Stochastic simulation of nonstationary oscillation hydroclimatic processes using empirical mode decomposition

Water Resources Research

Volumn 48, Issue 2, 2012, Pages

  Lee, T ^a   Ouarda, T B M J ^b,c  

^a Gyeongsang National University   (South Korea)

^b KHALIFA UNIVERSITY   (United Arab Emirates)

^c CENTRE EAU TERRE ENVIRONNEMENT   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

CLIMATIC VARIATION; DATA SERIES; DATA SETS; EMPIRICAL MODE DECOMPOSITION; EXOGENOUS VARIABLES; HIGH POTENTIAL; HYDROCLIMATIC; K-NEAREST NEIGHBORS; NAO INDEX; NON-PARAMETRIC; NONSTATIONARY; NORTH ATLANTIC OSCILLATIONS; QUEBEC , CANADA; RESAMPLING; SIMULATION TECHNIQUE; STATISTICAL CHARACTERISTICS; STOCHASTIC SIMULATION MODEL; STOCHASTIC SIMULATION TECHNIQUE; STOCHASTIC SIMULATIONS; STOCHASTIC TIME SERIES; SYNTHETIC DATASETS; TRIGONOMETRIC FUNCTIONS;

ATMOSPHERIC PRESSURE; SIGNAL PROCESSING; STOCHASTIC MODELS;

COMPUTER SIMULATION;

BOOTSTRAPPING; CLIMATE EFFECT; CLIMATE VARIATION; DATA SET; DECOMPOSITION; EMPIRICAL ANALYSIS; NORTH ATLANTIC OSCILLATION; NUMERICAL MODEL; OBSERVATIONAL METHOD; STOCHASTICITY; STREAMFLOW;

CANADA; ROMAINE RIVER;

EID: 84863143586     PISSN: 00431397     EISSN: None     Source Type: Journal    
DOI: 10.1029/2011WR010660     Document Type: Article

References (59)

