Applicability of Wakeby distribution in flood frequency analysis: a case study for eastern Australia. Issue 4 (11th March 2014)

• Record Type: Journal Article
• Authors: Rahman, Ataur; Zaman, Mohammad A.; Haddad, Khaled; El Adlouni, Salaheddine; Zhang, Chi
• Is Part Of: Hydrological processes. Volume 29:Issue 4(2015:Feb.)
• Page Start: 602
• Page End: 614
• Publication Date: 2014-03-11
• Subjects: Hydrology -- Periodicals; Hydrology -- Research -- Periodicals; Hydrologic models -- Periodicals; Hydrological forecasting -- Periodicals; 631.432
• DOI: 10.1002/hyp.10182
• Languages: English
• ISSNs: 0885-6087

Abstract

<abstract abstract-type="main"> <title>Abstract</title> <p>Parametric method of flood frequency analysis (FFA) involves fitting of a probability distribution to the observed flood data at the site of interest. When record length at a given site is relatively longer and flood data exhibits skewness, a distribution having more than three parameters is often used in FFA such as log‐Pearson type 3 distribution. This paper examines the suitability of a five‐parameter Wakeby distribution for the annual maximum flood data in eastern Australia. We adopt a Monte Carlo simulation technique to select an appropriate plotting position formula and to derive a probability plot correlation coefficient (PPCC) test statistic for Wakeby distribution. The Weibull plotting position formula has been found to be the most appropriate for the Wakeby distribution. Regression equations for the PPCC tests statistics associated with the Wakeby distribution for different levels of significance have been derived. Furthermore, a power study to estimate the rejection rate associated with the derived PPCC test statistics has been undertaken. Finally, an application using annual maximum flood series data from 91 catchments in eastern Australia has been presented. Results show that the developed regression equations can be used with a high degree of confidence to test whether the Wakeby distribution fits the annual maximum flood series data at a given station. The methodology developed in this paper can be adapted to other probability distributions and to other study areas. Copyright © 2014 John Wiley & Sons, Ltd.</p> </abstract>