| 22 Apr 2026
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.
While there is a useful scientific contribution here, I believe there are some substantial revisions necessary to they way this note is presented (both the text and the figures) before it is ready for publication. This is especially considering that the paper is meant to highlight a methodology for someone to use rather than presenting any new scientific knowledge contribution.
First is regarding the text and the tone of the note. I believe the manuscript is dismissive of the knowledge and experience of practitioners in performing flood frequency analysis. Phrases such as 'temptation (abstract)', 'confuse the analyst (abstract)', 'practitioner bias (par 250)', 'outlier confusion (255)') and more are unfair to the knowlede of practicing water resources professionals, and should be removed, especially if the intent is to create a tool that practitioners will use. Â
Second, regarding the overall text, there are examples where claims are provided without evidence. Example 1 (abstract) - 'in many cases the sample is composed of mixed populations'. No evidence is provided to support this claim, and in my experience in Canada this isn't true. Example 2 (par 250) refers to practitioner bias. It is not clear where the authors have observed this practitioner bias or how they know this is the case. Â
Along with overall issues with the tone and assumptions of the practitioners who perform ffa as part of their professional practice, the figures require substantial editing for clarity. In particular, on Figure 1 - 1) there are items in the legend which do not appear on the actual figure. 2) is is not clear what the light dashed vertical lines represent. 3) Text on the figures is places throughout and would be better placed in one consolidated note below eacher figure. 4) considering the long timeseries figures a and b would be better placed as wider panels stacked on top of eachother rather than side-by-side.Â
For figure 2 - 1) it is unclear what the colors of the bars in the proportion plot represent. 2) there appears to be colors on panel a points that arent shown in the legend. 3) all points in the legend are hollow whereas on the plot they are filled colors. 4) "proportion" is not a descriptive axis title for the sub-plot.Â
For figure 3 - The polor plots are quite novel but still have a number of issues. 1) there are two legends, and some of the colors are reused on both legends, making it difficult to tell what the color or symbols represent 2) there is seeming haphazard coloring and bolding of the note shown in the bottom left corner or fhte plot. 3) the clustering would likely be most useful in a table by year, is this provided with the analysis package?
Overall the figures could be simplified for better clarity, and many of the notes within the figures would be better provided as accompanying text rather than right on the figures.