A unified statistical model for hydrological variables including the selection of threshold for the peak over threshold method

Water Resources Research

Volumn 48, Issue 10, 2012, Pages

  Solari, S ^a,b   Losada, M A ^a  

^a UNIVERSITY OF GRANADA   (Spain)

^b UNIVERSIDAD DE LA REPÚBLICA   (Uruguay)

Author keywords

[No Author keywords available]

Indexed keywords

DATA SERIES; FLOW SERIES; FORT COLLINS; GENERALIZED PARETO DISTRIBUTIONS; HYDROLOGICAL DATA; HYDROLOGICAL VARIABLES; LOG-NORMAL; LOG-NORMAL DISTRIBUTION; MARGINAL DISTRIBUTION; MIXTURE MODEL; OBJECTIVE IDENTIFICATION; PEAK OVER THRESHOLD METHOD; SIMULATION STUDIES; STATISTICAL MODELS; UNITED KINGDOM;

MATHEMATICAL MODELS;

WATER RESOURCES;

DATA SET; HYDROLOGICAL RESPONSE; MAXIMUM LIKELIHOOD ANALYSIS; PARAMETERIZATION; PEAK FLOW; PRECIPITATION (CLIMATOLOGY); STATISTICAL ANALYSIS; THRESHOLD;

ANDALUCIA; COLORADO; ENGLAND; FORT COLLINS; GRANADA [ANDALUCIA]; GUADALFEO RIVER; SPAIN; THAMES RIVER [ENGLAND]; UNITED KINGDOM; UNITED STATES;

EID: 84868319988     PISSN: 00431397     EISSN: None     Source Type: Journal    
DOI: 10.1029/2011WR011475     Document Type: Article

References (40)