1. Ahn, J. H., and H. S. Kim (2005), Nonlinear modeling of El Nino/Southern Oscillation index, J. Hydrol. Eng., 10(1), 8-15. (Pubitemid 40102818)
2. Barnes, A. E. (2007), A tutorial on complex seismic trace analysis, Geophysics, 72(6), W33-W43. (Pubitemid 350104349)
3. Bartolini, P., J. D. Salas, and J. T. B. Obeysekera (1988), Multivariate periodic ARMA(1, 1) processes, Water Resour. Res., 24(8), 1237-1246.
4. Brockwell, P. J., and R. A. Davis (2003), Introduction to Time Series and Forecasting, 2nd ed., 456 pp., Springer, Berlin.
5. Chebaane, M., J. D. Salas, and D. C. Boes (1995), Product periodic auto-regressive processes for modeling intermittent monthly streamflows, Water Resour. Res., 31(6), 1513-1518. (Pubitemid 26440461)
6. Chiew, F. H. S., M. C. Peel, G. E. Amirthanathan, and G. G. S. Pegram (2005), Identification of oscillations in historical global streamflow data using empirical mode decomposition, in Regional Hydrological Impacts of Climate Change: Hydroclimatic Variability, edited by S. Franks et al., pp. 53-62, IAHS Press, Wallingford, UK. (Pubitemid 40538540)
7. Cullen, H. M., A. Kaplan, P. A. Arkin, and P. B. Demenocal (2002), Impact of the North Atlantic Oscillation on Middle Eastern climate and stream-flow, Clim. Change, 55(3), 315-338. (Pubitemid 35249930)
8. Dettinger, M. D., M. Ghil, and C. L. Keppenne (1995), Interannual and interdecadal variability in United States surface air temperatures, 1910-87, Clim. Change, 31(1), 35-66. (Pubitemid 26444612)
9. Frederick, W. K. (2009), Hilbert Transforms, 896 pp., Cambridge Univ. Press, Cambridge, U. K.
10. Gamiz-Fortis, S., D. Pozo-Vazquez, R. M. Trigo, and Y. Castro-Diez (2008), Quantifying the predictability of winter river flow in Iberia. Part II: Seasonal predictability, J. Clim., 21(11), 2503-2518.
11. Garrido, J., and J. A. Garcia (1992), Periodic signals in Spanish monthly precipitation data, Theor. Appl. Climatol., 45(2), 97-106.
12. Ghil, M., and R. Vautard (1991), Interdecadal oscillations and the warming trend in global temperature time series, Nature, 350(6316), 324-327. (Pubitemid 21912219)
13. Han, J., and M. van der Baan (2011), Empirical mode decomposition and robust seismic attribute analysis, paper presented at Recovery 2011, Joint Annual Conventions, Canadian Society of Exploration Geophysicists (CSEG), Calgary, Alberta, Canada.
14. Hipel, K. W., and A. I. McLeod (1996), Time Series Modelling of Water Resources and Environmental Systems, Elsevier, Amsterdam.
15. Huang, N. E., and Z. H. Wu (2008), A review on Hilbert-Huang transform: Method and its applications to geophysical studies, Rev. Geophys., 46, RG2006, doi:10.1029/2007RG000228.
16. Huang, N. E., Z. Shen, S. R. Long, M. L. C. Wu, H. H. Shih, Q. N. Zheng, N. C. Yen, C. C. Tung, and H. H. Liu (1998), The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis, Proc. R. Soc. London, Ser. A, 454(1971), 903-995. (Pubitemid 128733183)
17. Hurrell, J. W. (1995), Decadal trends in the North Atlantic Oscillation: Regional temperatures and precipitation, Science, 269(5224), 676-679. (Pubitemid 26439360)
18. Jones, P. D., T. Jonsson, and D. Wheeler (1997), Extension to the North Atlantic Oscillation using early instrumental pressure observations from Gibraltar and south-west Iceland, Int. J. Climatol., 17(13), 1433-1450.
19. Kanwal, R. P. (1996), Linear Integral Equations, 332 pp., Birkhäuser Boston, Boston, Mass.
20. Katz, R. W., and M. B. Parlange (1996), Mixtures of stochastic processes: Application to statistical downscaling, Clim. Res., 7(2), 185-193.
21. Kijewski-Correa, I., and A. Kareem (2007), Performance of wavelet transform and empirical mode decomposition in extracting signals embedded in noise, J. Eng. Mech., 133(7), 849-852. (Pubitemid 47007373)
22. Kwon, H. H., U. Lall, and A. F. Khalil (2007), Stochastic simulation model for nonstationary time series using an autoregressive wavelet decomposition: Applications to rainfall and temperature, Water Resour. Res., 43, W05407, doi:10.1029/2006WR005258. (Pubitemid 46998211)
23. Lall, U., and A. Sharma (1996), A nearest neighbor bootstrap for resampling hydrologic time series, Water Resour. Res., 32(3), 679-693.
24. Lall, U., B. Rajagopalan, and D. G. Tarboton (1996), A nonparametric wet/dry spell model for resampling daily precipitation, Water Resour. Res., 32(9), 2803-2823.
25. Lee, T., and T. B. M. J. Ouarda (2010), Long-term prediction of precipitation and hydrologic extremes with nonstationary oscillation processes, J. Geophys. Res., 115, D13107, doi:10.1029/2009JD012801.
26. Lee, T., and T. B. M. J. Ouarda (2011), Prediction of climate nonstationary oscillation processes with empirical mode decomposition, J. Geophys. Res., 116, D06107, doi:10.1029/2010JD015142.
27. Lee, T., J. D. Salas, and J. Prairie (2010), An enhanced nonparametric streamflow disaggregation model with genetic algorithm, Water Resour. Res., 46, W08545, doi:10.1029/2009WR007761.
28. Lee, T. S. (2008), Stochastic simulation of hydrologic data based on non-parametric approaches, PhD dissertation, 346 pp., Colo. State Univ., Fort Collins.
