  Probabilistic forecasting is receiving growing attention nowadays in a
variety of applied fields, including hydrology. Several machine learning
concepts and methods are notably relevant towards addressing the major
challenges of formalizing and optimizing probabilistic forecasting
implementations, as well as the equally important challenge of identifying the
most useful ones among these implementations. Nonetheless, practically-oriented
reviews focusing on such concepts and methods, and on how these can be
effectively exploited in the above-outlined essential endeavour, are currently
missing from the probabilistic hydrological forecasting literature. This
absence holds despite the pronounced intensification in the research efforts
for benefitting from machine learning in this same literature. It also holds
despite the substantial relevant progress that has recently emerged, especially
in the field of probabilistic hydrological post-processing, which traditionally
provides the hydrologists with probabilistic hydrological forecasting
implementations. Herein, we aim to fill this specific gap. In our review, we
emphasize key ideas and information that can lead to effective popularizations,
as such an emphasis can support successful future implementations and further
scientific developments. In the same forward-looking direction, we identify
open research questions and propose ideas to be explored in the future.
