Incorporating spatial dependence in regional frequency analysis

Water Resources Research

Volumn 50, Issue 12, 2014, Pages 9570-9585

  Wang, Zhuo ^a   Yan, Jun ^a   Zhang, Xuebin ^b  

^a UNIVERSITY OF CONNECTICUT   (United States)

^b ENVIRONMENT CANADA   (Canada)

Author keywords

extreme analysis; max stable process

Indexed keywords

FLOODS; FREQUENCY ESTIMATION; GEOGRAPHICAL REGIONS; MAXIMUM LIKELIHOOD ESTIMATION; MEAN SQUARE ERROR; METHOD OF MOMENTS; RAIN; STABILITY CRITERIA;

ANNUAL MAXIMUM PRECIPITATION; EXTREME ANALYSIS; GENERALIZED EXTREME VALUE DISTRIBUTION; MAX-STABLE PROCESS; MEAN SQUARED ERROR; PAIRWISE LIKELIHOOD; REGIONAL FREQUENCY ANALYSIS; SPATIAL DEPENDENCE;

PARAMETER ESTIMATION;

DATA SET; ESTIMATION METHOD; FLOOD; FREQUENCY ANALYSIS; INDEX METHOD; NUMERICAL MODEL; PARAMETERIZATION; PRECIPITATION (CLIMATOLOGY); SPATIAL ANALYSIS;

SWITZERLAND;

EID: 85027933298     PISSN: 00431397     EISSN: 19447973     Source Type: Journal    
DOI: 10.1002/2013WR014849     Document Type: Article

References (54)

