  Predictive hydrological uncertainty can be quantified by using ensemble
methods. If properly formulated, these methods can offer improved predictive
performance by combining multiple predictions. In this work, we use
50-year-long monthly time series observed in 270 catchments in the United
States to explore the performances provided by an ensemble learning
post-processing methodology for issuing probabilistic hydrological predictions.
This methodology allows the utilization of flexible quantile regression models
for exploiting information about the hydrological model's error. Its key
differences with respect to basic two-stage hydrological post-processing
methodologies using the same type of regression models are that (a) instead of
a single point hydrological prediction it generates a large number of "sister
predictions" (yet using a single hydrological model), and that (b) it relies on
the concept of combining probabilistic predictions via simple quantile
averaging. A major hydrological modelling challenge is obtaining probabilistic
predictions that are simultaneously reliable and associated to prediction bands
that are as narrow as possible; therefore, we assess both these desired
properties of the predictions by computing their coverage probabilities,
average widths and average interval scores. The results confirm the usefulness
of the proposed methodology and its larger robustness with respect to basic
two-stage post-processing methodologies. Finally, this methodology is
empirically proven to harness the "wisdom of the crowd" in terms of average
interval score, i.e., the average of the individual predictions combined by
this methodology scores no worse -- usually better -- than the average of the
scores of the individual predictions.
