Expected probabilities of exceedance for non-normal flood distributions

Gunasekara, T.A.G.; Cunnane, C.

doi:10.1016/0022-1694(91)90133-3

Cited by 15 publications

(5 citation statements)

References 6 publications

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“…Fitting the distributions to the left-censored data did not improve fits to the right tails. Gunasekara and Cunnane (1991) employed the expected probability as an indicator to gauge the relative merits of different flood frequency distributions. Gunasekara and Cunnane (1992) employed a split sampling technique for selecting a flood frequency analysis method using simulation experiments.…”

Section: Review Of Hydrological Literaturementioning

confidence: 99%

Probability of correct selection from lognormal and convective diffusion models based on the likelihood ratio

Strupczewski

Mitosek

Kochanek

et al. 2006

Stoch Environ Res Ris Assess

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The objective of the paper is to show that the use of a discrimination procedure for selecting a flood frequency model without the knowledge of its performance for the considered underlying distributions may lead to erroneous conclusions. The problem considered is one of choosing between lognormal (LN) and convective diffusion (CD) distributions for a given random sample of flood observations. The probability density functions of these distributions are similarly shaped in the range of the main probability mass and the discrepancies grow with the increase in the value of the coefficient of variation (C V ). This problem was addressed using the likelihood ratio (LR) procedure. Simulation experiments were performed to determine the probability of correct selection (PCS) for the LR method. Pseudo-random samples were generated for several combinations of sample sizes and the coefficient of variation values from each of the two distributions. Surprisingly, the PCS of the LN model was twice smaller than that of the CD model, rarely exceeding 50%. The results obtained from simulation were analyzed and compared both with those obtained using real data and with the results obtained from another selection procedure known as the QK method. The results from the QK are just the opposite to that of the LR procedure.

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Section: Review Of Hydrological Literaturementioning

confidence: 99%

Probability of correct selection from lognormal and convective diffusion models based on the likelihood ratio

Strupczewski

Mitosek

Kochanek

et al. 2006

Stoch Environ Res Ris Assess

View full text Add to dashboard Cite

show abstract

“…However, an unbiased estimator of the T -yr event will not, in general, be exceeded with an average probability of p = I/T. Beard (1960), Beard (1978), IACWD ["Guidelines" (1982), Appendix 11], Stedinger (1983), Cunnane (1991), andStedinger et al (1993) discuss this issue in greater detail.…”

Section: Expected Probability Adjustmentmentioning

confidence: 99%

“…We employ these corrections for all of the methods considered. Gunasekara and Cunnane (1991) showed that the expected probability correction for normally distributed samples is approximately valid for other distributions.…”

Section: Expected Probability Adjustmentmentioning

confidence: 99%

Flood‐Flow Frequency Model Selection in Southwestern United States

Vogel

Thomas

McMahon

1993

J. Water Resour. Plann. Manage.

118

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Uniform flood frequency guidelines in the United States recommend the use of the log Pearson type 3 (LP3) distribution in flood frequency investigations. Many investigators have suggested alternate models such as the generalized extreme value (GEV) distribution as an improvement over the LP3 distribution. Using flood-flow data at 383 sites in the southwestern United States, we explore the suitability of various flood frequency models using L-moment diagrams. We also repeat the experiment performed in the original Water Resource Council report (Bulletin 17B, issued in 1982), which led to the LP3 mandate. All our evaluations consistently reveal that the LP3, GEV, and the two-and three-parameter lognormal models (LN2 and LN3) provide a good approximation to flood-flow data in this region. Other models such as the normal, Pearson, and Gumbel distributions are shown to perform poorly. Recent research indicates that regional index-flood procedures should be more accurate and more robust than the type of at-site procedures evaluated here. Nevertheless, this study reveals that index-flood procedures need not be restricted to the GEV distribution because the LN2, LN3, and LP3 distributions appear to be suitable alternatives, at least in the southwestern United States.

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“…Such a statistical model has been widely used in the analysis of hydrological extremes because of its flexibility in representing the three asymptotic types of extreme value probability distributions, first introduced by Gnedenko (1943) and further refined by Gumbel (1954). For example, the GEV distribution was selected to model extreme flows in the pioneering regional flood analysis of Great Britain by NERC (1975), and to model the rainfall frequency in the United States by Willeke et al (1995)-see also Gunasekara andCunanne (1991) andÖ nöz andBayazit (1995) for other hydrological applications. Pickands (1975) introduced the Generalized Pareto (GP) distribution, which provides a framework for the statistical modeling of excesses over thresholds.…”

Section: Introductionmentioning

confidence: 99%