Improvements to Flood Frequency Analysis Using Climate and Paleoflood Data

Abstract:

Riverine floods affect communities across the United States and result in large financial losses to society annually. Hydrologists use flood frequency analyses to compute flood probabilities and appropriately plan mitigation responses. Generally, flood frequency analyses – in which annual discharge maxima from a stream gage are fit to a statistical probability distribution – produce large uncertainties when estimating the most extreme flood levels. This uncertainty reflects sample uncertainty, as the majority of stream gage records are relatively short (< 80 years), and the most extreme and infrequently occurring flood events tend to be poorly reflected by the instrumental gage records. An additional source of uncertainty is due to model uncertainty, as the statistical distribution that is fit to annual maxima is based on geopolitical borders and national procedures rather than environmental factors. Here we use a series of simulation experiments to show that additional hydroclimatic information based on hydrological reasoning can improve the accuracy and precision of extreme flood probability estimates. Our results demonstrate via L-moment diagrams that using simple hydroclimatic properties of the basin, including Köppen climate classification and precipitation intensity, can significantly improve the model fit to the distribution of annual maxima. We also demonstrate that incorporating large numbers of paleoflood events in flood frequency analyses can significantly reduce the uncertainty of extreme flood estimates when the flood magnitude inferences of the paleoflood data are sufficiently accurate and precise. We conclude with a set of best practices for how to incorporate hydroclimatic and paleoflood information into flood frequency analyses and reduce the uncertainty of extreme flood probability estimates.