 1547–1578, 2007. View at Publisher · View at Google Scholar · View at Scopus
  8. E. Zorita and H. von Storch, “The analog method as a simple statistical downscaling technique: comparison with more complicated methods,” Journal of Climate, vol. 12, no. 8, pp. 2474–2489, 1999. View at Google Scholar · View at Scopus
  9. Y. B. Dibike and P. Coulibaly, “Hydrologic impact of climate change in the Saguenay watershed: comparison of downscaling methods and hydrologic models,” Journal of Hydrology, vol. 307, no. 1–4, pp. 145–163, 2005. View at Publisher · View at Google Scholar · View at Scopus
  10. M. S. Khan, P. Coulibaly, and Y. Dibike, “Uncertainty analysis of statistical downscaling methods,” Journal of Hydrology, vol. 319, no. 1–4, pp. 357–382, 2006. View at Publisher · View at Google Scholar · View at Scopus
  11. D. J. Reid, “Combining three estimates of gross domestic product,” Economica, vol. 35, no. 140, pp. 431–444, 1968. View at Publisher · View at Google Scholar
  12. J. M. Bates and C.W. J. Granger, “The combination of forecasts,” Operational Research Quarterly, vol. 20, no. 4, pp. 451–468, 1969. View at Publisher · View at Google Scholar · View at Scopus
  13. J. P. Dickinson, “Some statistical results in the combination of forecasts,” Operational Research Quarterly, vol. 24, no. 2, pp. 253–260, 1973. View at Publisher · View at Google Scholar · View at Scopus
  14. A. Y. Shamseldin, K. M. O'Connor, and G. C. Liang, “Methods for combining the outputs of different rainfall-runoff models,” Journal of Hydrology, vol. 197, no. 1–4, pp. 203–229, 1997. View at Publisher · View at Google Scholar · View at Scopus
  15. K. P. Georgakakos, D.-J. Seo, H. Gupta, J. Schaake, and M. B. Butts, “Towards the characterization of streamflow simulation uncertainty through multimodel ensembles,” Journal of Hydrology, vol. 298, no. 1–4, pp. 222–241, 2004. View at Publisher · View at Google Scholar · View at Scopus
  16. N. K. Ajami, Q. Duan, H. Moradkhani, and S. Sorooshian, “Recursive Bayesian model combination for streamflow forecasting,” in Proceedings of the American Meteorological Society Meeting, San Diego, Calif, USA, January 2005.
  17. N. K. Ajami, Q. Duan, X. Gao, and S. Sorooshian, “Multimodel combination techniques for analysis of hydrological simulations: application to distributed model intercomparison project results,” Journal of Hydrometeorology, vol. 7, no. 4, pp. 755–768, 2006. View at Publisher · View at Google Scholar
  18. N. K. Ajami, Q. Duan, and S. Sorooshian, “An integrated hydrologic Bayesian multimodel combination framework: confronting input, parameter, and model structural uncertainty in hydrologic prediction,” Water Resources Research, vol. 43, no. 1, Article ID W01403, 2007. View at Publisher · View at Google Scholar · View at Scopus
  19. D. Madigan and A. E. Raftery, “Model selection and accounting for model uncertainty in graphical models using Occam's window,” Journal of the American Statistical Association, vol. 89, no. 428, pp. 1535–1546, 1994. View at Publisher · View at Google Scholar
  20. A. E. Raftery, “Bayesian model selection in social research,” Sociological Methodology, vol. 25, pp. 111–163, 1995. View at Publisher · View at Google Scholar
  21. D. Draper, “Assessment and propagation of model uncertainty,” Journal of the Royal Statistical Society B. Methodological, vol. 57, no. 1, pp. 45–97, 1995. View at Google Scholar · View at MathSciNet
  22. C. Fernández, E. Ley, and M. F. J. Steel, “Benchmark priors for Bayesian model averaging,” Journal of Econometrics, vol. 100, no. 2, pp. 381–427, 2001. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  23. K. Y. Yeung, R. E. Bumgarner, and A. E. Raftery, “Bayesian model averaging: development of an improved multi-class, gene selection and classification tool for microarray data,” Bioinformatics, vol. 21, no. 10, pp. 2394–2402, 2005. View at Publisher · View at Google Scholar · View at Scopus
  24. B. A. Wintle, M. A. McCarthy, C. T. Volinsky, and R. P. Kavanagh, “The use of bayesian model averaging to better represent uncertainty in ecological models,” Conservation Biology, vol. 17, no. 6, pp. 1579–1590, 2003. View at Publisher · View at Google Scholar · View at Scopus
