Improvements to Flood Frequency Analysis on Alluvial Rivers Using Paleoflood Data

Abstract

AbstractHydrologists and engineers routinely use flood frequency analyses to compute flood probabilities for mitigation, infrastructure planning, and emergency management. Conventional flood frequency analyses—in which annual discharge maxima from a stream gage are fit to a statistical probability distribution—often encounter large uncertainties when estimating extreme flood levels. Most gage records span relatively short periods of time (<100 years), and thus the most extreme and infrequently occurring flood events tend to be poorly represented in instrumental data sets. Here, we demonstrate how a new generation of paleoflood records derived from floodplain sediments can be used to improve the accuracy and precision of extreme flood probability estimates along alluvial rivers. We use a series of simulation experiments to show that incorporating large numbers of paleoflood events in flood frequency analyses can significantly reduce the uncertainty of extreme flood estimates when the paleoflood data are sufficiently accurate and precise. Our results illustrate that robust paleoflood records can improve the shape parameter of flood frequency distributions, which determine the thickness of distribution tails, when as many as 50 paleoflood events are incorporated. We conclude by demonstrating how an alluvial paleoflood data set reduces uncertainty in a flood frequency analysis for a gage on the lower Mississippi River. Finally, we provide recommendations for how to incorporate paleoflood information into flood frequency analysis to improve the accuracy of extreme flood probabilities.