Comparisons of traditional and novel stochastic models for the generation of daily precipitation occurrences

Journal of Hydrology

Volumn 380, Issue 1-2, 2010, Pages 222-236

  Ng, W W ^a,b   Panu, U S ^a,b  

^a UNIVERSITY OF MANITOBA   (Canada)

^b LAKEHEAD UNIVERSITY   (Canada)

Author keywords

Alternating renewal process; Dictionary approach; Geometric distribution; Markov chain; Precipitation occurrence; Probabilistic Word Matrix

Indexed keywords

ALTERNATING RENEWAL PROCESS; AVERAGE MUTUAL INFORMATION; BAYESIAN; COMPARATIVE ANALYSIS; GEOMETRIC DISTRIBUTION; GOODNESS OF FIT; GUIDING PRINCIPLES; HURST EXPONENTS; INFORMATION THEORETIC CRITERION; LIKELIHOOD ESTIMATE; MARKOV CHAIN; MARKOV CHAIN MODELS; MATRIX; METEOROLOGICAL STATION; PRECIPITATION SEQUENCE; SECOND ORDERS; SEGMENT LENGTHS; STRUCTURAL FLEXIBILITIES;

INFORMATION THEORY; MARKOV PROCESSES; MATRIX ALGEBRA; PROBABILITY DISTRIBUTIONS; STOCHASTIC SYSTEMS;

STOCHASTIC MODELS;

BAYESIAN ANALYSIS; COMPARATIVE STUDY; MARKOV CHAIN; PRECIPITATION (CLIMATOLOGY); STOCHASTICITY; WEATHER STATION;

CANADA; NORTH AMERICA;

EID: 71849096796     PISSN: 00221694     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jhydrol.2009.11.002     Document Type: Article

References (71)