  25. K. H. Morales, J. G. Ibrahim, C.-J. Chen, and L. M. Ryan, “Bayesian model averaging with applications to benchmark dose estimation for arsenic in drinking water,” Journal of the American Statistical Association, vol. 101, no. 473, pp. 9–17, 2006. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  26. G. Koop and L. Tole, “Measuring the health effects of air pollution: to what extent can we really say that people are dying from bad air?” Journal of Environmental Economics and Management, vol. 47, no. 1, pp. 30–54, 2004. View at Publisher · View at Google Scholar · View at Scopus
  27. A. E. Raftery, F. Balabdaoui, T. Gneiting, and M. Polakowski, “Using Bayesian model averaging to calibrate forecast ensembles,” Tech. Rep. 440, Department of Statistics, University of Washington, 2003. View at Google Scholar
  28. V. Viallefont, A. E. Raftery, and S. Richardson, “Variable selection and Bayesian model averaging in case-control studies,” Statistics in Medicine, vol. 20, no. 21, pp. 3215–3230, 2001. View at Publisher · View at Google Scholar · View at Scopus
  29. J. A. Hoeting, D. Madigan, A. E. Raftery, and C. T. Volinsky, “Bayesian model averaging: a tutorial,” Statistical Science, vol. 14, no. 4, pp. 382–417, 1999. View at Publisher · View at Google Scholar · View at Scopus
  30. “Bayesian model averaging and model search strategies,” in Bayesian Statistics, M. A. Clyde, J. M. Bernardo, A. P. Dawid, J. O. Berger, and A. F. M. Smith, Eds., vol. 6, pp. 157–185, Oxford University Press, Oxford, UK, 1999.
  31. A. E. Raftery and Y. Zheng, “Discussion: performance of bayesian model averaging,” Journal of the American Statistical Association, vol. 98, no. 464, pp. 931–938, 2003. View at Publisher · View at Google Scholar · View at Scopus
  32. S. P. Neuman, “Maximum likelihood Bayesian averaging of uncertain model predictions,” Stochastic Environmental Research and Risk Assessment, vol. 17, no. 5, pp. 291–305, 2003. View at Publisher · View at Google Scholar · View at Scopus
  33. Q. Duan, N. K. Ajami, X. Gao, and S. Sorooshian, “Multi-model ensemble hydrologic prediction using Bayesian model averaging,” Advances in Water Resources, vol. 30, no. 5, pp. 1371–1386, 2007. View at Publisher · View at Google Scholar · View at Scopus
  34. L. Zhang, X. Chen, X. Zhang, and X. Song, “Comparative study on streamflow simulation in small and middle watersheds via VIC model and SWAT model,” Yangtze River Resources and Environment, vol. 18, no. 8, pp. 745–752, 2009. View at Google Scholar
  35. H. Yang, B. Wang, and B. Wang, “Reducing biases in regional climate downscaling by applying Bayesian model averaging on large-scale forcing,” Climate Dynamics, vol. 39, no. 9-10, pp. 2523–2532, 2012. View at Publisher · View at Google Scholar · View at Scopus
  36. S. Tripathi, V. V. Srinivas, and R. S. Nanjundiah, “Downscaling of precipitation for climate change scenarios: a support vector machine approach,” Journal of Hydrology, vol. 330, no. 3-4, pp. 621–640, 2006. View at Publisher · View at Google Scholar · View at Scopus
  37. X. Y. Yu and S.-Y. Liong, “Forecasting of hydrologic time series with ridge regression in feature space,” Journal of Hydrology, vol. 332, no. 3-4, pp. 290–302, 2007. View at Publisher · View at Google Scholar · View at Scopus
  38. L. Yaoming, Z. Qiang, and C. Deliang, “Stochastic modeling of daily precipitation in China,” Journal of Geographical Sciences, vol. 14, no. 4, pp. 417–426, 2004. View at Publisher · View at Google Scholar
  39. Y. Liao, “Change of parameters of BCC/RCG-WG for daily non-precipitation variables in China: 1951–1978 and 1979–2007,” Journal of Geographical Sciences, vol. 23, no. 4, pp. 579–594, 2013. View at Publisher · View at Google Scholar · View at Scopus
  40. R. L. Wilby, C. W. Dawson, and E. M. Barrow, “SDSM—a decision support tool for the assessment of regional climate change impacts,” Environmental Modelling and Software, vol. 17, no. 2, pp. 147–159, 2002. View at Google Scholar · View at Scopus
  41. H. Chen, J. Guo, W. Xiong, S. Guo, and C. Xu, “Downscaling GCMs using the Smooth Support Vector Machine method to predict daily precipitation in the Hanjiang Basin,” Advances in Atmospheric Sciences, vol. 27, no. 2, pp. 274–284, 2010. View at Publisher · View at Google Scholar
  42. V. N. Vapnik, The Nature of Statistical Learning Theory, Springer-Verlag, New York, NY, USA, 1995. View at Publisher · View at Google Scholar · View at MathSciNet
