Abstract
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.