Flood Frequency Analysis With Historical and Paleoflood Information

TLDR

Flood quantile estimators that incorporate historical and paleoflood data, including censored magnitudes and binomial exceedance counts, are investigated. Monte Carlo simulations show that maximum likelihood estimators recover the equivalent of 10–30 additional years of record from a 50‑year historical series, outperforming the Bulletin 17B‑style adjusted‑moment estimator and remaining effective even when the true flood distribution deviates from lognormal.

Abstract

An investigation is made of flood quantile estimators which can employ “historical” and paleoflood information in flood frequency analyses. Two categories of historical information are considered: “censored” data, where the magnitudes of historical flood peaks are known; and “binomial” data, where only threshold exceedance information is available. A Monte Carlo study employing the two‐parameter lognormal distribution shows that maximum likelihood estimators (MLEs) can extract the equivalent of an additional 10–30 years of gage record from a 50‐year period of historical observation. The MLE routines are shown to be substantially better than an adjusted‐moment estimator similar to the one recommended in Bulletin 17B of the United States Water Resources Council Hydrology Committee (1982). The MLE methods performed well even when floods were drawn from other than the assumed lognormal distribution.