By Fredric T Howard (ed.)
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Extra info for Applications of fibonacci numbers : volume 8 : proceedings of 'The Eighth International Research Conference on Fibonacci Numbers and Their Applications', Rochester Institute of Technology, Rochester, New York, U.S.A., June 22-26, 1998
119. STATISTICAL FLOOD FREQUENCY ANALYSIS 33 25. 23-28. 26. C. 353. 27. 318-324. 28. 821-832. 29. , 'Robust Flood Frequency Models', Water Resources Research,1982, 18, pp. 318-324. 30. P. A. 1986, prepr1nts). 31. C. 547-555. 32. ,Matalas,N •. 902-920. 33. 26934. R. 1361-1379. 35. 325-335. 36. 550. 37. J. 714-718. 34 A. KUMAR AND S. CHANDER 38. N. 25-37. 39. 853-872. 40. 999- 1019. --- 41. 1409. 42. , 'The Mathematical Theory of Communication', Bell System Technical Journal,1979,27,pp. 379428. 43.
CHANDER govern the right hand (high flow) tail. 2. This distribution can explain some of the 'separation effects' (Hatalas et al,19 75) which many other distributions cannot. Though, t:1e \'iTakeby distribution is a five parameter distribution, its parameters value can be obtained without going into ble higher moments such as the skewness coefficient. 'rile essential difference between Wakeby and GEV is that for a particular ske,v, the former can be many times more kurtotic than the latter. 4 for the GEV distribution.
Chow 285-356, Academic Based Stochastic Hydrologic Analysis' Advances in Hydroscience, Vol. l1, , New York, NY APPENDIX The following expressions define the terms in eqs. (15), respectively. 4 (18) 3 = (I)3(1 + 3C 3 . C . C 3 . C . ) V1 where k is given m k = liN 1: (i. 4 1 J j=l and m I)S/s~ (i. MS = liN 1: 1 J j=l (23) - (24) 25) where sample size and standard deviation REVIEW OF STATISTICAL MODELS FOR FLOOD FREQUENCY ESTIMATION Conleth Cunnane, Department of Engineering Hydrology, University College, Galway, Ireland.