Discontinuities in flood frequency curves, here referred to as flood divides, hinder the estimation of rare floods. In this paper we develop an automated methodology for the detection of flood divides from observations and models, and apply it to a large set of case studies in the USA and Germany. We then assess the reliability of the PHysically-based Extreme Value (PHEV) distribution of river flows to identify catchments that might experience a flood divide, validating its results against observations. This tool is suitable for the identification of flood divides, with a high correct detection rate especially in the autumn and summer seasons. It instead tends to indicate the emergence of flood divides not visible in the observations in spring and winter. We examine possible reasons of this behavior, finding them in the typical streamflow dynamics of the concerned case studies. By means of a controlled experiment we also re-evaluate detection capabilities of observations and PHEV after discarding the highest maxima for all cases where both empirical and theoretical estimates display flood divides. PHEV mostly confirms its capability to detect a flood divide as observed in the original flood frequency curve, even if the shortened one does not show it. These findings prove its reliability for the identification of flood divides and set the premises for a deeper investigation of physiographic and hydroclimatic attributes controlling the emergence of discontinuities in flood frequency curves.