  43. V. N. Vapnik, Statistical Learning Theory, Wiley, New York, NY, USA, 1998.
  44. S. Ghosh and P. P. Mujumdar, “Statistical downscaling of GCM simulations to streamflow using relevance vector machine,” Advances in Water Resources, vol. 31, no. 1, pp. 132–146, 2008. View at Publisher · View at Google Scholar · View at Scopus
  45. A. Anandhi, V. V. Srinivas, R. S. Nanjundiah, and D. N. Kumar, “Downscaling precipitation to river basin in India for IPCC SRES scenarios using support vector machine,” International Journal of Climatology, vol. 28, no. 3, pp. 401–420, 2008. View at Publisher · View at Google Scholar · View at Scopus
  46. J. A. K. Suykens and J. Vandewalle, “Least squares support vector machine classifiers,” Neural Processing Letters, vol. 9, no. 3, pp. 293–300, 1999. View at Publisher · View at Google Scholar · View at Scopus
  47. C. Harpham and R. L. Wilby, “Multi-site downscaling of heavy daily precipitation occurrence and amounts,” Journal of Hydrology, vol. 312, no. 1–4, pp. 235–255, 2005. View at Publisher · View at Google Scholar · View at Scopus
  48. R. L. Wilby and M. D. Dettinger, “Streamflow changes in the Sierra Nevada, California, simulated using a statistically downscaled general circulation model scenario of climate change,” in Linking Climate Change to Land Surface Change, vol. 6 of Advances in Global Change Research, pp. 99–121, Springer, Dordrecht, The Netherlands, 2000. View at Publisher · View at Google Scholar
  49. F. Wetterhall, S. Halldin, and C. Y. Xu, “Seasonality properties of four statistical-downscaling methods in central Sweden,” Theoretical and Applied Climatology, vol. 87, no. 1, pp. 123–137, 2007. View at Publisher · View at Google Scholar
  50. J. Schuol, K. C. Abbaspour, R. Srinivasan, and H. Yang, “Estimation of freshwater availability in the West African sub-continent using the SWAT hydrologic model,” Journal of Hydrology, vol. 352, no. 1-2, pp. 30–49, 2008. View at Publisher · View at Google Scholar · View at Scopus
  51. D. L. Ficklin, Y. Luo, E. Luedeling, and M. Zhang, “Climate change sensitivity assessment of a highly agricultural watershed using SWAT,” Journal of Hydrology, vol. 374, no. 1-2, pp. 16–29, 2009. View at Publisher · View at Google Scholar · View at Scopus
  52. S. Peng and J. Xu, “Comparative study on reference crop’s evapotranspiration calculation methods,” Journal of Irrigation and Drainage, vol. 23, no. 6, pp. 5–9, 2004 (Chinese). View at Google Scholar
  53. C.-Y. Xu, L. Gong, T. Jiang, D. Chen, and V. P. Singh, “Analysis of spatial distribution and temporal trend of reference evapotranspiration and pan evaporation in Changjiang (Yangtze River) catchment,” Journal of Hydrology, vol. 327, no. 1-2, pp. 81–93, 2006. View at Publisher · View at Google Scholar · View at Scopus
  54. G. Pang, S. Peng, J. Ding, J. Xu, J. Cui, and Z. Wei, “Experimental study on the water demand laws for lawns in southern climate regions,” Journal of Hohai University (Natural Science), vol. 37, no. 2, pp. 143–146, 2009 (Chinese). View at Google Scholar