1. Banerjee, S.; B. P. Carlin, and, A. E. Gelfand, (2004), Hierarchical Modeling and Analysis for Spatial Data, 452 pp.; Chapman and Hall.; N. Y.
2. Blanchet, J.; and, A. C. Davison, (2011), Spatial modeling of extreme snow depth, Ann. Appl. Stat.; 5 (3), 1699-1725.
3. Buishand, T. A.; (1991), Extreme rainfall estimation by combining data from several sites, Hydrol. Sci. J.; 36, 345-365.
4. Castellarin, A.; (2007), Probabilistic envelope curves for design flood estimation at ungauged sites, Water Resour. Res.; 43, W04406, doi: 10.1029/2005WR004384.
5. Castellarin, A.; R. M. Vogel, and, N. C. Matalas, (2005), Probabilistic behavior of a regional envelope curve, Water Resour. Res.; 41, W06018, doi: 10.1029/2004WR003042.
6. Castellarin, A.; D. Burn, and, A. Brath, (2008), Homogeneity testing: How homogeneous do heterogeneous cross-correlated regions seem?, J. Hydrol.; 360 (1-4), 67-76.
7. Chebana, F.; and, T. Ouarda, (2009), Index flood-based multivariate regional frequency analysis, Water Resour. Res.; 45, W10435, doi: 10.1029/2008WR007490.
8. Clayton, M. K.; and, P.-S. Lin, (2005), Analysis of binary spatial data by quasi-likelihood estimating equations, Ann. Stat.; 33 (2), 542-555.
9. Coles, S. G.; and, M. J. Dixon, (1999), Likelihood-based inference for extreme value models, Extremes, 2, 5-23.
10. Cunderlik, J. M.; and, T. B. Ouarda, (2006), Regional flood-duration-frequency modeling in the changing environment, J. Hydrol.; 318 (1), 276-291.
11. Davison, A. C.; and, M. M. Gholamrezaee, (2012), Geostatistics of extremes, Proc. R. Soc. A, 468, 581-608.
12. Davison, A. C.; S. A. Padoan, and, M. Ribatet, (2012), Statistical modeling of spatial extremes, Stat. Sci.; 27 (2), 161-186.
13. de Haan, L.; (1984), A spectral representation for max-stable processes, Ann. Probab.; 12, 1194-1204.
14. Embrechts, P.; A. McNeil, and, D. Straumann, (2002), Correlation and dependence in risk management: Properties and pitfalls, in Risk Management: Value at Risk and Beyond, edited by, M. A. H. Dempster, pp. 176-223, Cambridge Univ. Press, Cambridge, U. K.
15. Genest, C.; and, A.-C. Favre, (2007), Everything you always wanted to know about copula modeling but were afraid to ask, J. Hydrol. Eng.; 12, 347-368.
16. Griffis, V.; and, J. Stedinger, (2007), The use of GLS regression in regional hydrologic analyses, J. Hydrol.; 344 (1-2), 82-95, doi: 10.1016/j.jhydrol.2007.06.023.
17. Gudendorf, G.; and, J. Segers, (2010), Extreme-value copulas, in Copula Theory and Its Applications, edited by, P. Jaworski, et al.; pp. 127-145, Springer Berlin Heidelberg.
18. Hanel, M.; T. A. Buishand, and, C. Ferro, (2009), A nonstationary index flood model for precipitation extremes in transient regional climate model simulations, J. Geophys. Res.; 114, D15107, doi: 10.1029/2009JD011712.
19. Heffernan, J. E.; and, J. A. Tawn, (2004), A conditional approach for multivariate extreme values (with discussion), J. R. Stat. Soc.; Ser. B, 66 (3), 497-546.
20. Hosking, J. R. M.; (1990), L-moments: Analysis and estimation of distributions using linear combinations of order statistics, J. R. Stat. Soc.; Ser. B, 52, 105-124.
21. Hosking, J. R. M.; and, J. R. Wallis, (1988), The effect of intersite dependence on regional flood frequency analysis, Water Resour. Res.; 24, 588-600.
22. Hosking, J. R. M.; and, J. R. Wallis, (1993), Some statistics useful in regional frequency analysis, Water Resour. Res.; 29, 271-281.
23. Hosking, J. R. M.; and, J. R. Wallis, (1997), Regional Frequency Analysis: An Approach Based on L-moments, 224 pp.; Cambridge Univ. Press, Cambridge, U. K.
24. Javelle, P.; T. B. Ouarda, M. Lang, B. Bobée, G. Galéa, and, J.-M. Grésillon, (2002), Development of regional flood-duration-frequency curves based on the index-flood method, J. Hydrol.; 258 (1), 249-259.
25. Kojadinovic, I.; J. Segers, and, J. Yan, (2011), Large-sample tests of extreme-value dependence for multivariate copulas, Can. J. Stat.; 39 (4), 703-720.
26. Kojadinovic, I.; H. Shang, and, J. Yan, (2014), A class of goodness-of-fit tests for spatial extremes models based on max-stable processes, Stat. Interface, in press.
27. Kysely, J.; and, J. Picek, (2007), Regional growth curves and improved design value estimates of extreme precipitation events in the Czech Republic, Clim. Res.; 33 (3), 243-255.
