Technical Note: A Visual Diagnostic Framework for Identifying Non-Stationarity and Mixed Populations in Flood Series

Abstract. Practitioners are commonly faced with conducting flood frequency analysis (ffa) with a specific purpose in mind. They are faced with the temptation to use all the available data and assume that the conditions of ffa are met. Flood frequency analysis relies on the assumptions that the flood time series are: [1] stationary, and, [2] independent, widely known as independent and identically distributed (i.i.d.). It is commonly understood that these conditions do not always exist. In many cases, the sample is composed of mixed populations and low outliers often confuse the analyst by biasing the selection of a distribution. Magnitude outliers may come from a different generating mechanism than the main population of peaks. Timing outliers can also indicate alternative generating mechanisms. A diagnostic framework for visual screening of annual maxima and peaks-over-threshold data is described that can better inform the analyst of the nature of the flood series. This integration allows the identification of mixed populations that are often missed in standard routines.