  Accurate and reliable probabilistic forecasts of hydrological quantities like
runoff or water level are beneficial to various areas of society. Probabilistic
state-of-the-art hydrological ensemble prediction models are usually driven
with meteorological ensemble forecasts. Hence, biases and dispersion errors of
the meteorological forecasts cascade down to the hydrological predictions and
add to the errors of the hydrological models. The systematic parts of these
errors can be reduced by applying statistical post-processing. For a sound
estimation of predictive uncertainty and an optimal correction of systematic
errors, statistical post-processing methods should be tailored to the
particular forecast variable at hand. Former studies have shown that it can
make sense to treat hydrological quantities as bounded variables. In this
paper, a doubly truncated Bayesian model averaging (BMA) method, which allows
for flexible post-processing of (multi-model) ensemble forecasts of water
level, is introduced. A case study based on water level for a gauge of river
Rhine, reveals a good predictive skill of doubly truncated BMA compared both to
the raw ensemble and the reference ensemble model output statistics approach.