1. Akaike H. A new look at the statistical model identification. IEEE Transactions on Automatic Control 19 6 (1974)
2. Bailey N.T.J. The Elements of Stochastic Processes (1964), John Wiley, New York
3. Bardossy A. Space-time model for daily rainfall using atmospheric circulation patterns. Water Resources Research 28 5 (1992) 1247-1259
4. Bellone E., Hughes J.P., and Guttorp P. A hidden Markov model for downscaling synoptic atmospheric patterns to precipitation amounts. Climate Research 15 (2000) 1-12
5. Buishand T.A. Some remarks on the use of daily rainfall models. Journal of Hydrology 36 (1978) 295-308
6. Bussemaker H.J., Li H., and Siggia E.D. Building a dictionary for genomes: identification of presumptive regulatory sites by statistical analysis. Proceedings of the National Academy of Sciences of the United States of America 97 18 (2000) 10096-10100
7. Chang T.J., Kavvas M.L., and Delleur J.W. Daily precipitation modeling by discrete autoregressive moving average processes. Water Resources Research 20 5 (1984) 565-580
8. Chin E.H. Modeling daily precipitation occurrence process with Markov chain. Water Resources Research 13 6 (1977) 949-956
9. Cole J.A., and Sherriff J.D.F. Some single- and multi-site models of rainfall within discrete time increments. Journal of Hydrology 17 (1972) 97-113
10. Cowpertwait P.S.P. Further developments of the Neyman-Scott clustered point process for modeling rainfall. Water Resources Research 27 (1991) 1431-1438
11. Duckstein L., Fogel M., and Kisiel C.C. A stochastic model of runoff-producing rainfall for summer type storms. Water Resources Research 8 2 (1972) 410-421
12. Feller W. An Introduction to Probability Theory and its Applications (1970), Wiley, New York
13. Fitzpatrick E.A., and Krishnan A. A first-order Markov model for assessing rainfall discontinuity in central Australia. Journal Theoretical and Applied Climatology 15 3 (1967) 242-259
14. Foufoula-Georgiou E., and Lettenmaier D.P. Continuous-time versus discrete-time point process models for rainfall occurrence series. Water Resources Research 22 4 (1986) 531-542
15. Foufoula-Georgiou E., and Lettenmaier D.P. A Markov renewal model for rainfall occurrences. Water Resources Research 23 (1987) 875-884
16. Fraser A.M., and Swinney H.L. Independent coordinates for strange attractors from mutual information. Physics Review A 33 (1986) 1134-1140
17. Gabriel K.R., and Neumann J. A Markov chain model for daily rainfall occurrence at Tel Aviv (1962) 88
18. Geng S., de Vries F.P., and Supit I. A simple method for generating daily rainfall data. Agricultural and Forest Meteorology 36 (1986) 363-376
19. Green J.R. A model for rainfall occurrence. Journal of the Royal Statistical Society. Series B (Methodological) 26 2 (1964) 345-353
20. Gupta M., and Liu J.S. Discovery of conserved sequence patterns using a stochastic dictionary model. Journal of the American Statistical Association 98 461 (2003) 1251-1257
21. Hay L.E., McCabe G.J., Wolock D.M., and Ayers M.A. Simulation of precipitation by weather type analysis. Water Resources Research 27 4 (1991) 493-501
22. Hayhoe H.N., and Stewart D.W. Evaluation of CLIGEN and WXGEN weather data generators under Canadian conditions. Canadian Water Resources Journal 21 (1996) 53-57
23. Holland J.H. Adaptation in Natural and Artificial Systems (1975), University of Michigan Press, Ann Arbor, MI
24. Hopkins J.W., and Robillard P. Some statistics of daily rainfall occurrence for the canadian prairie provinces. Journal of Applied Meteorology 3 (1964) 600-602
25. Hughes J.P., and Guttorp P. A class of stochastic models for relating synoptic atmospheric patterns to regional hydrologic phenomena. Water Resources Research 30 (1994) 1535-1546
26. Hughes J.P., Guttorp P., and Charles S.P. A non-homogeneous hidden Markov model for precipitation occurrence. Applied Statistics 48 1 (1999) 15-30
27. Hurst H.E. Long-term storage capacity of reservoirs. Transactions of the American Society of Civil Engineering 116 (1951) 770-799
28. Hutchinson, M.F., Richardson, C.W., Dyke, P.T., 1993. Normalization of rainfall across different time steps. In: Management of Irrigation and Drainage Systems. Park City, UT, pp. 432-439.
29. Katz R.W. Precipitation as a chain-dependent process. Journal of Applied Meteorology 16 (1977) 671-676
30. Katz R.W. On some criteria for estimating the order of a Markov chain. Technometrics 23 (1981) 243-249
31. Kavvas M.L., and Delleur J.W. A point stochastic model for continental droughts. Hydrological Sciences Bulletin 21 3 (1976) 407-417
32. Kavvas M.L., and Delleur J.W. A stochastic cluster model of daily rainfall sequences. Water Resources Research 17 (1981) 1151-1160
33. Kirkpatrick S., Gelatt C.D., and Vecchi M.P. Optimization by simulated annealing. Science 220 4598 (1983) 671-680
34. Lambert M.F., Whiting J.P., and Metcalfe A.V. A non-parametric hidden Markov model for climate state identification. Hydrology and Earth System Sciences 7 5 (2003) 652-667
35. Ng W.W., Panu U.S., and Lennox W.C. Comparative studies in problems of missing extreme daily streamflow records. Journal of Hydrologic Engineering 14 1 (2009) 91-100
36. Pachter L., Batzoglou S., Spitkovsky V.I., Banks E., Lander E.S., Kleitman D.J., and Berger B. A dictionary-based approach for gene annotation. Journal of Computational Biology 6 3/4 (1999) 419-430
37. Panu U.S., Khalil M., and Elshorbagy A. Streamflow data infilling techniques based on concepts of groups and neural networks. Artificial Neural Networks in Hydrology. (2000) 235-258 Chapter 12