28. Leclerc, M.; and, T. B. Ouarda, (2007), Non-stationary regional flood frequency analysis at ungauged sites, J. Hydrol.; 343 (3), 254-265.
29. Lettenmaier, D. P.; J. Wallis, and, E. Wood, (1987), Effect of regional heterogeneity on flood frequency estimation, Water Resour. Res.; 23, 313-323.
30. Lin, P.-S.; (2008), Estimating equations for spatially correlated data in multi-dimensional space, Biometrika, 95 (4), 847-858.
31. Lin, P.-S.; (2010), A working estimating equation for spatial count data, J. Stat. Plann. Inference, 140 (9), 2470-2477.
32. Lindsay, B. G.; (1988), Composite likelihood methods, in Statistical Inference from Stochastic Processes, edited by, N. U. Prabhu, pp. 221-239, Am. Math. Soc.; Providence, R. I.
33. Martins, E. S.; and, J. R. Stedinger, (2000), Generalized maximum-likelihood generalized extreme-value quantile estimators for hydrologic data, Water Resour. Res.; 36, 737-744, doi: 10.1029/1999WR900330.
34. Matalas, N. C.; and, W. B. Langbein, (1962), Information content of the mean, J. Geophys. Res.; 67 (9), 3441-3448.
35. Ngongondo, C. S.; C.-Y. Xu, L. M. Tallaksen, B. Alemaw, and, T. Chirwa, (2011), Regional frequency analysis of rainfall extremes in Southern Malawi using the index rainfall and l-moments approaches, Stochastic Environ. Res. Risk Assess.; 25 (7), 939-955.
36. Nikoloulopoulos, A. K.; H. Joe, and, N. R. Chaganty, (2011), Weighted scores method for regression models with dependent data, Biostatistics, 12, 653-665.
37. Northrop, P.; (2004), Likelihood-based approaches to flood frequency estimation, J. Hydrol.; 292, 96-113.
38. Ouarda, T.; J. Cunderlik, A. St-Hilaire, M. Barbet, P. Bruneau, and, B. Bobée, (2006), Data-based comparison of seasonality-based regional flood frequency methods, J. Hydrol.; 330 (1), 329-339.
39. Ouarda, T. B.; M. Haché, P. Bruneau, and, B. Bobée, (2000), Regional flood peak and volume estimation in Northern Canadian Basin, J. Cold Reg. Eng.; 14 (4), 176-191.
40. Ouarda, T. B. M. J.; (2013), Hydrological frequency analysis, regional, in Encyclopedia of Environmetrics, 2nd ed.; edited by, A. El-Shaarawi, and, W. Piegorsch, John Wiley, Chichester, U. K.; doi: 10.1002/9780470057339.vnn043.
41. Padoan, S. A.; M. Ribatet, and, S. A. Sisson, (2010), Likelihood-based inference for max-stable processes, J. Am. Stat. Assoc.; 105 (489), 263-277.
42. Ribatet, M.; and, R. Singleton, (2013), SpatialExtremes: Modelling Spatial Extremes, R package version 1.9-1. [Available at http://CRAN.R-project.org/package=SpatialExtremes.]
43. Schlather, M.; (2002), Models for stationary max-stable random fields, Extremes, 5 (1), 33-44.
44. Schlather, M.; and, J. A. Tawn, (2003), A dependence measure for multivariate and spatial extreme values: Properties and inference, Biometrika, 90 (1), 139-156.
45. Sklar, A. W.; (1959), Fonctions de répartition à n dimension et leurs marges, Publ. Inst. Stat. Univ. Paris, 8, 229-231.
46. Stedinger, J. R.; (1983), Estimating a regional flood frequency distribution, Water Resour. Res.; 19, 503-510, doi: 10.1029/WR019i002p00503.
47. Stoner, J. A.; and, B. G. Leroux, (2002), Analysis of clustered data: A combined estimating equations approach, Biometrika, 89 (3), 567-578.
48. Troutman, B. M.; and, M. R. Karlinger, (2003), Regional flood probabilities, Water Resour. Res.; 39 (4), 1095, doi: 10.1029/2001WR001140.
49. Varin, C.; (2008), On composite marginal likelihoods, Adv. Stat. Anal.; 92 (1), 1-28.
50. Varin, C.; N. Reid, and, D. Firth, (2011), An overview of composite likelihood methods, Stat. Sinica, 21 (1), 5-42.
51. Viglione, A.; A. Castellarin, M. Rogger, R. Merz, and, G. Blschl, (2012), Extreme rainstorms: Comparing regional envelope curves to stochastically generated events, Water Resour. Res.; 48, W01509, doi: 10.1029/2011WR010515.
52. Wang, Z.; J. Yan, and, X. Zhang, (2014), Incorporating spatial dependence in regional frequency analysis, Tech. Rep. 9, Dep. of Stat.; Univ. of Conn.; Storrs, Conn.
53. Westra, S.; and, S. A. Sisson, (2011), Detection of non-stationarity in precipitation extremes using a max-stable process model, J. Hydrol.; 406 (1), 119-128.
54. Yasui, Y.; and, S. Lele, (1997), A regression method for spatial disease rates: An estimating function approach, J. Am. Stat. Assoc.; 92, 21-32.