Retrospective analysis and forecasting of streamflows using a shifting level model

Journal of Hydrology

Volumn 296, Issue 1-4, 2004, Pages 135-163

  Fortin, V ^a   Perreault, L ^a   Salas, J D ^b  

^a HYDRO QUÉBEC   (Canada)

^b Engineering Research Center and Hydraulics Laboratory   (United States)

Author keywords

Bayesian analysis; Forecasting; Gibbs sampling; Hidden markov chain; Shifting level; Stochastic hydrology

Indexed keywords

COMPUTER SIMULATION; DATA REDUCTION; HYDROLOGY; MARKOV PROCESSES; MATHEMATICAL MODELS; MONTE CARLO METHODS; PARAMETER ESTIMATION; WEATHER FORECASTING;

STOCHASTIC SIMULTION; STREAMFLOW DATA;

STREAM FLOW;

BAYESIAN ANALYSIS; FLOW MODELING; FORECASTING METHOD; PARAMETERIZATION; STOCHASTICITY; STREAMFLOW;

AFRICA; SENEGAL RIVER; SUB-SAHARAN AFRICA; WEST AFRICA;

EID: 3242723155     PISSN: 00221694     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jhydrol.2004.03.016     Document Type: Article

References (54)

1. Akaike H. A new look at the statistical model identification. IEEE Transactions on Automatic Control. AP-19:1974;716-723
2. Barreto G., de Andrade M. Bayesian Inference and Markov Chain Monte Carlo Methods Applied to Streamflow Forecasting. Proceedings Sixth International Conference on Probabilistic Methods Applied to Power Systems (PMAPS), Funchal. vol. 2:2000;. FOR-034
3. Bengio Y. Markovian Models for Sequential Data. Neural Computing Surveys. 2:1999;129-162
4. Bernardo J.M., Smith M.F.A. Bayesian Theory. 1994;Wiley, Chichester
5. Bras R.L., Rodrigues-Iturbe I. Random Functions and Hydrology. 1985;Addison-Wesley, Menlo Park, CA. 575 pp
6. Brooks, S.P., Gelman, A., 1998. General Methods for Monitoring Convergence of Iterative Simulations. Journal of Computational and Graphical statistics, 7, 434-455.
7. Christiansen B., 2003. Evidence for non-linear climate change: two stratospheric regimes and a regime shift. J. Climate, 16, 3681-3689.
8. Clements M.P., Hendry D.H. Forecasting Non-stationary Economic Time Series. 1999;MIT Press, Cambridge, MA
9. Curry K., Bras R.L. Theory and application of the multivariate broken line, disaggregation and monthly autoregressive streamflow generators for the Nile River. Technical Report 78-5. 1978;Ralph M. Parsons Lab., Mass. Inst. Of Technol, Cambridge
10. Dacco R., Satchell S. Why do Regime-switching models forecast so badly? Journal of Forecasting. 18:1999;1-16
11. van Dijk D., Franses P.H. Selecting a nonlinear time series model using weighted tests of equal forecast accuracy. Econometric Institute Report EI 2003-10. 2003;Econometric Institute, Erasmus University, Rotterdam
12. Dirac, P.A.M., 1958. The Principles of Quantum Mechanics (Forth Edition), Oxford U. Press.
13. Fortin V., Perreault L., Ondo J.-C., Evra R.-C. Bayesian long-term forecasting of annual flows with a shifting-level model. Proceedings of the Symposium on Managing the Extremes - Floods and Droughts. 2002;Environmental and Water Resources Institute of ASCE, Roanoke, VA
14. Gelfand A.E., Smith A.F.M. Sampling based approaches to calculating marginal densities. Journal of the American Statistical Association. 85:1990;398-409
15. Gelfand A.E., Dey D.K. Bayesian model choice: aymptotics and exact calculations. Journal of the Royal Statistical Society B. 56:1994;501-514
16. Geman S., Geman D. Stochastic relaxation, Gibbs distribution and the Bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence. 6:1984;721-741
17. Hipel K.W., McLeod A.I. Time Series Modelling of Water Resources and Environmental Systems. 1994;Elsevier, Amsterdam
18. Hubert P. The segmentation procedure as a tool for discrete modeling of hydrometeorological regimes. Stochastic Environmental Research and Risk Assessment. 14:2000;297-304
19. Hubert P., Carbonnel J.P., Chaouche A. Segmentation des séries hydrométéorologiques. Application à des séries de précipitations et de débits de l'Afrique de l'Ouest. Journal of Hydrology. 110:1989;349-367
20. Hurst H.E. Long-term storage capacity of reservoirs. Transactions of ASCE. 116:1951;770-779
21. Kehagias, A., 2004. A hidden Markov Model Segmentation procedure for hydrological and environmental times series, Stoch. Env. Res. and Risk Ass., 18(2), 117-130.
22. Klemes V. The Hurst phenomena: a puzzle? Water Resources Research. 10:(4):1974;675-688
23. Kuczera G., Parent É. Monte Carlo assessment of parameter uncertainty in conceptual catchment models: the Metropolis algorithm. Journal of Hydrology. 211:(1-4):1998;69-85
24. Loucks D.P., Stedinger J.R., Haith D.A. Water Resource Systems Planning and Analysis. 1981;Prentice Hall, Englewood Cliffs
25. Lu Z.-Q., Berliner L.M. Markov switching time series models with application to a daily runoff series. Water Resources Research. 35:(2):1999;523-534
