A hidden Markov model combined with climate indices for multidecadal streamflow simulation

Water Resources Research

Volumn 50, Issue 10, 2014, Pages 7836-7846

  Bracken, C ^a,b   Rajagopalan, B ^a,c   Zagona, E ^a  

^a University of Colorado at Boulder   (United States)

^b U S BUREAU OF RECLAMATION   (United States)

^c UNIVERSITY OF COLORADO   (United States)

Author keywords

climate indicies; hidden Markov; logistic regression; streamflow; time series; Upper Colorado

Indexed keywords

DIGITAL STORAGE; HIDDEN MARKOV MODELS; LOGISTIC REGRESSION; STOCHASTIC MODELS; STOCHASTIC SYSTEMS; STREAM FLOW; TIME SERIES;

CLIMATE INDICIES; HIDDEN MARKOV; MULTIDECADAL VARIABILITY; MULTINOMIAL LOGISTIC REGRESSION MODELS; STOCHASTIC SIMULATIONS; STOCHASTIC TIME SERIES MODELS; STREAMFLOW SIMULATIONS; UPPER COLORADO;

CLIMATE MODELS;

MARKOV CHAIN; NUMERICAL MODEL; RISK FACTOR; STOCHASTICITY; STREAMFLOW; TIME SERIES ANALYSIS; WATER PLANNING;

COLORADO BASIN [NORTH AMERICA];

EID: 84911437846     PISSN: 00431397     EISSN: 19447973     Source Type: Journal    
DOI: 10.1002/2014WR015567     Document Type: Article

References (63)

