Toward an improved estimation of flood frequency statistics from simulated flows

The estimation of extreme flood frequency for ungauged or poorly gauged catchments is a longstanding problem of great practical importance. Simulated streamflow derived from distributed hydrological models can be used to address this issue, but their representation of extreme flood peaks is often prone to large biases. This study evaluates the potential of a nonasymptotic statistical approach able to consider all the independent flood peaks instead of extremes only, the Simplified Metastatistical Extreme Value (SMEV), for the estimation of extreme flood frequency from time series of simulated streamflow. We examined 28 years of simulated daily streamflow across the contiguous United States and compared SMEV to traditional statistical models based on annual maxima. Our results suggest that when its assumptions are met SMEV can moderate the impact of hydrological model biases in the quantification of extreme flood frequency. SMEV exhibits a lower relative difference between quantiles derived from observations and simulations for all return periods and forcing dataset. Quantiles estimated from simulated streamflow time series (28-year records) using SMEV are usually in better agreement with the estimates based on 70-year-long observations. Geographical variations in the results of SMEV are noticed, with a better performance of SMEV in the east and west coasts (California, New England, and Mid-Atlantic) and in the southwestern regions (Texas-Gulf). These results indicate that the potential of SMEV for flood frequency analyses in ungauged and poorly gauged basins deserves further investigations.