1. Akaike, H. (1974), A new look at the statistical model identification, IEEE Trans. Autom. Control, 19(6), 716-723.
2. Behrens, C. N., H. F. Lopes, and D. Gamerman (2004), Bayesian analysis of extreme events with threshold estimation, Stat. Model., 4, 227-244.
3. Benjamin, J., and C. Cornell (1970), Probability, Statistics and Decision for Civil Engineers, 684 pp., McGraw-Hill, New York.
4. Cai, Y., B. Gouldby, P. Dunning, and P. Hawkes (2007), A simulation method for flood risk variables, 2nd Institute ofMathematics and its Applications International Conference on Flood Risk Assessment, 4 September 2007, University of Plymouth, England.
5. Cai, Y., B. Gouldby, P. Hawkes, and P. Dunning (2008), Statistical simulation of flood variables: Incorporating short-term sequencing, J. Flood Risk Manage., 1, 3-12.
6. Carreau, J., P. Naveau, and E. Sauquet (2009), A statistical rainfall-runoff mixture model with heavy-tailed components, Water Resour. Res., 45(10), W10437, doi :10.1029/2009WR007880.
7. Castillo, E., A. S. Hadi, N. Balakrishnan, and J. M. Sarabia (2005), Extreme Value and Related Models with Applications in Engineering and Science, Wiley Series in Probability and Statistics, John Wiley, New York.
8. Chow, V. T. (1988), Applied Hydrology, 585 pp., McGraw-Hill, New York.
9. Coles, S. (2001), An Introduction to Statistical Modeling of Extreme Values, Springer Series in Statistics, Springer, Berlin.
10. Cunnane, C. (1979), A note on the Poisson assumption in partial duration series models, Water Resour. Res., 15(2), 489-494.
11. Davison, A., and R. Smith (1990), Models for exceedances over high thresholds, J. R. Soc. Ser. B, 52(3), 393-442.
12. de Zea Bermudez, P., and S. Kotz (2010a), Parameter estimation of the generalized Pareto distribution-Part II, J. Stat. Plan. Inference, 140(6), 1374-1388, doi:10.1016/j.jspi.2008.11.020.
13. de Zea Bermudez, P., and S. Kotz (2010b), Parameter estimation of the generalized Pareto distribution-Part I, J. Stat. Plan. Inference, 140(6), 1353-1373, doi:10.1016/j.jspi.2008.11.019.
14. Dupuis, D. (1998), Exceedances over high thresholds: A guide to threshold selection, Extremes, 1(3), 251-261.
15. Eastoe, E. F., and J. A. Tawn (2010), Statistical models for overdispersion in the frequency of peaks over threshold data for a flow series, Water Resour. Res., 46(2), W02510, doi:10.1029/2009WR007757.
16. 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.
17. Fawcett, L., and D. Walshaw (2006), Markov chain models for extreme wind speeds, Environmetrics, 17(8), 795-809, doi:10.1002/env.794.
18. Fawcett, L., and D. Walshaw (2007), Improved estimation for temporally clustered extremes, Environmetrics, 18, 173-188, doi:10.1002/env. Ferro, C. A. T., and J. Segers (2003), Inference for clusters of extreme values, J. R. Stat. Soc. Ser. B, 65(2), 545-556.
19. Frigessi, A., O. Haug, and H. Rue (2002), A dynamic mixture model for unsupervised tail estimation without threshold selection, Extremes, 5, 219-235.
20. Furrer, E. M., and R. W. Katz (2008), Improving the simulation of extreme precipitation events by stochastic weather generators, Water Resour. Res., 44(12), W12439, doi:10.1029/2008WR007316.
21. Herrero, J., M. Polo, A. Moñino, and M. A. Losada (2009), An energy balance snowmelt model in a Mediterranean site, J. Hydrol., 371(1-4), 98-107, doi:10.1016/j.jhydrol.2009.03.021.
22. Hosking, J. R. M., and J. R. Wallis (1987), Parameter and quantile estimation for the generalized Pareto distribution, Technometrics, 29(3), 339-349.
23. Hundecha, Y., M. Pahlow, and A. Schumann (2009), Modeling of daily precipitation at multiple locations using a mixture of distributions to characterize the extremes, Water Resour. Res., 45(12), W12412, doi:10.1029/ 2008WR007453.
24. Katz, R. W., M. B. Parlange, and P. Naveau (2002), Statistics of extremes in hydrology, Adv. Water Resour., 25(8-12), 1287-1304, doi:10.1016/ S0309-1708(02)00056-8.
25. Kottegoda, N. T., and R. Rosso (2008), Applied Statistics for Civil and Environmental Engineers, 2nd ed., Blackwell, London. Lang, M., T. Ouarda, and B. Bobe (1999), Towards operational guidelines for over-threshold modeling. J. Hydrol., 225, 103-117.
26. Leadbetter, M. (1991), On the basis for ''Peaks over Threshold'' modeling, Stat. Probab. Lett., 12(4), 357-362.
27. Luceño, A., M. Menéndez, and F. Méndez (2006), The effect of temporal dependence on the estimation of the frequency of extreme ocean climate events, Proc. R. Soc. A, 462, 1638-1697.
28. Madsen, H., P. F. Rasmussen, and D. Rosbjerg (1997), Comparison of annual maximum series and partial duration series methods for modeling extreme hydrologic events 1. At-site modeling, Water Resour. Res., 33(4), 747-757.
29. Mans, C., S. Bramato, A. Baquerizo, and M. A. Losada (2011), Surface seiche formation on a shallow reservoir in complex terrain, J. Hydraul. Eng., 137(5), 517-529, doi:10.1061/(ASCE)HY.1943-7900.0000328.
30. Marsh, T., B. Greenfield, and J. Hannaford (2005), The 1894 Thames flood a reappraisal, Proc. Inst. Civil Eng.Water Manage., 158(WM3), 103-110.
31. Millares, A., M. Polo, and M. A. Losada (2009), The hydrological response of baseflow in fractured mountain areas, Hydrol. Earth Syst. Sci. Discuss., 6(2), 3359-3384, doi:10.5194/hessd-6-3359-2009.
32. Nocedal, J., and S. J. Wright (2006), Numerical Optimization, 2nd ed., 686 pp., Springer, Berlin. Pickands, J. I. (1975), Statistical inference using extreme order statistics, Ann. Stat., 3(1), 119-131.
33. Salvadori, G., C. De Michele, N. Kottegoda, and R. Rosso (2007), Extreme in Nature. An Approach Using Copulas, Springer
34. Berlin. Smith, J. A. (1987), Estimating the upper tail of flood frequency distributions, Water Resour. Res., 23(8), 1657-1666, doi:10.1029/ WR023i008p01657.
35. Solari, S., and M. Losada (2011), Non-stationary wave height climate modeling and simulation, J. Geophys. Res., 116, C09032, doi:10.1029/ 2011JC007101.
36. Solari, S., and P. van Gelder (2011), On the use of vector autoregressive (VAR) and regime switching VAR models for the simulation of sea and wind state parameters, in Marine Technology and Engineering. CENTEC Anniversary Book, edited by C. Guedes Soares, CRC, Taylor & Francis, New York.
37. Tancredi, A., C. Anderson, and A. O'Hagan (2006), Accounting for threshold uncertainty in extreme value estimation, Extremes, 9, 87-106.
38. Thompson, P., Y. Cai, D. Reeve, and J. Stander (2009), Automated threshold selection methods for extreme wave analysis, Coastal Eng., 56, 1013-1021.
39. Vaz de Melo Mendes, B., and H. Freitas Lopes (2004), Data driven estimates for mixtures, Comput. Stat. Data Anal., 47, 583-598.
40. Vrac, M., and P. Naveau (2007), Stochastic downscaling of precipitation: From dry events to heavy rainfalls, Water Resour. Res., 43(7), W07402, doi:10.1029/2006WR005308.