26. Mandelbrot B.B., Wallis J.R. Computer experiments with fractional Gaussian noises, part 1, averages and variances. Water Resources Research. 5:(1):1969;228-241
27. Montanari A., Rosso R., Taqqu M.S. Fractionally differenced ARIMA models applied to hydrologic time series: identification, estimation and simulation. Water Resources Research. 33:1997;1035-1044
28. O'Connell R.T. A simple stochastic modeling of Hurst's law. International Symposium On Mathematic Models in Hydrology, Warsaw. 1971
29. Perreault L., Bernier J., Bobée B., Parent É. Bayesian change-point analysis in hydrometeorological time series, part 1, the normal model revisited. Journal of Hydrology. 235:2000;221-241
30. Perreault L., Bernier J., Bobée B., Parent É. Bayesian change-point analysis in hydrometeorological time series, part 2, comparison of change-point models and forecasting. Journal of Hydrology. 235:2000;242-263
31. Potter K.W., A Stochastic model of the Hurst phenomenon: nonstationarity in hydrologic processes. PhD Dissertation, John Hopkins University, 1976.
32. Quenouille M.H. Notes on bias in estimation. Biometrika. 43:1956;353-360
33. Ramsey J.B. If nonlinear models cannot forecast, what use are they? Studies in Nonlinear Dynamics and Econometrics. 1:1996;65-86
34. Rassam J.C., Fagherazzi L.D., Bobée B., Mathier L., Roy R., Carballada L. Beauharnois-Les Cèdres spillway: design flood study with stochastic approach. Final report to the Experts Committee. 1992;Hydro-Quebec, Montreal, Canada. 105 p
35. Rial J.A., Pielke R.A. Sr, Beniston M., Claussen M., Canadell J., Cox P., Held H., de Noblet-Ducoudré N., Prinn R., Reynolds J., Salas J.D. Nonlinearities, Feedbacks, and Critical Thresholds Within the Earth's Climate System. Climatic Change. 2004;. in press
36. Robert C. Discretization and MCMC convergence assessment. Lecture Notes 135. 1998;Springer, New York
37. Robert C., Casella G. Monte Carlo statistical methods. 1999;Springer, New York
38. Rodriguez-Iturbe I., Mejia J.M., Dawdy D.R. Streamflow simulation. 1, A new look at Markovian models, fractional Gaussian noise and crossing theory; 2, the broken line process as a potential model for hydrologic simulation. Water Resourses Research. 8:1972;921-941
39. Salas J.D. Analysis and modeling of hydrologic time series. Maidment D. Handbook of Hydrology. 1993;McGraw Hill, New York. (Chapter 19)
40. Salas J.D. Stochastic Analysis and Modeling for Simulation and Forecasting. Consulting report for Hydro-Québec. 2000;Colorado State University
41. Salas J.D., Boes D.C. Shifting level modeling of hydrologic series. Advances in Water Resources. 3:1980;59-63
42. Salas J.D., Boes D.C., Pegram G.G.S., Yevjevich V. The Hurst phenomenon as a preasymptotic behavior. Journal of Hydrology. 44:1979;1-15
43. Salas J.D., Delleur J.W., Yevjevich V., Lane W.L. Applied modeling of hydrologic time series. 1980;Water Resources Publications, Littleton, CO
44. Sharma A., O'Neill R. A nonparametric approach for representing interannual dependence in monthly streamflow sequences. Water Resources Research. 38:(7):2002;2105-2117
45. Spiegelhalter D.J., Thomas A., Best N.G. Computation on Bayesian graphical models. Bernardo J.M., Berger J.O., Dawid A.P., Smith A.F.M. Bayesian Statistics 5. 1996;407-425 Oxford University Press, New York
46. Sveinsson O.G.B., Salas J.D. Stochastic modeling and simulation of the great lakes net basin supplies based on univariate and multivariate shifting mean (with persistence). Consulting Report for Hydro-Québec. 2001;Colorado State University
47. Sveinsson O.G.B., Salas J.D., Boes D.C., Pielke R.A. Sr. Modeling of long term variability of climatic and hydrologic processes. Journal of Hydrometeorology. 4:2003;489-496
48. Schwing F.B., Jiang J., Mendelssohn R. Coherency of multi-scale abrupt changes between the NAO, NPI, and PDO. Geophysical Research Letters. 30:(7):2003;1406-1409
49. Thyer M., Kuczera G. Modelling long term persistence in hydro-climatic time series using a hidden state Markov model. Water Resources Research. 36:(11):2000;3301-3310
50. Thyer M., Kuczera G. A hidden Markov model for modeling long-term persistence in multi-site rainfall time series 1. Model calibration using a Bayesian approach. Journal of Hydrology. 275:2003;12-26
51. Thyer M., Kuczera G. A hidden Markov model for modeling long-term persistence in multi-site rainfall time series 1. Real data analysis. Journal of Hydrology. 275:2003;27-48
52. Valdes J.B., Burlando P., Salas J.D. Stochastic Forecasting of Precipitation and Streamflow Processes. Potter T.D., Colman B. Handbook of Weather, Climate, and Water. 2002;Wiley, New York. (Chapter 34)
53. Yonetani T., Gordon H.B. Abrupt changes as indicators of decadal climate variability. Climate Dynamics. 17:2001;249-258
54. Zucchini W., Guttorp P. A hidden Markov model for space-time precipitation. Water Resources Research. 27:(8):1991;1917-1923