1. Akaike, H., (1974), A new look at the statistical model identification, IEEE Trans. Autom. Control, 19 (6), 716-723.
2. Akintuʇ, B., and, P. F. Rasmussen, (2005), A Markov switching model for annual hydrologic time series, Water Resour. Res., 41, W09424, doi: 10.1029/2004WR003605.
3. Al-Saleh, J. A., and, S. K. Agarwal, (2007), Finite mixture of gamma distributions: A conjugate prior, Comput. Stat. Data Anal., 51 (9), 4369-4378.
4. Baum, L. E., T. Petrie, G. Soules, and, N. Weiss, (1970), A maximization technique occurring in the statistical analysis of probabilistic functions of Markov chains, Ann. Math. Stat., 41 (1), 164-171.
5. Berliner, L. M., C. K. Wikle, and, N. Cressie, (2000), Long-lead prediction of Pacific SSTs via Bayesian dynamic modeling: EBSCOhost, J. Clim., 13 (22), 3953.
6. Boes, D. C., and, J. D. Salas, (1978), Nonstationarity of the mean and the Hurst phenomenon, Water Resour. Res., 14 (1), 135-143.
7. Chi, M., E. Neal, and, G. K. Young, (1973), Practical application of fractional Brownian Motion and noise to synthetic hydrology, Water Resour. Res., 9 (6), 1523-1533.
8. Colorado River Commission and Coauthors (1923), Colorado River Compact. Government Printing Office Rep. II, 5 pp.
9. Cooley, D., and, S. R. Sain, (2010), Spatial hierarchical modeling of precipitation extremes from a regional climate model, J. Agric. Biol. Environ. Stat., 15 (3), 381-402.
10. Cooley, D., D. Nychka, and, P. Naveau, (2012), Bayesian spatial modeling of extreme precipitation return levels.
11. Dempster, A. P., N. M. Laird, and, D. B. Rubin, (1977), Maximum likelihood from incomplete data via the EM algorithm, J. R. Stat. Soc., Ser. B, 39 (1), 1-38.
12. Evin, G., J. Merleau, and, L. Perreault, (2011), Two-component mixtures of normal, gamma, and Gumbel distributions for hydrological applications, Water Resour. Res., 47, W08525, doi: 10.1029/2010WR010266.
13. Forney, G. D., (1973), The viterbi algorithm, Proc. IEEE, 61 (3), 268-278.
14. Fortin, V., L. Perrault, and, J. Salas, (2004), Retrospective analysis and forecasting of streamflows using a shifting level model, J. Hydrol., 296 (1-4), 135-163.
15. Frühwirth-Schnatter, S., (2006), Finite Markov mixture modeling, in Finite Mixture and Markov Switching Models, pp. 301-318, Springer, N. Y.
16. Gelati, E., H. Madsen, and, D. Rosberg, (2010), Markov-switching model for nonstationary runoff conditioned on El Niño information, Water Resour. Res., 46, W02517, doi: 10.1029/2009WR007736.
17. Grantz, K., B. Rajagopalan, M. Clark, and, E. Zagona, (2005), A technique for incorporating large-scale climate information in basin-scale ensemble streamflow forecasts, Water Resour. Res., 41, W10410, doi: 10.1029/2004WR003467.
18. Greene, A. M., A. W. Robertson, and, S. Kirshner, (2008), Analysis of Indian monsoon daily rainfall on subseasonal to multidecadal time-scales using a hidden Markov model, Q. J. R. Meteorol. Soc., 134 (633), 875-887.
19. Greene, A. M., A. W. Robertson, P. Smyth, and, S. Triglia, (2011), Downscaling projections of Indian monsoon rainfall using a non-homogeneous hidden Markov model, Q. J. R. Meteorol. Soc., 137 (655), 347-359.
20. Guimarães, R., and, E. G. Santos, (2011), Principles of stochastic generation of hydrologic time series for reservoir planning and design: A case study, J. Hydrol. Eng., 16 (11), 891-898.
21. Hastie, T., R. Tibshirani, and Friedman (2002), The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer Series in Statistics, 2nd ed., Springer, N. Y.
22. Henley, B. J., M. A. Thyer, G. Kuczera, and, S. W. Franks, (2011), Climate-informed stochastic hydrological modeling: Incorporating decadal-scale variability using paleo data, Water Resour. Res., 47, W11509, doi: 10.1029/2010WR010034.
23. Hidalgo, H., (2003), ENSO and PDO effects on hydroclimatic variations of the Upper Colorado River Basin, J. Hydrometeorol., 4 (1), 5-23.
24. Hosking, J. R. M., (1984), Modeling persistence in hydrological time series using fractional differencing, Water Resour. Res., 20 (12), 1898-1908.
25. Hughes, J. P., and, P. Guttorp, (1994), Incorporating spatial dependence and atmospheric data in a model of precipitation, J. Appl. Meteorol., 33 (12), 1503-1515.
26. Hughes, J. P., P. Guttorp, and, S. P. Charles, (1999), A non-homogeneous hidden Markov model for precipitation occurrence, J. R. Stat. Soc., Ser. C, 48 (1), 15-30.
27. Hunter, T., G. Tootle, and, T. Piechota, (2006), Oceanic-atmospheric variability and western U.S. snowfall, Geophys. Res. Lett., 33, L13706, doi: 10.1029/2006GL026600.
28. Hurst, H. E., (1951), Long-term storage capacity of reservoirs, Trans. Am. Soc. Civ. Eng., 116, 770-808.
29. Jackson, B. B., (1975), Markov mixture models for drought lengths, Water Resour. Res., 11 (1), 64-74.
30. Khalil, A. F., H.-H. Kwon, U. Lall, and, Y. H. Kaheil, (2010), Predictive downscaling based on non-homogeneous hidden Markov models, Hydrol. Sci. J., 55 (3), 333-350.
31. Koutsoyiannis, D., (2002), The Hurst phenomenon and fractional Gaussian noise made easy, Hydrol. Sci. J., 47 (4), 573-595.
32. Koutsoyiannis, D., and, A. Montanari, (2007), Statistical analysis of hydroclimatic time series: Uncertainty and insights, Water Resour. Res., 43, W05429, doi: 10.1029/2006WR005592.
