  In recent years, the paradigms of data-driven science have become essential
components of physical sciences, particularly in geophysical disciplines such
as climatology. The field of hydrology is one of these disciplines where
machine learning and data-driven models have attracted significant attention.
This offers significant potential for data scientists' contributions to
hydrologic research. As in every interdisciplinary research effort, an initial
mutual understanding of the domain is key to successful work later on. In this
work, we focus on the element of hydrologic rainfall--runoff models and their
application to forecast floods and predict streamflow, the volume of water
flowing in a river. This guide aims to help interested data scientists gain an
understanding of the problem, the hydrologic concepts involved, and the details
that come up along the way. We have captured lessons that we have learned while
"coming up to speed" on streamflow prediction and hope that our experiences
will be useful to the community.
