Risk management of nonstationary floods depends on an understanding of trends over a range of flood frequencies representing small (frequent) to large (infrequent) floods. Quantile regression is applied to the annual peak streamflow distributions at 2683 sites in the contiguous United States to test for trends in the 10th quantile (floods with a 0.9 annual exceedance probability), the 50th quantile (median annual flood), and 90th quantile (floods with a 0.1 annual exceedance probability). Trends are most common (36% of sites) for the median annual flood (50th quantile) and often coherent with trends in both frequent small floods (10th quantile) and infrequent large floods (90th quantile). Changes in the at-site variance of annual peak streamflow, indicated by convergence (decreasing variance) or divergence (increasing variance) of the 10th and 90th quantiles over time, are primarily in response to reservoir operation or urban development rather than climate. An analysis of synthetic series generated from nonstationary distributions demonstrates that quantile regression and standard trend tests used in flood frequency analysis have limited power and high rates of false negatives (>70%) when a test has a significance of p = 0.05. Quantile regression and tests with lower significance complement standard trend testing to inform flood risk management.
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