33. 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.
34. Kwon, H.-H., U. Lall, and, J. Obeysekera, (2008), Simulation of daily rainfall scenarios with interannual and multidecadal climate cycles for South Florida, Stochastic Environ. Res. Risk Assess., 23 (7), 879-896.
35. Lall, U., and, A. Sharma, (1996), A nearest neighbor bootstrap for resampling hydrologic time series, Water Resour. Res., 32 (3), 679-693.
36. Lohre, M., P. Sibbertsen, and, T. Könning, (2003), Modeling water flow of the Rhine River using seasonal long memory, Water Resour. Res., 39 (5), 1132, doi: 10.1029/2002WR001697.
37. Mares, C., I. Mares, H. Huebener, M. Mihailescu, U. Cubasch, and, P. Stanciu, (2014), A hidden Markov model applied to the daily spring precipitation over the Danube basin, Adv. Meteorol., 0142 (1), 1-11.
38. McCabe, G., J. Betancourt, and, H. Hidalgo, (2007), Associations of decadal to multidecadal sea-surface temperature variability with upper Colorado River Flow, J. Am. Water Resour. Assoc., 43, 183-192, doi: 10.1111/j.1752-1688.2007.00015.x.
39. McCullagh, J., and, P. Nelder, (1989), Generalized Linear Models, 2nd ed., Chapman and Hall, Boca Raton, Fla.
40. Mehrotra, R., and, A. Sharma, (2005), A nonparametric nonhomogeneous hidden Markov model for downscaling of multisite daily rainfall occurrences, J. Geophys. Res., 110, D16108, doi: 10.1029/2004JD005677.
41. Mesa, O. J., and, G. Poveda, (1993), The Hurst effect: The scale of fluctuation approach, Water Resour. Res., 29 (12), 3995-4002.
42. Nash, L., and, P. Gleick, (1991), Sensitivity of streamflow in the Colorado basin to climatic changes, J. Hydrol., 125 (3-4), 221-241.
43. Nowak, K., M. Hoerling, B. Rajagopalan, and, E. Zagona, (2012), Colorado River Basin hydroclimatic variability, J. Clim., 25 (12), 4389-4403.
44. Nowak, K. C., B. Rajagopalan, and, E. Zagona, (2011), Wavelet auto-regressive method (WARM) for multi-site streamflow simulation of data with non-stationary spectra, J. Hydrol., 410 (1), 1-12.
45. Ouarda, T., J. Labadie, and, D. Fontane, (1997), Indexed sequential hydrologic modeling for hydropower capacity estimation, J. Am. Water Resour. Assoc., 33, 1337-1349.
46. Piechota, T. C., and, J. A. Dracup, (1996), Drought and regional hydrologic variation in the United States: Associations with the El Niño-Southern Oscillation, Water Resour. Res., 32 (5), 1359-1373.
47. Prairie, J., K. Nowak, B. Rajagopalan, U. Lall, and, T. Fulp, (2008), A stochastic nonparametric approach for streamflow generation combining observational and paleoreconstructed data, Water Resour. Res., 44, W06423, doi: 10.1029/2007WR006684.
48. Robertson, A. W., S. Kirshner, and, P. Smyth, (2004), Downscaling of daily rainfall occurrence over Northeast Brazil using a hidden Markov model, J. Clim., 17 (22), 4407-4424.
49. Robinson, P. M., (1994), Semiparametric analysis of long-memory time series, Ann. Stat., 22 (01), 515-539.
50. Salas, J., and, D. Boes, (1980), Shifting level modelling of hydrologic series, Adv. Water Resour., 3 (2), 59-63.
51. Salas, J. D., J. D. A. Yevjevich, and, W. L. Lane, (1980), Applied Modeling of Hydrologic Time Series, Water Resour. Publ, Highlands Ranch, Colo.
52. Schliep, E. M., D. Cooley, S. R. Sain, and, J. A. Hoeting, (2010), A comparison study of extreme precipitation from six different regional climate models via spatial hierarchical modeling, Extremes, 13 (2), 219-239.
53. Schwarz, G., (1978), Estimating the dimension of a model, Ann. Stat., 6 (2), 461-464.
54. Stedinger, J. R., and, M. R. Taylor, (1982), Synthetic streamflow generation: 1. Model verification and validation, Water Resour. Res., 18 (4), 909-918.
55. Taqqu, M. S., V. Teverovsky, and, W. Willinger, (1995), Estimators for long-range dependence: An empirical study, Fractals, 03 (04), 785-798.
56. Thyer, M., and, G. Kuczera, (2000), Modeling long-term persistence in hydroclimatic time series using a hidden state Markov model, Water Resour. Res., 36 (11), 3301-3310.
57. Thyer, M., and, G. Kuczera, (2003), A hidden Markov model for modelling long-term persistence in multi-site rainfall time series 1. Model calibration using a Bayesian approach, J. Hydrol., 275 (1-2), 12-26.
58. Thyer, M., B. Renard, D. Kavetski, G. Kuczera, S. W. Franks, and, S. Srikanthan, (2009), Critical evaluation of parameter consistency and predictive uncertainty in hydrological modeling: A case study using Bayesian total error analysis, Water Resour. Res., 45, W00B14, doi: 10.1029/2008WR006825.
59. Timlsena, J., T. Piechota, G. Tootle, and, A. Singh, (2009), Associations of interdecadal/interannual climate variability and long-term Colorado River Basin streamflow, J. Hydrol., 365 (3-4), 289-301.
60. Wiper, M., D. R. Insua, and, F. Ruggeri, (2001), Mixtures of gamma distributions with applications, J. Comput. Graphical Stat., 10 (3), 440-454.
61. Yoo, J. H., A. W. Robertson, and, I.-S. Kang, (2010), Analysis of intraseasonal and interannual variability of the Asian summer monsoon using a hidden Markov model, J. Clim., 23 (20), 5498-5516.
62. Zucchini, W., (2009), Hidden Markov Models for Time Series: An Introduction Using R, CRC Press, Boca Raton, Fla.
63. Zucchini, W., and, P. Guttorp, (1991), A hidden Markov model for space-time precipitation, Water Resour. Res., 27 (8), 1917-1923.