29. Li, J. P., and J. X. L. Wang (2003), A new North Atlantic Oscillation index and its variability, Adv. Atmos. Sci., 20(5), 661-676. (Pubitemid 38306127)
30. Lorenz, E. N. (1963), Deterministic nonperiodic flow, J. Atmos. Sci., 20, 130-141.
31. McMahon, T. A., A. S. Kiem, M. C. Peel, P. W. Jordan, and G. G. S. Pegram (2008), A new approach to stochastically generating six-monthly rainfall sequences based on empirical mode decomposition, J. Hydro-meteorol., 9(6), 1377-1389.
32. Nelles, O. (2001), Nonlinear System Identification: From Classical Approaches to Neural Networks and Fuzzy Models, 785 pp., Springer, Heidelberg, Germany.
33. Ouarda, T. B. M. J., J. W. Labadie, and D. G. Fontane (1997), Indexed sequential hydrologic modeling for hydropower capacity estimation, J. Am. Water Resour. Assoc., 33(6), 1337-1349. (Pubitemid 28052130)
34. Peel, M. C., T. A. McMahon, and G. G. S. Pegram (2009), Assessing the performance of rational spline-based empirical mode decomposition using a global annual precipitation data set, Proc. R. Soc. A, 465(2106), 1919-1937.
35. Pegram, G. G. S., M. C. Peel, and T. A. McMahon (2008), Empirical mode decomposition using rational splines: An application to rainfall time series, Proc. R. Soc. London, Ser. A, 464(2094), 1483-1501. (Pubitemid 351553758)
36. Prairie, J. R., B. Rajagopalan, T. J. Fulp, and E. A. Zagona (2005), Statistical nonparametric model for natural salt estimation, J. Environ. Eng., 131(1), 130-138. (Pubitemid 40098636)
37. Prairie, J. R., B. Rajagopalan, T. J. Fulp, and E. A. Zagona (2006), Modified K-NN model for stochastic streamflow simulation, J. Hydrol. Eng., 11(4), 371-378.
38. Press, W., S. Teukolsky, T. William, and B. Flannery (2002), Numerical Recipes in C, 2nd ed., Cambridge Univ. Press, Cambridge, U. K.
39. Rajagopalan, B., and U. Lall (1999), A k-nearest-neighbor simulator for daily precipitation and other weather variables, Water Resour. Res., 35(10), 3089-3101. (Pubitemid 29461972)
40. Rogers, J. C. (1990), Patterns of low frequency monthly sea level pressure variability (1899-1986) and associated wave cyclone frequencies, J. Clim., 3(12), 1364-1379.
41. Rössler, O. E. (1976), An equation for continuous chaos, Phys. Lett. A, 57(5), 397-398.
42. Rössler, O. E. (1995), An introduction to chaos, Int. J. Intell. Syst., 10(1), 5-13.
43. Salas, J. D. (1993), Analysis and modeling of hydrologic time series, in Handbook of Hydrology, edited by D. R. Maidment, pp. 19.11-19.72, McGraw-Hill, New York.
44. Salas, J. D., and D. C. Boes (1980), Shifting level modeling of hydrologic series, Adv. Water Resour., 3, 59-63.
45. Salas, J. D., and T. Lee (2010), Non-parametric simulation of single site seasonal streamflows, J. Hydrol. Eng., 15(4), 284-296.
46. Salas, J. D., J. W. Delleur, V. Yevjevich, and W. L. Lane (1980), Applied Modeling of Hydrologic Time Series, 484 pp., Water Resour. Publ., Littleton, Colo.
47. Schlesinger, M. E., and N. Ramankutty (1994), Low-frequency oscillation: Reply, Nature, 372(6506), 508-509.
48. Silverman, B. W. (1986), Density Estimation for Statistics and Data Analysis: Monographs on Statistics and Applied Probability, 175 pp., Chapman and Hall, London.
49. Stedinger, J. R., and M. R. Taylor (1982a), Synthetic streamflow generation: 1. Model verification and validation, Water Resour. Res., 18(4), 909-918. (Pubitemid 123423)
50. Stedinger, J. R., and M. R. Taylor (1982b), Synthetic streamflow generation: 2. Effect of parameter uncertainty, Water Resour. Res., 18(4), 919-924. (Pubitemid 13262461)
51. Stedinger, J. R., D. P. Lettenmaier, and R. M. Vogel (1985), Multisite ARMA(1,1) and disaggregation models for annual streamflow generation, Water Resour. Res., 21(4), 497-509. (Pubitemid 17269217)
52. Sveinsson, O. G. B., J. D. Salas, D. C. Boes, and R. A. Pielke (2003), Modeling the dynamics of long-term variability of hydroclimatic processes, J. Hydrometeorol., 4(3), 489-505. (Pubitemid 36906484)
53. Vogel, R. M., and A. L. Shallcross (1996), The moving blocks bootstrap versus parametric time series models, Water Resour. Res., 32(6), 1875-1882.
54. Wilks, D. S. (1997), Resampling hypothesis tests for autocorrelated fields, J. Clim., 10(1), 65-82. (Pubitemid 28000974)
55. Wu, Z. H., and N. E. Huang (2004), A study of the characteristics of white noise using the empirical mode decomposition method, Proc. R. Soc. London, Ser. A, 460(2046), 1597-1611. (Pubitemid 38703409)
56. Wu, Z. H., and N. E. Huang (2005), Statistical significance test of intrinsic mode functions, in Hilbert-Huang Transform and Its Applications, edited by N. E. Huang and S. P. Shen, pp. 125-148, World Sci., Hackensack, N. J.
57. Wu, Z. H., and N. E. Huang (2009), Ensemble empirical mode decomposition: A noise-assisted data analysis method, Adv. Adaptive Data Anal., 1(1), 1-41.
58. Wu, Z. H., N. E. Huang, S. R. Long, and C. K. Peng (2007), On the trend, detrending, and variability of nonlinear and nonstationary time series, Proc. Natl. Acad. Sci. U. S. A., 104(38), 14,889-14,894. (Pubitemid 47619564)
59. Xie, L., L. J. Pietrafesa, and K. J. Wu (2002), Interannual and decadal variability of landfalling tropical cyclones in the southeast coastal states of the United States, Adv. Atmos. Sci., 19(4), 677-686.