Flood risk assessment and the design of protection measures often
require the estimation of high water levels of a given probability of
exceedance, i.e. design flood levels. The common approach for the
estimation of design flood levels has typically three main steps. First,
direct measurements of annual maximum water levels in a river
cross-section are converted into annual maximum flows by using a rating
curve. Second, a probability distribution function is fitted to these
annual maximum flows to derive discharges of a desired probability of
exceedance, i.e. design peak flows. Third, a hydraulic model is applied
to derive the corresponding design flood levels. Each of the three steps
is associated with significant uncertainties, affecting the accuracy of
estimated design flood levels. In this study, we compare this common
approach with an alternative one based on the statistical analysis of
time series of annual maximum water levels. The rationale behind this
study is that high water levels are directly measured and often come
along with higher precision and accuracy than peak flows. While the
direct use of high water levels is typical in coastal flood hazard and
risk assessment, the potential of this approach in the context of river
flooding has not been sufficiently explored. In this study we compare
the common approach with an alternative approach based on statistical
analysis of annual maximum water levels.