The primary method to model flood flows is flood frequency analysis. Typical methods model annual maximum flows as a stationary process, and the 1% chance flood is computed at the 0.99 quantile (99th percentile) of an assumed underlying distribution. Nonstationarity in a flood series may occur for a variety of reasons. External disturbances, either due to natural and non-natural changes in climate, greatly impact the assumption of stationarity of a flood series. For this analysis, a method for flood frequency analysis is proposed for nonstationary data. The first part of this analysis would be to attempt to detect a changepoint in the flood series. The flood series can then be discretized into known components, so a pooling weight can be calculated as the proportion of the series belonging to each component. Within each component, a test will be performed to assess whether a finite mixture model is needed to estimate that component's flood series' distribution. The point estimate of the 0.99 quantile and its estimated standard error will then be computed.