38. Panu U.S., and Unny T.E. Stochastic synthesis of hydrologic data based on concepts of pattern recognition, I. General methodology of the approach. Journal of Hydrology 46 1-2 (1980) 5-34
39. Panu U.S., Unny T.E., and Ragade R.K. A feature prediction model in synthetic hydrology based on concepts of pattern recognition. Water Resources Research 14 2 (1978) 335-344
40. Pickering N.B., Hansen J.W., Jones J.W., Wells C.M., Chan V.K., and Godwin D.C. Weatherman: a utility for managing and generating daily weather data. Agronomy Journal 86 (1994) 332-337
41. Racsko P., Szeidl L., and Semenov M. A serial approach to local stochastic weather models. Ecological Modelling 57 (1991) 27-41
42. Rasmussen, P.F., Akintug, B., 2004, Drought frequency analysis with a hidden state Markov model. In: Proceedings of the 2004 World Water and Environmental Resources Congress: Critical Transitions in Water and Environmental Resources Management, pp. 2078-2087.
43. Richardson C.W. Stochastic simulation of daily precipitation, temperature, and solar radiation. Water Resources Research 17 (1981) 182-190
44. Richardson, C.W., Wright, D.A., 1984. WGEN: a model for generating daily weather variables, US Department of Agriculture, Agricultural Research Service, ARS-8, p. 83.
45. Rolda'n J., and Woolhiser D.A. Stochastic daily precipitation models, 1, a comparison of occurrence processes. Water Resources Research 18 (1982) 1451-1459
46. Salas J. Analysis and modeling of hydrologic time series. In: Maidment D. (Ed). Handbook of Hydrology (1993), McGraw-Hill, New York
47. Salas J.D., and Boes D.C. Shifting level modelling of hydrologic time series. Advances in Water Resources 3 (1980) 59-63
48. Schwarz G. Estimating the dimension of a model. Annals of Statistics 6 2 (1978)
49. Semenov M.A., and Barrow E.M. Use of a stochastic weather generator in the development of climate change scenarios. Climatic Change 35 (1997) 397-414
50. Semenov M.A., Brooks R.J., Barrow E.M., and Richardson C.W. Comparison of the WGEN and LARS-WG stochastic weather generators for diverse climate. Climate Research 10 (1998) 95-107
51. Shannon C.E. A mathematical theory of communication. Bell System Technical Journal 27 (1948) 379-423 (623-656)
52. Smith E.R., and Schreiber H.A. Point processes of seasonal thunderstorm rainfall, 1, distribution of rainfall events. Water Resources Research 9 4 (1973) 871-884
53. Smith J.A. Statistical modelling of daily rainfall occurrences. Water Resources Research 23 (1987) 885-893
54. Smith J.A., and Karr A.F. A point process model of summer season rainfall occurrences. Water Resources Research 19 1 (1983) 95-103
55. Smith J.A., and Karr A.F. Parameter estimation for a model of space-time rainfall. Water Resources Research 21 (1985) 1251-1257
56. Srikanthan R., and McMahon T. Stochastic generation of annual, monthly and daily climate data: a review. Stochastic Hydrology and Earth System Sciences 5 4 (2001) 653-670
57. Stern R.D., and Coe R. A model fitting analysis of daily rainfall data. Journal of Royal Statistical Society, Series A 147 (1984) 1-34
58. Thyer M., and 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
59. Thyer M., and Kuczera G. A hidden Markov model for modelling long-term persistence in multi-site rainfall time series 1. Model calibration using a Bayesian approach. Journal of Hydrology 275 (2003) 12-26
60. Thyer M., and Kuczera G. A hidden Markov model for modelling long-term persistence in multi-site rainfall time series 2. Real data analysis. Journal of Hydrology 275 (2003) 27-48
61. Todorovic P., and Woolhiser D.A. Stochastic model of daily rainfall. Miscellaneous Publication United States Department of Agriculture 1275 (1974) 232-246
62. Tong H. Determination of the order of a Markov chain by Akaike's information criterion. Journal of Applied Probability 12 3 (1995)
63. Wan H., Zhang X., and Barrow E.M. Stochastic modelling of daily precipitation for Canada. Atmosphere-Ocean 43 1 (2005) 23-32
64. Waymire E. The mathematical structure of rainfall representations 1. A review of the stochastic rainfall models. Water Resources Research 17 5 (1981) 1261-1272
65. Weiss L.L. Sequences of wet and dry days described by a Markov chain model. Monthly Weather Review 92 (1964) 169-175
66. Wilby, R.L., Charles, S., Zorita, E., Timbal, B., Whetton, P., Mearns, L., 2004. Guidelines for use of climate scenarios developed from statistical downscaling methods. "Supporting material" of the Intergovernmental Panel on Climate Change for consideration by the IPCC at the request of its task group on data and scenario support for impacts and climate analysis (TGICA).
67. Wilks D.S. Conditioning stochastic daily precipitation models on total monthly precipitation. Monthly Weather Review 25 (1989) 1429-1439
68. Wilks D.S. Interannual variability and extreme-value characteristics of several stochastic daily precipitation models. Journal of Agricultural Meteorology 93 (1999) 153-169
69. Wiser E.H. Modified Markov probability models of sequences of precipitation events. Monthly Weather Review 93 (1965) 511-516
70. Woolhiser D.A., and Pegram G.G.S. Maximum likelihood estimation of fourier coefficients to describe seasonal variations of parameters in stochastic daily precipitation models. Journal of Applied Meteorology 18 (1979) 34-42
71. Zucchini W., and Guttorp P. A hidden Markov model for space-time precipitation. Water Resources Research 27 (1991) 1917-1923