Hydrologists 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.

Flood hazard is typically evaluated by computing extreme flood probabilities from a flood frequency distribution following nationally defined procedures in which observed peak flow series are fit to a parametric probability distribution. These procedures, also known as flood frequency analysis, typically recommend only one probability distribution family for all watersheds within a country or region. However, large uncertainties associated with extreme flood probability estimates (>50-year flood or Q₅₀) can be further biased when fit to an inappropriate distribution model because of differences in the tails between distribution families. Here, we demonstrate that hydroclimatic parameters can aid in the selection of a parametric flood frequency distribution. We use L-moment diagrams to visually show the fit of gaged annual maxima series across the United States, grouped by their Köppen climate classification and the precipitation intensities of the basin, to a general extreme value (GEV), log normal 3 (LN3), and Pearson 3 (P3) distribution. Our results show that in real space basic hydroclimatic properties of a basin exert a significant influence on the statistical distribution of the annual maxima. The best-fitted family distribution shifts from a GEV towards an LN3 distribution across a gradient from colder and wetter climates (Köppen group D, continental climates) towards more arid climates (Köppen group B, dry climates). Due to the diversity of hydrologic processes and flood-generating mechanisms among watersheds within large countries like the United States, we recommend that the selection of distribution model be guided by the hydroclimatic properties of the basin rather than relying on a single national distribution model.

Flood hazard is typically evaluated by computing extreme flood probabilities from a flood frequency distribution following nationally defined procedures in which observed peak flow series are fit to a parametric probability distribution. These procedures, also known as flood frequency analysis, typically recommend only one probability distribution family for all watersheds within a country or region. However, extreme flood probability estimates (> 500-year-flood or Q₅₀₀) can be biased when fit to an inappropriate distribution model because of differences in the tails between distribution families. Here, we demonstrate that hydroclimatic parameters can aid the selection of a parametric flood frequency distribution. We use L-moment diagrams to visually show the fit of gaged annual maxima series across the United States, grouped by their Köppen climate classification and the precipitation intensities of the basin, to a General Extreme Value (GEV), Log Normal 3 (LN3) and (log-)Pearson 3 (P3) distribution. Our results show that basic hydroclimatic properties of a basin exert a significant influence on the statistical distribution of the annual maxima. The best-fitted family distribution shifts from an GEV towards an LN3 distribution across a gradient from colder and wetter climates (Köppen group D, continental climates) towards more arid climates (Köppen group B, dry climates). Due to the diversity of hydrologic processes and flood generating mechanisms among watersheds within large countries like the United States, we recommend that the selection of distribution model be guided by the hydroclimatic properties of the basin rather than relying on a single national distribution model.