Flood‐Flow Frequency Model Selection in Southwestern United States

Abstract

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.

Probability distribution of flood flows in Tunisia

“…Unlike similar previous work, such as the studies by Vogel et al (1993b) and Vogel and Wilson (1996), in which flood frequency distributions were determined for all of Australia and the United States respectively, Tunisia, in this study, was divided into two sub-regions, for which separate flood frequency analysis procedures were applied.…”

L-moments approach for flood frequency analysis of river Okhuwan in Benin-Owena River basin in Nigeria

“…The method of L-moments is extensively presented in [10] but in summary it is a modification of the probability weighted moments (PWMs) method presented in [14]. L-moments and probability weighted moments are used to summarize theoretical distributions and observed samples thereby making them liable for use in parameter estimation, interval estimation and hypothesis testing [15]. Lmoments and L-moment ratios are however more convenient than probability weighted moments because they are more easily interpretable as measures of distribution scale and shape [16].…”

Statistical distribution of rainfall in Uttarakhand, India

“…The choice of a best-fit distribution also has been applied to a discharge series in the USA (Benson 1968;Vogel et al 1993), the UK (NERC 1975), Australia (McMahon and Srikanthan 1981), and Turkey (Haktanir 1991). A review of the selection of the best distribution was given by (Curmane 1989).…”