  Deep learning (DL), a new-generation of artificial neural network research,
has transformed industries, daily lives and various scientific disciplines in
recent years. DL represents significant progress in the ability of neural
networks to automatically engineer problem-relevant features and capture highly
complex data distributions. I argue that DL can help address several major new
and old challenges facing research in water sciences such as
inter-disciplinarity, data discoverability, hydrologic scaling, equifinality,
and needs for parameter regionalization. This review paper is intended to
provide water resources scientists and hydrologists in particular with a simple
technical overview, trans-disciplinary progress update, and a source of
inspiration about the relevance of DL to water. The review reveals that various
physical and geoscientific disciplines have utilized DL to address data
challenges, improve efficiency, and gain scientific insights. DL is especially
suited for information extraction from image-like data and sequential data.
Techniques and experiences presented in other disciplines are of high relevance
to water research. Meanwhile, less noticed is that DL may also serve as a
scientific exploratory tool. A new area termed 'AI neuroscience,' where
scientists interpret the decision process of deep networks and derive insights,
has been born. This budding sub-discipline has demonstrated methods including
correlation-based analysis, inversion of network-extracted features,
reduced-order approximations by interpretable models, and attribution of
network decisions to inputs. Moreover, DL can also use data to condition
neurons that mimic problem-specific fundamental organizing units, thus
revealing emergent behaviors of these units. Vast opportunities exist for DL to
propel advances in water sciences.
