Abstract. Recently, deep learning (DL) has emerged as a revolutionary and
versatile tool transforming industry applications and generating new and
improved capabilities for scientific discovery and model building. The
adoption of DL in hydrology has so far been gradual, but the field is now
ripe for breakthroughs. This paper suggests that DL-based methods can open up a
complementary avenue toward knowledge discovery in hydrologic sciences. In
the new avenue, machine-learning algorithms present competing hypotheses that
are consistent with data. Interrogative methods are then invoked to interpret
DL models for scientists to further evaluate. However, hydrology presents
many challenges for DL methods, such as data limitations, heterogeneity
and co-evolution, and the general inexperience of the hydrologic field with
DL. The roadmap toward DL-powered scientific advances will require the
coordinated effort from a large community involving scientists and citizens.
Integrating process-based models with DL models will help alleviate data
limitations. The sharing of data and baseline models will improve the
efficiency of the community as a whole. Open competitions could serve as the
organizing events to greatly propel growth and nurture data science education
in hydrology, which demands a grassroots collaboration. The area of
hydrologic DL presents numerous research opportunities that could, in turn,
stimulate advances in machine learning as well.